WILBERT

Description: In order to obtain high peak power RF intensity-modulated pulses for long-distance applications, a Q-switched Nd:YAG laser is injection seeded by a dual-frequency laser signal. The beat note frequency of the dual-frequency laser signal is adjusted to the frequency spacing of two adjacent longitudinal modes of the Q-switched laser. When the dual-frequency signal is injected into the Q-switched slave resonator, simultaneously two cavity axial modes are tuned to allow the injected wave frequencies within the axial modes selection range, the two selected slave cavity modes will oscillate due to phase-pulling effect. The Interference of the two modes results in intensity modulated pulses. The modulation frequency equals to a free spectral range of the Q-switched cavity, which is 225 MHz in our experiment. Maximum intensity-modulated pulse energy of 7 mJ and pulsewidth of 16 ns are measured. The dependence of locking bandwidth to the injected power is also studied experimentally, it is confirmed that the locking bandwidth increases with respect to the injected power of the seed signal. The high peak power RF intensity-modulated pulsed light source is a promising candidate for long-distance lidar-radar applications.

Description: A microwave photonic system that can realize frequency up- and down-conversion, multichannel phase shift, high conversion gain, and elimination of dispersion-induced power fading is proposed and experimentally demonstrated. The scheme is based on an integrated dual-polarization quadrature phase shift keying modulator that contains two dual parallel Mach–Zehnder modulators (X-DPMZM and Y-DPMZM). The X-DPMZM implements dual side band carrier suppression (DSB-CS) modulation of radio frequency signal, and the Y-DPMZM implements frequency shift of an optical carrier. They are combined in orthogonal polarizations to implement frequency up- and down-conversion. The polarization multiplexed signal will go through polarization controllers and polarizers to implement multichannel phase shift. In the experiment, the phase shift can be tuned independently over 360° in each channel. By suppressing the optical carrier, the conversion gain and LO isolation are improved by 20.5 dB and 51.26 dB, respectively, compared with conventional dual side band modulation scheme. In addition, the proposed scheme can achieve a spurious-free dynamic range (SFDR) of 103.6 dB·Hz 2/3 .

Description: An ultrafast high-sensitivity refractive index (RI) and temperature-sensing system based on an optoelectronic oscillator (OEO) is proposed and demonstrated in this paper. A Fabry–Perot fiber Bragg grating (FP-FBG), which combines a gap with two FBGs in a silica V-shaped slot and characterizes a narrow notch in the reflection spectrum, is incorporated in the OEO to implement a microwave photonic filter and perform oscillating frequency selection. A microwave signal is generated by the OEO, whose oscillating frequency is determined by the center frequency of the FP-FBG notch, which varies with the surrounding environments. The RI or the temperature change can be accordingly measured by monitoring the frequency shift of the microwave signal using an electrical spectrum analyzer or a digital signal processor. An experiment is performed to verify the proposal. An RI sensitivity of 413.8 MHz/0.001RIU and a temperature sensitivity of 2516 MHz/°C are successively demonstrated.

Description: Phase retrieval is an important tool for image recovery techniques based on Fourier spectrum. Different iterative algorithms have been developed to retrieve phase information. However, due to the nonconvex feature of the phase optimization problem, it remains a challenge to globally obtain the optimal phase information. In this work, we proposed an iterative algorithm to retrieve the global optimal phase information by adopting particle swarm optimization technique to the hybrid input–output scheme. By escaping the local minima using stochastic perturbations and information exchange among particles’ local solutions, the proposed scheme increases the possibility of reaching the global minimum in phase retrieval optimization. In the numerical simulations, the images reconstructed by the proposed scheme have an averaged mean-square error of 0.0055, which is, respectively, 43.88% and 36.78% smaller than those of the images reconstructed by hybrid input–output and guided hybrid input–output schemes. The feasibility of the proposed scheme was demonstrated by the results from actual experiments, which showed an agreement with the simulation. The proposed scheme is statistically capable of obtaining accurate phase information, and, therefore, can be applied to Fourier spectrum based image recovery techniques.

Description: A new tensor-based approach, which is capable of significantly increasing the degrees of freedom (DOF) of an L-shaped array consisting of two orthogonal uniform linear arrays (ULAs), is proposed. By dividing each ULA into some overlapping subarrays and then combining their received signals into a data tensor, a new cross-correlation tensor between the two data tensors from the two ULAs is built. Analyses show that such a cross-correlation tensor can be transformed into an equivalent received data matrix of a virtual uniform rectangular array (URA). Under the constraint of a fixed actual number of physical sensors, the optimal number of the subarrays is found by maximizing the DOF of the URA. It is shown that a virtual URA with approximately $0.34{{(M+1)}^{2}}$ DOF can be obtained from an L-shaped array with $2M$ physical sensors. To exploit the increased DOF for the two dimensional (2-D) DOA estimation without the multidimensional search, a parallel factor (PARAFAC) model of the URA is provided so that the PARAFAC decomposition can be utilized to do the 2-D DOA estimation effectively. Simulation results demonstrate that the proposed method can yield a better estimation performance and resolve more sources than some computationally efficient methods reported in literature.

Description: We study the degrees of freedom (DoFs) of a multiple-input multiple-output $K$ -pair two-way relay channel with delayed channel state information (CSI) with $J$ distributed relays. In the considered model, we assume that the users are equipped with $M$ antennas and the relay with $N$ antennas. We propose two schemes, where the signaling design can be carried out either in each individual time slot or across multiple time slots with and without knowledge of CSI. The scheme involves a joint design of user beamforming matrices, relay beamforming matrices, and user postprocessing matrices, so as to meet interference neutralization and rank conditions. We show that the optimal DoF of $frac{N}{2K}$ per user can be reached for $frac{M}{N}geq 1$ for an arbitrary number of user pairs when $J=1$ . This implies that delayed CSI does not compromise the DoF performance of the considered model when $frac{M}{N}geq 1$ .

Description: This letter introduces a method based on the Hilbert–Huang transform (HHT) to estimate the fundamental frequency of nonstationary noisy speech signals. For this purpose, the target signals are analyzed by means of the ensemble empirical mode decomposition and the Hilbert transform. The main contribution of the proposed solution, namely HHT-Amp, relies on the extraction of pitch information from the instantaneous amplitude of the first decomposition modes. The HHT-Amp and four competitive algorithms are evaluated considering speech signals corrupted by five acoustic noises with different nonstationarity degrees. The HHT-Amp achieves the lowest gross error rate and mean absolute error for the most severe noisy conditions. This demonstrates that the proposed approach outperforms the baseline methods in estimating the fundamental frequency of noisy speech.

Description: As soon as the new Dutch government took office in October, it announced an aggressive target—to reduce carbon emissions by 49 percent by 2030. This will ultimately require the Netherlands to sequester 20 million metric tons of carbon dioxide per year-equivalent to the annual emissions produced by 4.5 coal-fired power plants. Sequestering that much CO 2 underground will be difficult, whether it's captured directly from the flues of power stations and steel mills or extracted from the air. Currently, the Netherlands sequesters less than 10,000 metric tons of CO 2 annually.

Description: Telecom Italia, Italy's largest telecommunications provider, is putting the finishing touches on a new wireless network for the Internet of Things that should be available nationwide by the end of January. The Internet of Things (IoT) is a catchall term for many kinds of connected devices-such as sensors, speakers, and cameras-found in cities, factories, and homes. These devices often don't need as much bandwidth as smartphones, but connecting them through existing LTE networks is expensive.

Description: Sixty years ago, on 1 January 1958, Belgium, France, Italy, Luxembourg, the Netherlands, and the Federal Republic of Germany jointly formed the European Economic Community (EEC) with the goal of economic integration and free trade within a customs union. Although the immediate goals were explicitly economic, the aspirations were always far higher: In the first two sentences of the founding document, the Treaty of Rome, the member states declared their determination "to lay the foundations of an ever-closer union among the peoples of Europe" and "to ensure the economic and social progress of their countries by common action to eliminate the barriers which divide Europe." Sixty years ago, these goals seemed to be quite unrealistic: Europe was divided not only by national prejudices and economic inequalities but, most fundamentally, by the Iron Curtain, which ran from the Baltic to the Black Sea, with Moscow controlling the nations to the east of it.

Description: This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT.

Description: Features of high-risk coronary artery plaques prone to major adverse cardiac events (MACE) were identified by intravascular ultrasound (IVUS) virtual histology (VH). These plaque features are: thin-cap fibroatheroma (TCFA), plaque burden PB ≥ 70%, or minimal luminal area MLA ≤ 4 mm 2 . Identification of arterial locations likely to later develop such high-risk plaques may help prevent MACE. We report a machine learning method for prediction of future high-risk coronary plaque locations and types in patients under statin therapy. Sixty-one patients with stable angina on statin therapy underwent baseline and one-year follow-up VH-IVUS non-culprit vessel examinations followed by quantitative image analysis. For each segmented and registered VH-IVUS frame pair ( ${n} =6341$ ), location-specific ( $approx 0.5$ mm) vascular features and demographic information at baseline were identified. Seven independent support vector machine classifiers with seven different feature subsets were trained to predict high-risk plaque types one year later. A leave-one-patient-out cross-validation was used to evaluate the prediction power of different feature subsets. The experimental results showed that our machine learning method predicted future TCFA with correctness of 85.9%, 81.7%, and 77.0% (G-mean) for baseline plaque phenotypes of TCFA, thick-cap fibroatheroma, and non-fibroatheroma, respectively. For predicting PB ≥ 70%, correctness was 80.8% for baseline PB ≥ 70% and 85.6% for 50% ≤ PB < 70%. Accuracy of predicted MLA ≤- 4 mm 2 was 81.6% for baseline MLA ≤ 4 mm 2 and 80.2% for 4 mm 2 < MLA ≤ 6 mm 2 . Location-specific prediction of future high-risk coronary artery plaques is feasible through machine learning using focal vascular features and demographic variables. Our approach outperforms previously reported results and shows the importance of local factors on high-risk coronary artery plaque development.

Description: The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach, which uses a static elasticity model to register cell images. Compared with that scheme, the new approach employs a dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with the existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters.

Description: An on-demand long-lived ultrasound contrast agent that can be activated with single pulse stimulated imaging (SPSI) has been developed using hard shell liquid perfluoropentane filled silica 500-nm nanoparticles for tumor ultrasound imaging. SPSI was tested on LnCAP prostate tumor models in mice; tumor localization was observed after intravenous (IV) injection of the contrast agent. Consistent with enhanced permeability and retention, the silica nanoparticles displayed an extended imaging lifetime of 3.3±1 days (mean±standard deviation). With added tumor specific folate functionalization, the useful lifetime was extended to 12 ± 2 days; in contrast to ligand-based tumor targeting, the effect of the ligands in this application is enhanced nanoparticle retention by the tumor. This paper demonstrates for the first time that IV injected functionalized silica contrast agents can be imaged with an in vivo lifetime ~500 times longer than current microbubble-based contrast agents. Such functionalized long-lived contrast agents may lead to new applications in tumor monitoring and therapy.

Description: Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.

Description: This paper presents a method for automatically calibrating and assessing the calibration quality of an externally tracked 2-D ultrasound (US) probe by scanning arbitrary, natural tissues, as opposed a specialized calibration phantom as is the typical practice. A generative topic model quantifies the posterior probability of calibration parameters conditioned on local 2-D image features arising from a generic underlying substrate. Auto-calibration is achieved by identifying the maximum a-posteriori image-to-probe transform, and calibration quality is assessed online in terms of the posterior probability of the current image-to-probe transform. Both are closely linked to the 3-D point reconstruction error (PRE) in aligning feature observations arising from the same underlying physical structure in different US images. The method is of practical importance in that it operates simply by scanning arbitrary textured echogenic structures, e.g., in-vivo tissues in the context of the US-guided procedures, without requiring specialized calibration procedures or equipment. Observed data take the form of local scale-invariant features that can be extracted and fit to the model in near real-time. Experiments demonstrate the method on a public data set of in vivo human brain scans of 14 unique subjects acquired in the context of neurosurgery. Online calibration assessment can be performed at approximately 3 Hz for the US images of $640times 480$ pixels. Auto-calibration achieves an internal mean PRE of 1.2 mm and a discrepancy of [2 mm, 6 mm] in comparison to the calibration via a standard phantom-based method.

Description: The motion of the common carotid artery (CCA) wall has been established to be useful in early diagnosis of atherosclerotic disease. However, tracking the CCA wall motion from ultrasound images remains a challenging task. In this paper, a nonlinear state-space approach has been developed to track CCA wall motion from ultrasound sequences. In this approach, a nonlinear state-space equation with a time-variant control signal was constructed from a mathematical model of the dynamics of the CCA wall. Then, the unscented Kalman filter (UKF) was adopted to solve the nonlinear state transfer function in order to evolve the state of the target tissue, which involves estimation of the motion trajectory of the CCA wall from noisy ultrasound images. The performance of this approach has been validated on 30 simulated ultrasound sequences and a real ultrasound dataset of 103 subjects by comparing the motion tracking results obtained in this study to those of three state-of-the-art methods and of the manual tracing method performed by two experienced ultrasound physicians. The experimental results demonstrated that the proposed approach is highly correlated with (intra-class correlation coefficient ≥ 0.9948 for the longitudinal motion and ≥ 0.9966 for the radial motion) and well agrees (the 95% confidence interval width is 0.8871 mm for the longitudinal motion and 0.4159 mm for the radial motion) with the manual tracing method on real data and also exhibits high accuracy on simulated data (0.1161 ~ 0.1260 mm). These results appear to demonstrate the effectiveness of the proposed approach for motion tracking of the CCA wall.

Description: I was telling someone how intelligent my dog was. He shrugged dismissively and said, "Dogs are just really good pattern detectors." Afterward, I looked at my dog a little differently. "Are you intelligent, or just a pattern detector?" I asked her. She just wagged her tail and said nothing, and I suppose that's open to interpretation. She swims in a sea of data from vision, sounds, and smells. From this data, she forms a model of the world- a dog's world, and one that is unknowable to us, and yet seems to have commonalities with our own. She knows the objects and inhabitants of her world and the patterns of everyday experience and she is keenly aware of any anomalies. I once heard a speaker on intellectual property say that "your dog knows where your property ends." I’m not sure that my dog does, but if so, it would be an example of deriving an abstract rule from patterns of behavioral data.

Description: We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object’s interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes’ geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al. , entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes’ geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.

Description: In this paper, we propose a generalized joint sparsity regularization prior and reconstruction framework for the synergistic reconstruction of positron emission tomography (PET) and under sampled sensitivity encoded magnetic resonance imaging data with the aim of improving image quality beyond that obtained through conventional independent reconstructions. The proposed prior improves upon the joint total variation (TV) using a non-convex potential function that assigns a relatively lower penalty for the PET and MR gradients, whose magnitudes are jointly large, thus permitting the preservation and formation of common boundaries irrespective of their relative orientation. The alternating direction method of multipliers (ADMM) optimization framework was exploited for the joint PET-MR image reconstruction. In this framework, the joint maximum a posteriori objective function was effectively optimized by alternating between well-established regularized PET and MR image reconstructions. Moreover, the dependency of the joint prior on the PET and MR signal intensities was addressed by a novel alternating scaling of the distribution of the gradient vectors. The proposed prior was compared with the separate TV and joint TV regularization methods using extensive simulation and real clinical data. In addition, the proposed joint prior was compared with the recently proposed linear parallel level sets (PLSs) method using a benchmark simulation data set. Our simulation and clinical data results demonstrated the improved quality of the synergistically reconstructed PET-MR images compared with the unregularized and conventional separately regularized methods. It was also found that the proposed prior can outperform both the joint TV and linear PLS regularization methods in assisting edge preservation and recovery of details, which are otherwise impaired by noise and aliasing artifacts. In conclusion, the proposed joint sparsity regularization within the presented a- ADMM reconstruction framework is a promising technique, nonetheless our clinical results showed that the clinical applicability of joint reconstruction might be limited in current PET-MR scanners, mainly due to the lower resolution of PET images.

Description: New York City has long offered varied and colorful nightlife. But on a recent Saturday evening, Senior Associate Editor Eliza Strickland got to see something truly new in Gotham. At a virtual reality arcade called VR World, Strickland strolled through a neon-lit wonderland and gawked at screens displaying what looked like thriller-movie footage. Players battled zombies, flew through space, and tested their nerves by climbing sheer cliffs. While VR arcades are already popular in East Asia, only a few have opened in the United States. "It's a new type of entertainment venue," Strickland says. "But backers are hoping these arcades will catch on and become as common as movie theaters."

Description: Two years after World War II, billionaire Howard Hughes personally piloted his "Spruce Goose" troop transport aircraft on the first and only flight of the largest seaplane ever built. It lasted barely a minute. Now, more than 70 years later, a U.S. startup is testing a new seaplane concept—one that could evolve into huge cargo drones that fly 109 metric tons of freight across the Pacific, touch down autonomously over water, and unload at ports around the world.

Description: Electronics enthusiasts like being able to make things themselves. In IEEE Spectrum's Hands On column, we've detailed how readers can make their own solder reflow ovens, conductive ink, and synthetic aperture radars. But making DIY integrated circuits seemed impossibly out of reach. After all, building a modern fab is astronomically expensive: For example, in 2017 Intel announced it was investing US $7 billion to complete a facility for making chips with 7-nanometer- scale features. But Sam Zeloof was not deterred. This 17-year-old high school student has started making chips in his garage,albeit with technology that's a few steps back along the curve of Moore's Law. Zeloof says he has been working on his garage fab, located in his home near Flemington, N.J., for about a year. He began thinking about how to make chips as his "way of trying to learn what's going on inside semiconductors and transistors. I started reading old books and old patents because the newer books explain processes that require very expensive equipment."

Description: The word robotic usually brings to mind movements that are halting and inelegant. But during a charity concert for the gala of the first International Robotics Festival in September, a humanoid robot named YuMi demonstrated astounding dexterity, subtlety, and nuance. The automaton directed the Lucca Philharmonic Orchestra, impressing musicians and singers- including Italian tenor Andrea Bocelli, who sang a program of compositions by Verdi. The robot maestro got its conducting chops in two stages: During rehearsals, cameras captured the movements of Maestro Andrea Colombini, the orchestra's human director; as YuMi imitated Colombini's performance, its motions were recorded. The interaction between the robot's elbows, forearms, and wrists were then fine-tuned using specialized software.

Description: Over the years, programmers have developed all sorts of conventions to wrangle the infinitely malleable nature of software. There are rules for how new versions should be numbered; how variables and functions should be named; the use of README files; and so on. Such measures provide consistency, especially for people grappling with software written by others, which can otherwise be a maddeningly obfuscated black box.

Description: Neurotechnology is one of the hottest areas of engineering, and the technological achievements sound miraculous: Paralyzed people have controlled robotic limbs with their brains, while blind people are receiving implants that send signals to their visual centers. Researchers are figuring out how to make better devices to record neural signals or change the way the brain functions. While much of this work is intended to help people with disabilities or illnesses, there's growing interest in augmenting the abilities of all. Senior Associate Editor Eliza Strickland talked to Anders Sandberg, a researcher at the University of Oxford’s Future of Humanity Institute about the ethical implications, and how neurotech could reshape society.

Description: None of the people walking around San Diego's East Village neighborhood one recent afternoon were looking up at the streetlights (except me). And if they had, they likely wouldn't have noticed that some of these lights were a little thicker around the middle than others, or that some lanterns topping old-style lampposts had a clear glass panel here and there. But unbeknownst to the people below, those streetlights were looking- and listening-all around them, while also monitoring temperature, humidity, and other characteristics of the air.

Description: Welcome to the 5G Olympics, where Nathan Chen, the 18-year-old figure-skating phenom, has just landed another quadruple jump. Can't see him well from your seat in the nosebleed section? No problem. Just slip on your 5G virtual reality headset for a 360-degree rink-side view! Now watch your step-we're boarding the 5G bus to the next attraction. Check out the windows: They're in fact transparent display screens providing ultrahigh-definition video—streamed live—from a hockey player's headcam, from drones flying above the ski slopes, and from the cockpit of a bobsled barreling down an icy track at 100...0...0 kilometers per hour!

Description: "A fab is like an iceberg," someone tells me. I can't tell who because we're all covered head to toe in clean-room garb. A tour of GlobalFoundries' Fab 8 in Malta, N.Y., certainly reinforces that analogy: We've just come up from the "sub-fab," the 10 meters of vertical space under the floor, where pipes and wires snake down from each semiconductor-manufacturing tool above to a set of automated chemical handlers, water analyzers, power conditioners, and-in the case of the unit I've come to see-kilowatt-class lasers.

Description: Geometric distortion induced by the main B0 field disrupts the consistency of fetal echo planar imaging (EPI) data, on which diffusion and functional magnetic resonance imaging is based. In this paper, we present a novel data-driven method for simultaneous motion and distortion correction of fetal EPI. A motion-corrected and reconstructed T2 weighted single shot fast spin echo (ssFSE) volume is used as a model of undistorted fetal brain anatomy. Our algorithm interleaves two registration steps: estimation of fetal motion parameters by aligning EPI slices to the model; and deformable registration of EPI slices to slices simulated from the undistorted model to estimate the distortion field. The deformable registration is regularized by a physically inspired Laplacian constraint, to model distortion induced by a source-free background B0 field. Our experiments show that distortion correction significantly improves consistency of reconstructed EPI volumes with ssFSE volumes. In addition, the estimated distortion fields are consistent with fields calculated from acquired field maps, and the Laplacian constraint is essential for estimation of plausible distortion fields. The EPI volumes reconstructed from different scans of the same subject were more consistent when the proposed method was used in comparison with EPI volumes reconstructed from data distortion corrected using a separately acquired B0 field map.

Description: Recently, matrix gradient coils (also termed multi-coils or multi-coil arrays) were introduced for imaging and B 0 shimming with 24, 48, and even 84 coil elements. However, in imaging applications, providing one amplifier per coil element is not always feasible due to high cost and technical complexity. In this simulation study, we show that an 84-channel matrix gradient coil (head insert for brain imaging) is able to create a wide variety of field shapes even if the number of amplifiers is reduced. An optimization algorithm was implemented that obtains groups of coil elements, such that a desired target field can be created by driving each group with an amplifier. This limits the number of amplifiers to the number of coil element groups. Simulated annealing is used due to the NP-hard combinatorial nature of the given problem. A spherical harmonic basis set up to the full third order within a sphere of 20-cm diameter in the center of the coil was investigated as target fields. We show that the median normalized least squares error for all target fields is below approximately 5% for 12 or more amplifiers. At the same time, the dissipated power stays within reasonable limits. With a relatively small set of amplifiers, switches can be used to sequentially generate spherical harmonics up to third order. The costs associated with a matrix gradient coil can be lowered, which increases the practical utility of matrix gradient coils.

Description: In dynamic planar imaging, extraction of signals specific to structures is complicated by structures superposition. Due to overlapping, signals extraction with classic regions of interest (ROIs) methods suffers from inaccuracy, as extracted signals are a mixture of targeted signals. Partial volume effect raises the same issue in dynamic tomography. Source separation methods, such as factor analysis of dynamic sequences, have been developed to unmix such data. However, the underlying problem is underdetermined and the model used is not relevant in the whole image. This non-uniqueness issue was overcome by introducing prior knowledge, such as sparsity or smoothness, in the separation model. In practice, these methods are barely used because of the lack of reliability of their results. Previously developed methods aimed to be fully automatic, but efficiency can be improved with additional prior knowledge. Some methods using ROIs knowledge in a straightforward way have been proposed. In this paper, we propose an unmixing method, based on an objective function minimization and integrating these ROIs in a different and robust manner. The objective function promotes consistent solutions regarding ROIs while relaxing the model outside ROIs. In order to reduce user-dependent effects, ROIs are used as soft constraints in a robust way through the use of a distance matrix. Consistency, effectiveness, and robustness to the ROIs selection are demonstrated on a toy example, a highly realistic simulated renography data set and a clinical data set. Performance is compared with the competitive methods.

Description: HyperLabelMe is a web platform that allows the automatic benchmarking of remote-sensing image classifiers. To demonstrate this platform's attributes, we collected and harmonized a large data set of labeled multispectral and hyperspectral images with different numbers of classes, dimensionality, noise sources, and levels. The registered user can download training data pairs (spectra and land cover/use labels) and submit the predictions for unseen testing spectra. The system then evaluates the accuracy and robustness of the classifier, and it reports different scores as well as a ranked list of the best methods and users. The system is modular, scalable, and ever-growing in data sets and classifier results.

Description: Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.

Description: An emerging way to deal with high-dimensional noneuclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This paper aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as time-vertex signal processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple partial differential equations on graphs; b) we improve the accuracy of joint filtering operators by up-to two orders of magnitude; c) using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.

Description: Bilinear and Volterra models are important when dealing with nonlinear systems which arise in several signal processing applications. The former can approximate a large class of systems affine in the input with relatively low parametric complexity. Such an approximate bilinear model can be derived by means of Carleman bilinearization (CB). Then, a Volterra model can be computed from it, having the advantage of being linear in the parameters, but often involving a large number of them. In this paper, we develop efficient routines for CB and for computing the Volterra kernels of a bilinear system. We argue that they are useful for studying a class of systems for which a reference physical model is known. In particular, the so-derived kernels allow assessing the suitability of a Volterra filter and of other alternatives for modeling the system of interest. Techniques exploiting sparsity and low rank of involved matrices are proposed for alleviating computing cost. Several examples are given along the paper to illustrate their use, based on existing physical models of loudspeakers.

Description: Presents the front cover for this issue of the publication.

Description: We demonstrate a whispering-gallery-mode (WGM) microsphere resonator on a no-core fiber (NCF) tip. The device is fabricated by fusion splicing of a single-mode fiber (SMF) with a short section of NCF, and followed by gluing a microsphere on the arc end of NCF. The incident light propagates from the SMF core to the microsphere via the NCF, and when the angle between the SMF-NCF axis and the center of microsphere is appropriate, WGM is excited. The device is simple in structure, easy in fabrication, and of low cost, and can be used for temperature sensing with a good sensitivity of 21.34 pm/°C.

Description: Ferrofluid is a new sort of functional nanomaterial with interesting optical characteristics under external magnetic field which receives widespread attention in photonics. Here, we demonstrate a microfiber based interferometric photonic sensor for real-time detection of magnetic field by immersing one beam of microfiber based interferometer into ferrofluid. Since the applied magnetic field can change the refractive index of ferrofluid and the interferometer sensitive to ambient refractive index variation, the interferometric fringe would be shifted under external field. Thus, the magnetic field can be demodulated by monitoring the fringe shift. The proposed sensor was successfully used to detect both static magnetic field range from 0 to 330 Oe and alternating magnetic field with frequency of 50 Hz.

Description: Variable gas diffusion cell geometries were tested for the extraction of dissolved inorganic carbon (DIC) from a microfluidic (tens to hundreds of microliters) seawater sample. With a focus on optimization of diffusion cell geometry, we compare five unique diffusion cell designs. Using 3-D printing technology to streamline the prototyping and testing process, we were able to conceive, design, fabricate, and thoroughly evaluate each design over the course of about one month. In total, 1043 DIC measurements were carried out in 109 experiments for the five working manifolds. We find that a small diameter, cylindrical diffusion cell design offers several advantages over its planar counterparts and a larger diameter cylindrical cell, most notably the ability to increase the ratio of the exchange membrane’s contact surface area to solution volume (the “aspect ratio”) without sacrificing channel integrity. Multiple designs approached short-term repeatability of <1%, but only the cylindrical diffusion cell design allowed for <0.2% repeatability using less than 200 $mu text{L}$ of sample.

Description: Numerous industrial processes are based on the application of phenolic compounds such as Bisphenol-A (BPA), which is used as a monomer and additive in plastics, resins, and coatings manufacturing. Many phenolic compounds are highly toxic and responsible for high pollution in food, water, and soil. The endocrine disrupting activities of BPA makes it an emerging environmental pollutant. The most common source of BPA release into the environment is from plastic packaging. This leakage from plastic packaging is facilitated by heat and pH conditions which speeds up the hydrolysis of the ester bonds. In this paper, we report the estimation of BPA concentration in ethanol, using nanostructured manganese dioxide as a receptor using a non-faradaic electrochemical impedance spectroscopy. An equivalent electrical circuit model has been proposed for electrochemical impedance data of BPA solutions in pure ethanol. A linear relationship of impedance response with the concentration over a range from 1 nM– $62.5~mu text{M}$ is observed. The detection limit is estimated around to be $0.66~mu text{M}$ . During the measurement, it has been taken care that the electrodes must give excellent reproducibility.

Description: In this paper, we have investigated a resistive ammonia (NH 3 ) gas sensor fabricated with a combination of polyaniline (PANI) as a conductive polymer and different weight percentages of nitrogen-doped graphene quantum dots (N-GQDs). We used two different metals of silver (Ag) and aluminium (Al) as electrodes to the sensing films. It has been found that Ag contact showed an ohmic behaviour and Al electrode exhibited a Schottky junction. This paper presented here shows that the sensor with Ag electrode has much higher response to NH 3 than that of the sensor with Al electrode. The sensor of (50wt%)N-GQDs/PANI with Ag contact exhibited the best response of 110.92 towards 1500 ppm NH 3 at room temperature. However, a much response of 86.91 was measured for the sensor with Al electrode. Fourier transform infrared spectroscopy of the synthesized structure was investigated to analyze the differences between pure PANI and N-GQDs/PANI. Further, field emissions scanning electron microscopy was used to characterize sensing samples.

Description: This paper presents fixed point operation-based Field Programmable Gate Arrays (FPGA) implementation of Steinhart–Hart Equation (SHHE) for thermistor linearization. FPGA implementation issues of SHHE are presented and their solutions are proposed and experimentally validated in a LabVIEW TM environment. Experimental temperature calibration, performed using a M/S Fluke drywell calibrator, revealed a lowest nonlinearity of 0.11% for an industrial grade thermistor in the input temperature range from −20 °C to 120 °C. Therefore, the main contribution of this work is to demonstrate the lowest nonlinearity for a wider temperature range. This work is expected to be very useful to instrumentation engineers as it employs a time-tested technique, for thermistor linearization in FPGA, leading to the lowest nonlinearity for a larger input temperature range.

Description: This paper concerns the nature of the peculiar second-order buoyancy experienced by a magnet in magnetic fluid acceleration sensor. The equilibrium differential equation of magnetic fluid under the action of magnetic field and gravity field is established and the expression for calculating the second-order buoyancy is derived. Three practical and effective methods to calculate the second-order buoyancy called surface integral method, magnetic force method, and equivalent magnetic force method are proposed. Besides, the second-order buoyancy is calculated by the three methods mentioned above and measured experimentally. The calculation results are in very good agreement with the experimental results, for all the three methods, the calculation error is less than 7% compared with the numerical range of second-order buoyancy. Both calculation and experimental results show that the second-order buoyancy increases rapidly with the decrease of the axial distance between the bottom of the magnet and the container, and the trend can be fitted well by an exponential function.

Description: This paper is conducted to design, fabricate, and characterize a novel high-density large-scale ultrasonic transducer, which is based on a $64times 4$ array of 0.753 MHz piezoelectric micromachined ultrasonic transducer (pMUT) with $100~mu text{m}$ size and $120~mu text{m}$ pitch. The fill factor of the array is 69.4%, which is higher than the other arrays in reports. Sol-gel method with layer-by-layer annealing is used to fabricate isolated piezoelectric lead zirconate titanate elements into a piezoelectric layer. The membrane of the pMUT is released by backside deep silicon etching. The impedance-frequency spectrum of a $4times 5$ subarray of the transducers is characterized by HP 4294A impedance phase analyzer. As a result, the mean value resonant frequency is 0.753 MHz, in reasonable agreement with COMSOL Multiphysics simulation results. And the array shows good uniformity in resonant frequency. The equivalent circuit of the transducer is extracted from the spectrum and the admittance circle deduced from the circuit is in accordance with experimental data. The results show that the present array has a good application prospect on ultrasonic imaging.

Description: A novel simple and rapid detection method for interferon-gamma (IFN- $gamma$ ) was developed based on a label-free single-stranded DNA (ssDNA) and thioflavin T (ThT). The ssDNA is the aptamer of the IFN- $gamma $ . The aptamer is a G-rich oligonucleotide, and it can fold into a G-quadruplex structure in the presence of ThT, resulting in a high fluorescence emission. Upon the addition of the target, the aptamer combines with its ligand due to higher affinity and specificity, leading to the disruption of the G-quadruplex and the release of ThT. A remarkable decrease of fluorescence was observed. The proposed strategy shows a limit of detection at 1 nM in both buffer and diluted serum. Moreover, the detection of IFN- $gamma $ can be finished in several minutes and it can be used repeatedly.

Description: This paper illustrates the extension of Rayleigh wave-based surface acoustic wave (SAW) viscosity and density sensor previously developed by the authors for integration with microfluidics and printed circuit board (PCB)-based electronics. The SAW device is first modeled with a microchannel and analyzed using finite-element method (FEM) software. Precise fabrication, alignment, and bonding of polydimethylsiloxane microchannels on diced $Y$ - $Z$ lithium niobate substrates are accomplished. A high-frequency PCB is built to obtain a better performance for SAW device testing. Low glycerin concentrations in deionized (DI) water are analyzed. The FEM simulation results and vector network analyzer measurements of the devices with the microchannel and PCB integration are presented. For low-frequency SAW sensor, a sensitivity of 171.9 Hz/(% glycerin) or 5.57 kHz/(kg/ $text{m}^{2}surd text{s}$ ) in frequency shifts, 0.09°/(% glycerin) or 2.92°/(kg/ $text{m}^{2}surd text{s}$ ) in phase difference, and minimum signal-to-noise ratio of 13.9 dB are achieved at peak frequency of 29.7 MHz. On the other hand, high-frequency (86.1 MHz) SAW sensor provides a sensitivity of 937.5 Hz/(% glycerin) or 37.15 kHz/(kg/ $text{m}^{mathbf {2}}surd text{s}$ ) in absolute frequency shifts, 0.37°/(% glycerin) or 14.7°/(kg/ $text{m}^{mathbf {2}}surd text{s}$ ) in phase difference, and minimum signal-to-noise ratio - f 20.5 dB.

Description: Near-infrared spectroscopy (NIRS) is a widely-used noninvasive optical technique for measuring concentration of hemoglobin species in human tissues. Despite its potential, NIRS has seen limited clinical application largely due to a lack of in-vivo standard reference. Hence, cross-validation studies to validate novel NIRS instrumentation against established NIRS devices are needed. This in-vivo study compared a wearable continuous-wave (CW-NIRS) oximeter against a non-wearable frequency-domain (FD-NIRS) oximeter on optical properties and hemoglobin concentrations. To measure absolute coefficients and stoichiometric hemoglobin concentrations, the CW-NIRS oximeter exploits spectral properties of water. Both CW-NIRS and FD-NIRS provided physiologically-valid measurements in skeletal muscles during normoxia, hypoxia, and hyperemia. Although absorption-scattering crosstalk was evident, absorption coefficient $mu _{a}$ and reduced scattering coefficient $mu '_{s}$ at 690nm and deoxyhemoglobin concentration (HbR) were mostly within agreement limits, and the measurements differed principally by a systematic offset. Specifically, CW-NIRS estimated larger $mu '_{s}$ values (+2 cm −1 ) and smaller $mu _{a}$ (from −0.04 to −0.08 cm −1 ) at all wavelengths. The results suggest that lower-cost, wearable CW-NIRS oximeters are a potential alternative to FD-NIRS to measure optical properties and molar concentration of oxyhemoglobin and HbR in skeletal muscles in-vivo as long as the estimated water content is reasonably accurate.

Description: Distributed Temperature Sensing (DTS) is a technique that uses the interaction of laser pulses with silica to continuously sense temperature along the length of fiber-optic cables. The temporal and spatial resolution of DTS makes it an excellent technique for monitoring the performance of district-scale geothermal exchange borefields. A dynamic, double-ended calibration routine developed in response to site-specific challenges and constraints (i.e., more than 5 km, many splices, different fiber segments, and extended observation periods) is systematically presented and analyzed to provide novel insight on calibration considerations. Results show that different combinations of calibration baths may change calibration accuracy, and over determination in the calculation of calibration parameters provides greater accuracy. Fixing the $gamma $ calibration parameter does not appreciably change accuracy but does provide a buffer against error from variations in calibration bath temperatures. Differential attenuation varied by up to 25% between discrete fiber sections and should be calculated for each array section to prevent errors generated from applying just one attenuation coefficient value for the entire fiber array. Furthermore, dynamically calculated differential attenuation may vary systematically with time and space. In a double-ended configuration, the consideration of whether the forward, reverse, or some combination of all light data is used will affect the robustness of the calibration over time. Each of these results may assist in thoughtful consideration of calibration design at future DTS installations facing similar challenges.

Description: Multi-scale geometric analysis, one of the most often-used multi-sensor image fusion (MSIF) techniques, can offer outstanding performance during extracting the features of source image. Interval type-2 fuzzy sets (Type-2 FS) have a good prospect in image fusion field, because it can effectively address the uncertain and fuzzy problem in image fusion for selecting the high-quality pixels or coefficients of source images. We try to extend the application fields of Type-2 FS and improve the performance of MSIF; therefore, this paper presents a hybrid method by combining the local spatial frequency (LSF) with interval Type-2 FS in nonsubsampled shearlet transform (NSST) domain. NSST is used to decompose source images, and interval Type-2 FS and LSF is employed to extract the regional features of source images; so it can extract and fuse the detailed features of different source images accurately. First, NSST is performed to decompose the source images into low frequency and high frequency sub-images. Second, LSF-based fusion rule is applied to fuse low frequency sub-images. Thirdly, a novel fusion process based on interval Type-2 FS is designed to fuse high frequency sub-images. At last, inverse NSST2 (INSST) is implemented to reconstruct the fused images. The experimental and contrastive results of different image sets show that the proposed method is an effective MSIF scheme, which can achieve better fusion effect than the existing representative methods.

Description: Flow-induced vibration of loose parts located on steam generator (SG) tube sheets can result in SG tube damage and eventual loss of SG efficiency. Regular inspection of SG tubes at support structures can help extend SG life by detecting and monitoring flaws as well as identifying accumulation of loose parts. Application of conventional eddy-current technologies in this case, however, is limited when magnetite fouling is present or when loose parts are resting directly on the SG tube sheet. Pulsed eddy current (PEC) combined with a modified principal components analysis (MPCA) was investigated as a method of detecting and identifying a variety of loose part materials in close proximity to 15.9-mm outer diameter Alloy-800 SG tubes and surrounding steel support structures. Loose parts were found to be detectable for varying axial positions along the SG tube and when in contact with the tube sheet. When offset radially from the SG tube, 1.6-mm-diameter carbon steel parts with a strong magnetic component allowed detection up to 3.2 mm away, while nonferromagnetic stainless steel parts of the same size could only be detected up to 1.5 mm away. Signal variation in the presence of loose parts was attributed to differences in amplitude of transient response and in relaxation times for diffusion of transient magnetic fields through various materials. The ability to accurately detect and identify loose parts located in close proximity to SG tubes establishes PEC combined with MPCA as a potential candidate for SG inspection.

Description: The use of affordable amendments required increasing the sensitivity and functionality of electrochemical devices is still challenging. Novel scalable, gold nanoparticles-grafted nitrogen-doped graphene nanostructured polyaniline nanocomposites were stupendously developed in this paper. The charge transfer resistance was dramatically decreased from 11000 $Omega $ (for bare fluorine doped tin oxide electrode) down to 115 $Omega $ (for the nanocomposite-modified electrode). The present nanocomposites provided a superb opportunity for the design of electrochemical biosensors with extraordinary performance via enhanced analyte sensitivity for the detection of ascorbic acid (0.640 mM detection limit at signal to noise ratio = 3, and two linear ranges of 0.96–4.66 and 5.06–9.86 mM with the sensitivity of 10.44 and $28.91~mu text{A}$ . mM $^{-1}$ . cm $^{-2}$ , respectively), and high selectivity over electroactive components interfering species, commonly found in real serum samples.

Description: A device-free human detection and tracking system using a received signal strength indicator (RSSI) for an indoor environment is presented in this paper. The proposed system has two major functions: a wireless communication system and a human detection and tracking system. The first function is developed for measuring and collecting RSSI signals affected by human presence and movement, while the second function is developed for detecting and tracking the human using a predefined threshold and a zone selection method. The novelty of our proposed system is that the communication protocol can avoid signal interference and packet loss in the network, and the detection and tracking method can specify an actual zone that the human is present by taking an optimal predefined threshold and a level of RSSI variation in each zone into consideration. The proposed system is verified by experiments, and various human movement patterns with different directions and speeds are tested. The experimental results show that the proposed communication protocol can significantly provide communication reliability, and the proposed method can properly detect and track human movements. The packet delivery ratio indicating communication reliability is almost 100%. Detection and tracking accuracy measured by the number of times the method can detect and track the human with the correct zone is almost 100% in all cases of one man movements.

Description: The presence of mobile nodes in any wireless network can affect the performance of the network, leading to higher packet loss and increased energy consumption. However, many recent applications require the support of mobility and an efficient approach to handle mobile nodes is essential. In this paper, a game scenario is formulated, where nodes compete for network resources in a selfish manner, to send their data packets to the sink node. Each node counts as a player in the non-cooperative game. The optimal solution for the game is found using the unique Nash equilibrium (NE), where a node cannot improve its pay-off function while other players use their current strategy. The proposed solution aims to present a strategy to control different parameters of mobile nodes (or static nodes in a mobile environment) including transmission rate, timers, and operation mode, in order to optimize the performance of routing protocol for low-power and lossy networks (RPL) under mobility in terms of packet delivery ratio, throughput, energy consumption, and end-to-end-delay. The proposed solution monitors the mobility of nodes based on received signal strength indication readings, and it also takes into account the priorities of different nodes and the current level of noise in order to select the preferred transmission rate. An optimized protocol called game-theory based mobile RPL (GTM-RPL) is implemented and tested in multiple scenarios with different network requirements for Internet of Things applications. Simulation results show that in the presence of mobility, GTM-RPL provides a flexible and an adaptable solution that improves throughput whilst maintaining lower energy consumption showing more than 10% improvement compared to related work. For applications with high throughput requirements, GTM-RPL shows a significant advantage with more than 16% improvement in throughput and 20% improvement in energy consumption.

Description: Digital measurement of differential settlement is crucial in the structural health assessment. A new differential settlement monitoring system using inverse perspective mapping (IPM) technique is proposed to measure the relative displacement of pillars. First, the displacement of adjacent pillars is indicated by a point of laser beam. Subsequently, camera module attached perpendicularly to the wall captures an image centroid of laser point. As the camera has the image perspective effect, IPM is used to transform the image coordinate of laser point into actual ground plane for vertical displacement measurement in millimeter. A relative displacement graph is plotted onto a Web-base for differential settlement monitoring. The proposed system allows camera to be set up closer to the laser projected wall. Hence, no camera holder is required and no gap is inserted between the camera and targeted screen for laser projection. As a result, the construction of the proposed system is relatively less complex and bulky. A test rig is built to evaluate the proposed differential settlement system. Empirical results showed that the proposed system obtains an accuracy of measurement up to 1 mm ± 0.1 mm. The test results also verify the performance of the system and its applicability to real structures.

Description: Our aim is to maximize the interpretable information for gait analysis. To achieve this, it is important to find the optimal sensor placement and the parameters that influence the extraction of automatic gait features. We investigated the effect of different anatomical foot locations on inertial measurement unit (IMU) sensor output. We designed and developed an android app to collect real-time synchronous sensor output. We selected a set of five anatomical foot locations covering most of the foot regions to place wearable IMU sensors for data collection. Each participant performed a trial in a straight corridor comprising 25 strides of normal walking, a turn-around, and another 25 strides. We proposed an automatic gait features extraction method to analyze the data for stride number, distance, speed, length and period of stride, stance, and swing phases during walking. The highest accuracy for detecting stride number was in location 1 (first cuneiform) followed by location 5 (Achilles Tendon) and 4 (Talus). Location 1 was the closest to correlate estimate to the measured distance travelled. The accuracy of detecting number of strides on average is 95.47% from accelerometer data and 93.60% from gyroscope data and closest to the 60:40% split for average stance and swing for 15 subjects. To validate our results, we conducted trials using the Qualisys motion capture instrument and from our sensors concurrently. The average accuracy of the result is 97.77% with 95% confidence interval 0.767 for estimated and 99.01% with 95% confidence interval 0.266 for period.

Description: The feasibility of an electromagnetic sensor to assist the autonomous walking of visually impaired and blind users is demonstrated in this paper. It is known that the people affected by visual diseases usually walk assisted by some supports, among which the white cane is the most common. Our idea consists in applying a microwave radar on the traditional white cane making aware the user about the presence of an obstacle in a wider and safer range. Compared to the already existing electronic travel aids devices, the proposed system exhibits better performance, noise tolerance, and reduced dimensions. In the following, the latest developments of this research activity are presented, with special concern for the miniaturization of circuit board and antennas. A laboratory prototype has been designed and realized and the first test results of obstacle detection are hereby shown to demonstrate the effectiveness of the system.

Description: This paper presents a sensor system to estimate wrist and elbow positions developing two wearable miniature motion sensors. The developed sensors consist of a processor, a motion sensor, which comprise a 3-D accelerometer, a 3-D gyroscope, a 3-D magnetometer, as well as a transceiver operating at industrial, scientific, and medical band frequency of 2.4 GHz. By proposing a modified complementary filter alongside employing hand kinematic problem, wrist and elbow positions for three different exercises are estimated. In order to determine the accuracy and precision of the developed system, the estimated positions are compared to an optical tracking system. The obtained results illustrate root-mean-square error (RMSE) of these two exercises for positions of $x$ -, $y$ -, and $z$ -axes less than 0.4, 0.59, and 0.29 cm, respectively. Also, the RMSEs of joint angles are obtained less than 1.64° in these exercises. These results show a 11%–23% improvement for estimating these positions than Madgwick filter and prove that the proposed system can be used as an accurate position feedback for monitoring hand joint movement in rehabilitation exercises.

Description: A new laser vision system is proposed for the navigation of mobile robots in pipe environments. This paper mainly focuses on forward recognition of individual T-junctions and elbow pipes to prevent the robots from becoming stuck in pipes. We propose to mount weaving 2-D laser scanners on robots to obtain selective 3-D front-pipe maps. Recognition of T-junctions requires 3-D maps for pipe wall on wheel sides, whereas recognition of elbow pipes requires full-forward 3-D maps. From these selective forward 3-D maps, algorithms for recognition of T-junctions and elbow pipes were developed, utilizing the mathematical approach to describe the pipeline structures. The practicality of the proposed algorithms was validated for several pipe environments, including T-junction, single elbow pipe, and the latter pipe of double elbow pipes.

Description: Eye movement tracking is concerned with many important areas of research, of which tracking with a traditional scleral search coil system is deemed the gold standard. However, since such coil system requires anaesthetic use due to the stiffness of the copper wire coil in the system, it is of great importance to improve the wearing sensation experience. Here, an innovative electronic system for eye movement recording is proposed and realized, in which a liquid metal coil is introduced as the core element, attempting to provide a comfortable eye sensation. Theoretical investigation shows that this type of coil has performance similar to the traditional copper coil, which implies it is suitable for eye movement tracking. It has been demonstrated that such liquid metal coil is soft, flexible, and easy to produce, indicating that the coil is potentially favorable as a wearable medical device. Experiments show that the variance fitting curve between the induced voltage and rotation angle measured with the liquid metal coil during eye movement accords well with that of the classical copper scleral search coil, with both determination coefficients over 0.98, which confirms the reliability of the new coil. Overall, the feasibility of the liquid metal coil system suggests a useful approach for detecting human eye movement behavior and related diseases, which may facilitate disease diagnosis and prediction.

Description: This paper presents a comprehensive evaluation of fabrication techniques for the integration of coils into textiles, for the purpose of enabling low-power wireless power transfer; for example, the powering of on-body monitoring devices, such as heart-rate monitors. Key electrical parameters of the coils required to maximize power transfer efficiency are identified from theory. Flexible coils have been fabricated using standard processes widely used in the textile industry, such as screen printing and embroidery. The screen printed coils were fabricated with a silver-polymer ink on a printed interface layer; the embroidered coils were fabricated using a variety of conductive threads formed by coating textile fibers and through the use of copper fibers. These coils have been experimentally characterized and evaluated for use in wireless power transfer applications. The effects of coil geometry and separation on the dc–dc power transfer efficiency using Qi standard compliant driver and receiver circuits are reported.

Description: In this paper, throughput maximization in a wireless energy harvesting uplink is considered. For fairness, we assume that each node’s throughput cannot drop below what it achieves by direct transmission to the base station. The throughput is maximized using data relaying and by optimally assigning a role to each node, i.e., relay, source (direct transmission), or user (of a relay). Both centralized and decentralized algorithms that find the optimal assignment of each node are proposed and studied. We consider fixed and variable transmit power scenarios and address complexity issues. Our algorithms are shown to have a linear or quadratic complexity compared with the exponential complexity of the brute force approach. Compared with cooperative transmission, our approach maximizes the overall throughput of the network such that no node’s throughput is adversely affected.

Description: The phonocardiogram (PCG) signal indicates closing instants of atrio-ventricular and semilunar valves, and this information can also be extracted from two major profiles of a seismocardiographic (SCG) cycle. This letter presents a method to extract fundamental heart sounds (HSs) from a SCG signal. The proposed method employs discrete wavelet transform for signal decomposition, and subsequently, center of gravity (CoG) of power spectrum for each of the subbands is computed. Our method can optimally select wavelet subbands for any combination of sampling frequencies of the signal and number of decomposition levels. Based on proposed CoG criterion, the signal is reconstructed from a number of selected subbands, and instantaneous Hilbert envelope is constructed for localizing peaks of the signal. Finally, a simple decision rule is applied to annotate S1 and S2 sounds of the PCG signal. Experimental results show that the HS waves S1 and S2 can be well localized with the help of one SCG cycle without using a reference ECG cycle.

Description: The two-stream CNNs prove very successful for video-based action recognition. However, the classical two-stream CNNs are time costly, mainly due to the bottleneck of calculating optical flows (OFs). In this paper, we propose a two-stream-based real-time action recognition approach by using motion vector (MV) to replace OF. MVs are encoded in video stream and can be extracted directly without extra calculation. However, directly training CNN with MVs degrades accuracy severely due to the noise and the lack of fine details in MVs. In order to relieve this problem, we propose four training strategies which leverage the knowledge learned from OF CNN to enhance the accuracy of MV CNN. Our insight is that MV and OF share inherent similar structures which allow us to transfer knowledge from one domain to another. To fully utilize the knowledge learned in OF domain, we develop deeply transferred MV CNN. Experimental results on various datasets show the effectiveness of our training strategies. Our approach is significantly faster than OF based approaches and achieves processing speed of 390.7 frames per second, surpassing real-time requirement. We release our model and code to facilitate further research. 1 1 https://github.com/zbwglory/MV-release

Description: This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using $alpha $ -expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Description: Internet of Things (IoT) requires cloud infrastructures for data analysis (e.g., temperature monitoring, energy consumption measurement, etc.). Traditionally, cloud services have been implemented in large datacenters in the core network. Core cloud offers high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, collecting information and bringing it into one core cloud infrastructure is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A scalable and efficient solution, both at the network and cloud level, is to distribute the IoT analytics between the core cloud and the edge of the network (e.g., first analytics on the edge cloud and the big data analytics on the core cloud). For an efficient distribution of IoT analytics and use of network resources, it requires to integrate the control of the transport networks (packet and optical) with the distributed edge and cloud resources in order to deploy dynamic and efficient IoT services. This paper presents and experimentally validates the first IoT-aware multilayer (packet/optical) transport software defined networking and edge/cloud orchestration architecture that deploys an IoT-traffic control and congestion avoidance mechanism for dynamic distribution of IoT processing to the edge of the network (i.e., edge computing) based on the actual network resource state.

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Description: In face of staggering traffic growth driven by cloud-based platforms, modern optical networks—forming the backbone of such connectivity—are faced with increasing requirements in terms of operational reliability. The challenge is that of cognition-driven learning and fault management workflows, cost-effectively assuring the next-generation networks. Machine learning, an artificial intelligence tool, can be conceived as an extremely promising instrument to address network assurance via dynamic data-driven operation, as opposed to static pre-engineered solutions. In this paper, we propose and demonstrate a cognitive fault detection architecture for intelligent network assurance. We introduce the concept of cognitive fault management, elaborate on its integration in transport software defined network controller, and demonstrate its operation based on real-world fault examples. Our framework both detects and identifies significant faults, and outperforms conventional fixed threshold-triggered operations, both in terms of detection accuracy and proactive reaction time.

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Description: Multicore fiber (MCF) transmission is considered as one of the promising technologies for breaking the capacity limit of traditional single mode fibers. Managing the crosstalk (XT) and configuring optical paths adaptively based on the XT as well as achieving longer distance and larger capacity transmission are important, because intercore XT could be the main limiting factor for MCF transmission. In a real MCF network, the intercore XT in a particular core is likely to change continuously as the optical paths in the adjacent cores are dynamically assigned to match the dynamic nature of the data traffic. If we configure the optical paths while ignoring the intercore XT value, the Q-factors may become excessive. Therefore, monitoring the intercore XT value continuously and configuring optical path parameters adaptively and flexibly are essential. To address these challenges, we develop an MCF transport network testbed and demonstrate an XT-aware traffic engineering scenario. With the help of a software-defined network controller, the modulation format and optical path route are adaptively changed based on the monitored XT values by using programmable devices such as a real-time transponder and a reconfigurable optical add–drop multiplexer.

Description: Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

Description: Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers, and data sources for the attacks, giving structure to the fuzzy term “smart city.” Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities.

Description: A variety of communication networks, such as industrial communication systems, have to provide strict delay guarantees to the carried flows. Fast and close to optimal quality of service (QoS) routing algorithms, e.g., delay-constrained least-cost (DCLC) routing algorithms, are required for routing flows in such networks with strict delay requirements. The emerging software-defined networking (SDN) paradigm centralizes the network control in SDN controllers that can centrally execute QoS routing algorithms. A wide range of QoS routing algorithms have been proposed in the literature and examined in individual studies. However, a comprehensive evaluation framework and quantitative comparison of QoS routing algorithms that can serve as a basis for selecting and further advancing QoS routing in SDN networks is missing in the literature. This makes it difficult to select the most appropriate QoS routing algorithm for a particular use case, e.g., for SDN controlled industrial communications. We close this gap in the literature by conducting a comprehensive up-to-date survey of centralized QoS routing algorithms. We introduce a novel four-dimensional (4D) evaluation framework for QoS routing algorithms, whereby the 4D correspond to the type of topology, two forms of scalability of a topology, and the tightness of the delay constraint. We implemented 26 selected DCLC algorithms and compared their runtime and cost inefficiency within the 4D evaluation framework. While the main conclusion of this evaluation is that the best algorithm depends on the specific sub-space of the 4D space that is targeted, we identify two algorithms, namely Lagrange relaxation-based aggregated cost (LARAC) and search space reduction delay-cost-constrained routing (SSR+DCCR), that perform very well in most of the 4D evaluation space.