WILBERT

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

Description: The Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO)-SkyMed Second Generation (CSG) ground segment (GS) is based on an interoperable and multimission design that provides CSG functionalities to external partners and access through the CSG to services belonging to other Earth observation (EO) partners. Moreover, the CSG GS design supports such cooperation by expansion through replication of user GSs (UGSs) in different ways. In this manner, the CSG GS is able to manage EO foreign missions by providing centralized and multimission access in an integrated environment, thus offering valuable technological solutions to the defense and civilian communities. This article provides an indepth description of the CSG system access portfolio, focusing on the architectural details of the GS that allow the provisioning and exploitation of the CSG's interoperability, expandability, and multisensor/multimission (IEM) features.

Description: Advertisement.

Description: Advertisement.

Description: Advertisement.

Description: Advertisement.

Description: Advertisement.

Description: Advertisement.

Description: We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++ , which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.

Description: In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors ( de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

Description: The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result.

Description: Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines $L_{0}$ , $L_{1}$ and $L_{2}$ regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

Description: This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.

Description: In this paper, we consider the inference problem for a wide class of time-series models, referred to as multistate autoregressive models. The time series that we consider are composed of multiple epochs, each modeled by an autoregressive process. The number of epochs is unknown, and the transitions of states follow a Markov process of an unknown order. We propose an inference strategy that enables reliable and efficient offline analysis of this class of time series. The inference is carried out through a three-step approach: detecting the structural changes of the time series using a recently proposed multiwindow algorithm, identifying each segment as a state and selecting the most appropriate number of states, and estimating the Markov source based upon the symbolic sequence obtained from previous steps. We provide theoretical results and algorithms in order to facilitate the inference procedure described above. We demonstrate the accuracy, efficiency, and wide applicability of the proposed algorithms via an array of experiments using synthetic and real-world data.

Description: Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation problem with graph Laplacian constraints. While such problems are convex, existing methods cannot guarantee that solutions will have specific graph topology properties (e.g., being a tree), which are desirable for some applications. The problem of learning a graph with topology properties is in general non-convex. In this paper, we propose an approach to solve these problems by decomposing them into two sub-problems for which efficient solutions are known. Specifically, a graph topology inference (GTI) step is employed to select a feasible graph topology. Then, a graph weight estimation (GWE) step is performed by solving a generalized graph Laplacian estimation problem, where edges are constrained by the topology found in the GTI step. Our main result is a bound on the error of the GWE step as a function of the error in the GTI step. This error bound indicates that the GTI step should be solved using an algorithm that approximates the data similarity matrix by another matrix whose entries have been thresholded to zero to have the desired type of graph topology. The GTI stage can leverage existing methods, which are typically based on minimizing the total weight of removed edges. Since the GWE stage is an inverse covariance estimation problem with linear constraints, it can be solved using existing convex optimization methods. We demonstrate that our approach can achieve good results for both synthetic and texture image data.

Description: This special section of the IEEE/ACM Transactions on Computational Biology and Bioinformatics contains extended versions of the best papers presented at the First International Conference on Algorithms for Computational Biology (AlCoB 2014). Out of 39 submissions to the conference, only four papers representing the current state-of-the-art in their respective domains were accepted to this special section.

Description: In this paper, we study the feasibility of an opportunistic radar, which exploits the probing signals transmitted during the sector level sweep of the IEEE 802.11ad beamforming training protocol. Several solutions are presented to detect the presence of prospective obstacles and estimate their position, radial velocity, and backscattered signal amplitude, which differ in the amount of prior information as to the transmitted signal and the channel fluctuation. Also, we derive the Cramér–Rao bound as a benchmark for the proposed estimators: The derivation of these bounds is per se relevant, as it generalizes classical results to the case where the echo is not entirely contained in the observation window. Numerical examples are provided to assess performance of the proposed solutions. The results indicate that the close-to-one detection probability is achievable up to 90 m with a probability of false alarm of 1e-4 and Swerling-I target fluctuation; in this region, the target delay is estimated with an accuracy smaller than the symbol interval (corresponding to a range resolution smaller than 10 cm) with probability close to one, while the velocity estimate is generally quite poor as a consequence of the very short duration of the probing signal.

Description: This paper studies resilient multiagent distributed estimation of an unknown vector parameter when a subset of the agents is adversarial. We present and analyze a flag raising distributed estimator ( $mathcal {FRDE}$ ) that allows the agents under attack to perform accurate parameter estimation and detect the adversarial agents. The $mathcal {FRDE}$ algorithm is a consensus+innovations estimator in which agents combine estimates of neighboring agents (consensus) with local sensing information (innovations). We establish that, under $mathcal {FRDE}$ , either the uncompromised agents' estimates are almost surely consistent, or the uncompromised agents detect compromised agents (with arbitrarily small false alarm probability) if and only if the network of uncompromised agents is connected and globally observable. Numerical examples illustrate the performance of $mathcal {FRDE}$ .

Description: Genome Rearrangements are large-scale mutational events that affect genomes during the evolutionary process. Therefore, these mutations differ from punctual mutations. They can move genes from one place to the other, change the orientation of some genes, or even change the number of chromosomes. In this work, we deal with inversion events which occur when a segment of DNA sequence in the genome is reversed. In our model, each inversion costs the number of elements in the reversed segment. We present a new algorithm for this problem based on the metaheuristic called Greedy Randomized Adaptive Search Procedure (GRASP) that has been routinely used to find solutions for combinatorial optimization problems. In essence, we implemented an iterative process in which each iteration receives a feasible solution whose neighborhood is investigated. Our analysis shows that we outperform any other approach by significant margin. We also use our algorithm to build phylogenetic trees for a subset of species in the Yersinia genus and we compared our trees to other results in the literature.

Description: Read trimming is a fundamental first step of the analysis of next generation sequencing (NGS) data. Traditionally, it is performed heuristically, and algorithmic work in this area has been neglected. Here, we address this topic and formulate three optimization problems for block-based trimming (truncating the same low-quality positions at both ends for all reads and removing low-quality truncated reads). We find that all problems are NP-hard. Hence, we investigate the approximability of the problems. Two of them are NP-hard to approximate. However, the non-random distribution of quality scores in NGS data sets makes it tempting to speculate that quality constraints for read positions are typically satisfied by fulfilling quality constraints for reads. Thus, we propose three relaxed problems and develop efficient polynomial-time algorithms for them including heuristic speed-up techniques and parallelizations. We apply these optimized block trimming algorithms to 12 data sets from three species, four sequencers, and read lengths ranging from 36 to 101 bp and find that (i) the omitted constraints are indeed almost always satisfied, (ii) the optimized read trimming algorithms typically yield a higher number of untrimmed bases than traditional heuristics, and (iii) these results can be generalized to alternative objective functions beyond counting the number of untrimmed bases.

Description: The endoplasmic reticulum (ER) is an intricate network that pervades the entire cortex of plant cells and its geometric shape undergoes drastic changes. This paper proposes a mathematical model to reconstruct geometric network dynamics by combining the node movements within the network and topological changes engendered by these nodes. The network topology in the model is determined by a modified optimization procedure from the work (Lemarchand, et al. 2014) which minimizes the total length taking into account both degree and angle constraints, beyond the conditions of connectedness and planarity. A novel feature for solving our optimization problem is the use of “lifted” angle constraints, which allows one to considerably reduce the solution runtimes. Using this optimization technique and a Langevin approach for the branching node movement, the simulated network dynamics represent the ER network dynamics observed under latrunculin B treated condition and recaptures features such as the appearance/disappearance of loops within the ER under the native condition. The proposed modeling approach allows quantitative comparison of networks between the model and experimental data based on topological changes induced by node dynamics. An increased temporal resolution of experimental data will allow a more detailed comparison of network dynamics using this modeling approach.

Description: Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the $ell _1$ norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted $ell _2$ method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the $ell _1$ norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior info- mation about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.

Description: Phylogenetic tree reconciliation is an important technique for reconstructing the evolutionary histories of species and genes and other dependent entities. Reconciliation is typically performed in a maximum parsimony framework and the number of optimal reconciliations can grow exponentially with the size of the trees, making it difficult to understand the solution space. This paper demonstrates how a small number of reconciliations can be found that collectively contain the most highly supported events in the solution space. While we show that the formal problem is NP-complete, we give a $1-frac{1}{e}$ approximation algorithm, experimental results that indicate its effectiveness, and the new DTL-RnB software tool that uses our algorithms to summarize the space of optimal reconciliations ( www.cs.hmc.edu/dtlrnb ).

Description: A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and “realistic” trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of “realistic” rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of “autumn trees”, a nice framework for the formulation of algorithms based on the mathematics of “maximum acyclic agreement forests”. While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

Description: Reconstruction of ancestral relationships among genera, species, and populations is a core task in evolutionary biology. At the population level, pedigrees have been commonly used. Reconstruction of pedigree is required in practice due to legal or medical reasons. Pedigrees are very important to geneticists for inferring haplotype segments, recombination, and allele sharing status with which disease loci can be identified. Evaluating reconstruction methods requires comparing the inferred pedigree and the known pedigrees. Moreover, comparison of pedigrees is required in studying relationships among crops such as maize, wheat and barley, etc. In this paper, we discuss three models for comparison of pedigrees, the maximum pedigree isomorphism problem, the maximum paternal-path-preserved mapping problem, and the minimum edge-cutting mapping problem. For the maximum pedigree isomorphism problem, we prove that the problem is NP-hard and give a fixed-parameter algorithm for the problem. For the maximum paternal-path-preserved mapping problem, we give a dynamic-programming algorithm to find the mapping that preserves the maximum number of paternal paths between the two input pedigrees. For the minimum edge-cutting mapping problem, we prove that the problem is NP-hard and give a fixed-parameter algorithm with running time $O(n(1+sqrt{2})^k)$ , where $n$ is the number of vertices in the two input pedigrees and $k$ is the number of edges to be cut. This algorithm is useful in practice when comparing two similar pedigrees.

Description: The human colorectal carcinoma cell line (Caco-2) is a commonly used in-vitro test that predicts the absorption potential of orally administered drugs. In-silico prediction methods, based on the Caco-2 assay data, may increase the effectiveness of the high-throughput screening of new drug candidates. However, previously developed in-silico models that predict the Caco-2 cellular permeability of chemical compounds use handcrafted features that may be dataset-specific and induce over-fitting problems. Deep Neural Network (DNN) generates high-level features based on non-linear transformations for raw features, which provides high discriminant power and, therefore, creates a good generalized model. We present a DNN-based binary Caco-2 permeability classifier. Our model was constructed based on 663 chemical compounds with in-vitro Caco-2 apparent permeability data. Two hundred nine molecular descriptors are used for generating the high-level features during DNN model generation. Dropout regularization is applied to solve the over-fitting problem and the non-linear activation. The Rectified Linear Unit (ReLU) is adopted to reduce the vanishing gradient problem. The results demonstrate that the high-level features generated by the DNN are more robust than handcrafted features for predicting the cellular permeability of structurally diverse chemical compounds in Caco-2 cell lines.

Description: To assess the genetic diversity of an environmental sample in metagenomics studies, the amplicon sequences of 16s rRNA genes need to be clustered into operational taxonomic units (OTUs). Many existing tools for OTU clustering trade off between accuracy and computational efficiency. We propose a novel OTU clustering algorithm, hc-OTU, which achieves high accuracy and fast runtime by exploiting homopolymer compaction and k-mer profiling to significantly reduce the computing time for pairwise distances of amplicon sequences. We compare the proposed method with other widely used methods, including UCLUST, CD-HIT, MOTHUR, ESPRIT, ESPRIT-TREE, and CLUSTOM, comprehensively, using nine different experimental datasets and many evaluation metrics, such as normalized mutual information, adjusted Rand index, measure of concordance, and F-score. Our evaluation reveals that the proposed method achieves a level of accuracy comparable to the respective accuracy levels of MOTHUR and ESPRIT-TREE, two widely used OTU clustering methods, while delivering orders-of-magnitude speedups.

Description: Advances in de novo synthesis of DNA and computational gene design methods make possible the customization of genes by direct manipulation of features such as codon bias and mRNA secondary structure. Codon context is another feature significantly affecting mRNA translational efficiency, but existing methods and tools for evaluating and designing novel optimized protein coding sequences utilize untested heuristics and do not provide quantifiable guarantees on design quality. In this study we examine statistical properties of codon context measures in an effort to better understand the phenomenon. We analyze the computational complexity of codon context optimization and design exact and efficient heuristic gene recoding algorithms under reasonable constraint models. We also present a web-based tool for evaluating codon context bias in the appropriate context.

Description: Hi-C technology, a chromosome conformation capture (3C) based method, has been developed to capture genome-wide interactions at a given resolution. The next challenge is to reconstruct 3D structure of genome from the 3C-derived data computationally. Several existing methods have been proposed to obtain a consensus structure or ensemble structures. These methods can be categorized as probabilistic models or restraint-based models. In this paper, we propose a method, named ShRec3D+, to infer a consensus 3D structure of a genome from Hi-C data. The method is a two-step algorithm which is based on ChromSDE and ShRec3D methods. First, correct the conversion factor by golden section search for converting interaction frequency data to a distance weighted graph. Second, apply shortest-path algorithm and multi-dimensional scaling (MDS) algorithm to compute the 3D coordinates of a set of genomic loci from the distance graph. We validate ShRec3D+ accuracy on both simulation data and publicly Hi-C data. Our test results indicate that our method successfully corrects the parameter with a given resolution, is more accurate than ShRec3D, and is more efficient and robust than ChromSDE.

Description: Differential gene expression testing is an analysis commonly applied to RNA-Seq data. These statistical tests identify genes that are significantly different across phenotypes. We extend this testing paradigm to multivariate gene interactions from a classification perspective with the goal to detect novel gene interactions for the phenotypes of interest. This is achieved through our novel computational framework comprised of a hierarchical statistical model of the RNA-Seq processing pipeline and the corresponding optimal Bayesian classifier. Through Markov Chain Monte Carlo sampling and Monte Carlo integration, we compute quantities where no analytical formulation exists. The performance is then illustrated on an expression dataset from a dietary intervention study where we identify gene pairs that have low classification error yet were not identified as differentially expressed. Additionally, we have released the software package to perform OBC classification on RNA-Seq data under an open source license and is available at http://bit.ly/obc_package .

Description: New de novo transcriptome assembly and annotation methods provide an incredible opportunity to study the transcriptome of organisms that lack an assembled and annotated genome. There are currently a number of de novo transcriptome assembly methods, but it has been difficult to evaluate the quality of these assemblies. In order to assess the quality of the transcriptome assemblies, we composed a workflow of multiple quality check measurements that in combination provide a clear evaluation of the assembly performance. We presented novel transcriptome assemblies and functional annotations for Pacific Whiteleg Shrimp ( Litopenaeus vannamei ), a mariculture species with great national and international interest, and no solid transcriptome/genome reference. We examined Pacific Whiteleg transcriptome assemblies via multiple metrics, and provide an improved gene annotation. Our investigations show that assessing the quality of an assembly purely based on the assembler's statistical measurements can be misleading; we propose a hybrid approach that consists of statistical quality checks and further biological-based evaluations.

Description: Future 5G systems will include a point-to-multipoint (P2MP) transmission mode to achieve high capacity and high spectrum efficiency for multiple use cases, such as IoT, lifeline communications, and broadcast-type services. Layered-division-multiplexing (LDM) is a novel non-orthogonal multiplexing technology recently adopted by the next generation digital TV broadcast system, ATSC 3.0, which is capable of providing significant capacity improvement when delivering multiple broadcast services simultaneously. This article explores the application of LDM as an enabling technology for 5G to achieve high-efficiency P2MP transmission and to deliver more diversified broadcast-type services using the mobile broadband infrastructure. The potential advantages that can be offered by LDM are demonstrated by capacity analysis and computer simulations. Coverage studies show that a 5G P2MP subsystem with LDM can deliver high-quality broadcast services using the broadband infrastructure. Finally, some general guidelines on the receiver implementation are presented to minimize the hardware complexity of consumer devices.

Description: According to a report by the World Health Organization, diseases caused by an unhealthy lifestyle represent the leading cause of death all over the world. Therefore, it is crucial to monitor and avoid users' unhealthy behaviors. Existing health monitoring approaches still face many challenges of limited intelligence due to insufficient healthcare data. Therefore, this article proposes a smart personal health advisor (SPHA) for comprehensive and intelligent health monitoring and guidance. The SPHA monitors both physiological and psychological states of the user. The SPHAScore model is proposed to evaluate the overall health status of the user. Finally, a testbed for verification of feasibility and applicability of the proposed system was developed. The experimental and simulation results have shown that the proposed approach is efficient for proper user state monitoring.

Description: Our interactions with the world are increasingly dependent on context-aware services, and the future of smart cities is coupled with how efficiently and reliably we can deliver these services to end users. In this article we present the premise of personalized IoT systems, by leveraging novel advancements in user-centric technologies under the fog computing architecture. This means leveraging the connectivity and processing potential of the fog to bring IoT control and analytics closer to the user, and improve the coupling of services with local IoT components in user-centric contexts. The potential gain in access latency and context-sensitive service matching will enable a multitude of smart city services. On one hand, data management (collection, pruning, denaturing [1], and encryption) can take place closer to the edge, thereby leveraging network load and service times. On the other hand, service matching in smart city applications will witness higher responsiveness and resource visibility in areas with intermittent connectivity or high mobility. We first present the challenges in migrating cloud-IoT architectures to the network edge, and detail the hindrances in transitioning the control and management of IoT systems to the user end. As a remedy, we survey recent advancements in the IoT, ubiquitous computing, and user-centric services, which enable us to advance personalized IoT architectures. We finally present a framework for IoT in the fog to synergize these advancements, and present a proof-of-concept use case to highlight its utility and impact. We conclude this article with prime directions for future work to realize a personalized IoT architecture, and highlight the potential gain in prioritizing five high-yield potential research issues.

Description: TCP BBR is a new TCP variant developed at Google, which, as of this year, is fully deployed in Google's internal WANs and used by services such as Google.com and YouTube. In contrast to other commonly used TCP variants, TCP BBR is not loss-based but model-based: It builds a model of the network path between communicating nodes in terms of bottleneck bandwidth and minimum round-trip delay and tries to operate at the point where all available bandwidth is used and the round-trip delay is at a minimum. Although TCP BBR has indeed resulted in lower latency and more efficient usage of bandwidth in fixed networks, its performance over cellular networks is less clear. This article studies TCP BBR in live mobile networks and through emulations, and compares its performance with TCP NewReno and TCP CUBIC, two of the most commonly used TCP variants. The results from these studies suggest that in most cases TCP BBR outperforms both TCP NewReno and TCP CUBIC. However, under precise network conditions, competing TCP BBR flows do not share the available bandwidth in a fair way, something that shows up, for example, when shorter TCP BBR flows struggle to get their fair share from longer ones.

Description: After ECN was first added to IP in 2001, it was hit by a succession of deployment problems. Studies in recent years have concluded that path traversal of ECN has become close to universal. In this article, we test whether the performance enhancement called ECN++ will face a similar deployment struggle as did base ECN. For this, we assess the feasibility of ECN++ deployment over mobile as well as fixed networks. In the process, we discover bad news for the base ECN protocol: contrary to accepted beliefs, more than half the mobile carriers we tested wipe the ECN field at the first upstream hop. All packets still get through, and congestion control still functions, just without the benefits of ECN. This throws into question whether previous studies used representative vantage points. This article also reports the good news that, wherever ECN gets through, we found no deployment problems for the "++" enhancement to ECN. The article includes the results of other in-depth tests that check whether servers that claim to support ECN actually respond correctly to explicit congestion feedback. Those interested can access the raw measurement data online.

Description: Customer experience is becoming of utmost importance for operators in home network management. Hence, a notion of the customers' QoE is vital, but has mostly been neglected in favor of QoS. In this article, we aim to close a significant gap between QoS and QoE in home networks by proposing a framework for inferring QoE from remotely collected network QoS metrics. We focus on video services (e.g., YouTube application) as the main contributor and generator of indoor network traffic. A case study is performed where an experimentally obtained dataset comprising network and application QoS parameters is obtained under varying conditions (i.e., poor coverage, network overload, contention, and interference). Predictive modeling is then used to build a predictor for multiple QoE classes given the network QoS metrics remotely accessible from access points based on industry adopted standards (i.e., TR-181). This enables operators to infer specific QoE metrics using remotely collected passive network measurement with no knowledge of application-specific parameters. We show that the proposed framework achieves accuracy in the range of 85 to 95 percent depending on the QoE class, hence demonstrating the effectiveness and potential of our approach.

Description: The use of license-exempt bands offers a promising opportunity to additionally enhance operator networks to meet the future traffic demand. The recent specification efforts in 3GPP have resulted in two major aggregation features that enable LTE networks to benefit from unlicensed spectrum via WLAN. In this article, we provide a thorough overview of these features known as, LTE-WLAN aggregation (LWA) and LTE-WLAN radio level integration with IP security tunnel (LWIP). The article presents and motivates the design choices of protocol architectures, procedures, mobility, and security. It also proposes flow control algorithms suitable for both technologies, which aim at the best usage of licensed and unlicensed spectrum. Simulation results show the performance unveiling the potential gains of these features in different load conditions, also in a comparative manner, showing that LWA substantially outperforms LWIP.

Description: We propose and experimentally demonstrate vector signals seamless integration delivery over fiber-THz-fiber link. Up to 13 Gb/s quadrature-phase-shift-keying signals are transmitted over 10 km single mode fiber-28 (SMF-28), 3.8 m wireless link, and 2.2 km SMF-28 link. At the transmitter, we use a photonics-aided scheme to generate 450 GHz THz signal. While at the receiver side, we employ analog down-conversion by one electrical mixer, and then the down-converted signals are used to drive one directly modulated laser to realize electrical-to-optical conversion.

Description: In this letter, we propose an iterative Kalman type algorithm based on posterior linearization. The proposed algorithm uses a nested loop structure to optimize the mean of the estimate in the inner loop and update the covariance, which is a computationally more expensive operation, only in the outer loop. The optimization of the mean update is done using a damped algorithm to avoid divergence. Our simulations show that the proposed algorithm is more accurate than existing iterative Kalman filters.

Description: This letter addresses the problem of target localization based on two-way time of arrival (TW-ToA) measurements with clock imperfections. In addition to the target location, the turn-around times and clock skews are considered unknown. Since an optimal estimator for this problem cannot be tackled directly, we approximate it by a suboptimal, robust one, formulated as a generalized trust region subproblem. Even though nonconvex in general, exact solution of the derived estimator can be obtained by just a bisection procedure. Simulation results validate the effectiveness of the proposed technique, matching the performance of the state of the art with significantly lower computational complexity.

Description: Graph-based dimensionality reduction techniques have been widely and successfully applied to clustering and classification tasks. The basis of these algorithms is the constructed graph which dictates their performance. In general, the graph is defined by the input affinity matrix. However, the affinity matrix derived from the data is sometimes suboptimal for dimension reduction as the data used are very noisy. To address this issue, we propose the projective unsupervised flexible embedding models with optimal graph (PUFE-OG). We build an optimal graph by adjusting the affinity matrix. To tackle the out-of-sample problem, we employ a linear regression term to learn a projection matrix. The optimal graph and the projection matrix are jointly learned by integrating the manifold regularizer and regression residual into a unified model. The experimental results on the public benchmark datasets demonstrate that the proposed PUFE-OG outperforms state-of-the-art methods.

Description: In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance. Extensive experimental results on challenging benchmark data sets demonstrate that the proposed CPF tracking algorithm performs favorably against the state-of-the-art methods.

Description: We propose a new trajectory clustering method using submodular optimization for better motion segmentation in videos. A small number of representative trajectories are first selected by submodular maximization automatically. Then all the initial trajectories can be segmented into fragments with the representative trajectories as centers of fragments. At last, fragments are merged into clusters by a two-stage bottom-up clustering method, and each cluster shows the motion of one moving object. The submodular energy function integrates the quality of all trajectories and their correlations. As a result, thousands of initial trajectories are replaced by only dozens of representative trajectories, which will reduce the negative influence of inaccurate initial trajectories on motion segmentation. The representative trajectories will have larger weights while extracting color or texture information of each moving entity at the step of motion segmentation. Experimental results demonstrate that our method can divide trajectories into more accurate clusters. The final motion segmentation results also illustrate that our method outperforms state-of-the-art motion segmentation methods based on trajectory clustering. 1 1 Our source code is available at https://github.com/shenjianbing/submodularmotion .

Description: This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

Description: Noise estimation is crucial in many image processing algorithms such as image denoising. Conventionally, the noise is assumed as a signal-independent additive white Gaussian process. However, for the real raw data of image sensor, the present noise should be practically modeled as signal dependent. In this paper, we propose an effective and fast image sensor noise estimation method for a single raw image. The noise model parameters are estimated via constrained weighted least squares (WLS) fitting on a number of data samples, each of which is generated from a group of weakly textured patches. Specifically, we first design a fast scheme for selecting weakly textured patches, with the guidance of image histogram. To robustly fit the data samples, we then explicitly account for the credibility of each sample by measuring the texture strength of the grouped patches. The image sensor noise estimation is finally formulated as a constrained WLS optimization problem, which can be solved efficiently. Experimental results demonstrate that our method could run much faster than the existing schemes, while retaining the state-of-the-art estimation performance.

Description: Presents the table of contents for this issue of the publication.

Description: The implementation of automatic image registration is still difficult in various applications. In this paper, an automatic image registration approach through line-support region segmentation and geometrical outlier removal is proposed. This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations. To begin with, line-support regions, namely a straight region whose points share roughly the same image gradient angle, are extracted to address the issues of inconsistent content existing in images. To alleviate the incompleteness of line segments, an iterative strategy with multi-resolution is employed to preserve global structures that are masked at full resolution by image details or noise. Then, geometrical outlier removal is developed to provide reliable feature point matching, which is based on affine-invariant geometrical classifications for corresponding matches initialized by scale invariant feature transform. The candidate outliers are selected by comparing the disparity of accumulated classifications among all matches, instead of conventional methods which only rely on local geometrical relations. Various image sets have been considered in this paper for the evaluation of the proposed approach, including aerial images with simulated affine deformations, remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal), and map images with inconsistent annotations. Experimental results demonstrate the superior performance of the proposed method over the existing approaches for the whole data set.

Description: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Description: The problem of blind image recovery using multiple blurry images of the same scene is addressed in this paper. To perform blind deconvolution, which is also called blind image recovery, the blur kernel and image are represented by groups of sparse domains to exploit the local and nonlocal information such that a novel joint deblurring approach is conceived. In the proposed approach, the group sparse regularization on both the blur kernel and image is provided, where the sparse solution is promoted by $ell _{1}$ -norm. In addition, the reweighted data fidelity is developed to further improve the recovery performance, where the weight is determined by the estimation error. Moreover, to reduce the undesirable noise effects in group sparse representation, distance measures are studied in the block matching process to find similar patches. In such a joint deblurring approach, a more sophisticated two-step interactive process is needed in which each step is solved by means of the well-known split Bregman iteration algorithm, which is generally used to efficiently solve the proposed joint deblurring problem. Finally, numerical studies, including synthetic and real images, demonstrate that the performance of this joint estimation algorithm is superior to the previous state-of-the-art algorithms in terms of both objective and subjective evaluation standards. The recovery results of real captured images using unmanned aerial vehicles are also provided to further validate the effectiveness of the proposed method.

Description: We tackle the challenge of constructing 64 pixels for each individual pixel of a thumbnail face image. We show that such an aggressive super-resolution objective can be attained by taking advantage of the global context and making the best use of the prior information portrayed by the image class. Our input image is so small (e.g., $16times 16$ pixels) that it can be considered as a patch of itself. Thus, conventional patch-matching-based super-resolution solutions are unsuitable. In order to enhance the resolution while enforcing the global context, we incorporate a pixel-wise appearance similarity objective into a deconvolutional neural network, which allows efficient learning of mappings between low-resolution input images and their high-resolution counterparts in the training data set. Furthermore, the deconvolutional network blends the learned high-resolution constituent parts in an authentic manner, where the face structure is naturally imposed and the global context is preserved. To account for the possible artifacts in upsampled feature maps, we employ a sub-network composed of additional convolutional layers. During training, we use roughly aligned images (only eye locations), yet demonstrate that our network has the capacity to super-resolve face images regardless of pose and facial expression variations. This significantly reduces the requirement of precisely face alignments in the data set. Owing to the network topology we apply, our method is robust to translational misalignments. In addition, our method is able to upsample rotational unaligned faces with data augmentation. Our extensive experimental analysis manifests that our method achieves more appealing and superior results than the state of the art.

Description: A physics-based compact model of the parasitic bipolar current induced by an energetic particle is presented for single-event transients in FinFETs. The terminal charges are modeled to predict the body voltage of the FinFET in the transient correctly. The models are implemented using Verilog-A and are verified through 3-D technology computer-aided design (TCAD) simulations. The results of the modeling show good agreement with the TCAD data, for both structural variations and energy variations of the energetic particles.

Description: Safe, stable, and efficient operation of nuclear reactors is very necessary for the development of nuclear power industry. During the course of its operation, a nuclear reactor is subjected to many intentional or unintentional perturbations which tend to change the rector power from its steady operating value. Power-level regulation is a significant technique for guaranteeing both operation stability and efficiency of nuclear reactors. Therefore, thorough stability analysis of power control loop is necessary to demonstrate its ability to withstand the reactivity perturbations. In this paper, the stability of the total power control loop of a large pressurised heavy water reactor is investigated in discrete-time domain by linearizing the system around its equilibrium points and identifying eigenvalues of the closed-loop system. The dynamics of the system vary widely depending on factors such as operating power levels, core–fuelling states and cycle time of reactor regulating system. Hence, the input to the stability analysis are these system parameters and the output is a stability characterization in terms of the stability regions shown in $G_{P}$ (total power control gain)– $G_{H}$ (level tilt gain) plane. The results quantify the influence of these system parameters on the stability of the total power control loop.

Description: A parity space approach to monitoring and fault detection and identification of systems in nuclear power plants (NPPs) can be beneficial. However, if the number of fault classes exceeds the total independent residual signatures, the parity space method needs to be further enhanced to achieve the optimal fault classification. This situation happens frequently in NPP applications, where the safety and reliability are paramount. A possible enhancement proposed in this paper is to combine Fisher discriminant analysis with the parity space method to maximize the scatter among different fault classes, while minimizing the scatter within each class. Under identical conditions, the proposed technique can achieve optimal separation among different fault classes. Design, real-time implementation, and experimental evaluation of the proposed method are detailed in this paper. The implemented system has been validated on the Nuclear Power Control Test Facility to demonstrate the feasibility. The test results have revealed many salient features of the proposed method with potential applications in NPPs.

Description: Optical absorption, luminescence spectra, scintillation decay curves, and scintillation light yield were measured in a series of undoped yttrium–aluminum garnet (YAG) crystals of different origins. This paper reveals a correlation between luminescent properties and scintillation efficiency of undoped YAG crystals. All the samples were separated into three groups, Type I, Type II, and Type III, based on their optical properties and scintillation efficiency. The intrinsic ultraviolet luminescence band peaking around 300 nm is found in the samples with high scintillation yield (Type I). Luminescence intensity of this band depends on the crystal purity and on the stoichiometry of the garnets. The UV emission may be related to e–h recombination in vicinity of vacancies. The garnets of Type II contain more impurities which quench the 300-nm emission. The samples of Type III do not exhibit any significant radioluminescence. Light yield of the finest undoped YAG samples in the series, Type I, is similar to the yield of Ce-doped YAG. This Type of crystals can potentially be used in scintillation detectors.

Description: A radiation-tolerant digital multiphase current-mode hysteretic point-of-load regulator fabricated on a commercial 180-nm CMOS process is presented. Experiments and simulations are used to demonstrate its single-event immunity and its total-dose tolerance over 100 krad(Si). Key electrical performance parameters are: 5-V input, 0.8–3.3 V output, 10-A load current, 93% peak efficiency, four-phase hysteretic quasi-current-mode buck converter with ±1.5% frequency synchronization, ±3.6% current sharing error, and 1% ripple.

Description: Two radiation hardening processes for shallow trench isolation (STI), i.e., Si + implantation and STI oxide nitridation are investigated, including the impact on nominal electrical characteristics and radiation hardness. The total ionizing dose effects of the nMOS devices are proved to be sensitive to the STI radiation hardening process conditions. There are optimum process conditions to achieve the best effectiveness of radiation hardening. Then, a 130-nm radiation-hardened PDSOI technology has been developed. The radiation hardness is verified by static random access memories with small storage capacities.

Description: In this paper, a compendium of InGaAs photodiode irradiation test results is presented. These photodiodes were irradiated either with $gamma $ -rays, protons, neutrons, electrons, pions, alpha particles, or carbon ions of various energies. The displacement damage dose formalism was found to be effective in describing the radiation-induced dark current increase of any of the studied InGaAs photodiodes. The exploitation of capacitance-bias voltage and current-bias voltage measurements also allows us to deduce a damage factor that can be used to assess the radiation-induced dark current in a great number of radiation environments.

Description: An in situ detection method for beta-ray distribution was developed. The method uses the gamma-ray sensitivity difference between two scintillators: a cadmium–tungstate (CdWO 4 ) scintillator and a plastic scintillator. The gamma-ray sensitivity ratios for several gamma-ray sources such as 137 Cs, 60 Co, 22 Na, and 54 Mn were experimentally characterized. The sensitivity ratio for 137 Cs between the CdWO 4 and plastic scintillators was 8 ± 0.32, while the sensitivity ratio for beta rays from 90 Sr was 1.03 ± 0.006. The beta-ray contributions to the total counts can be evaluated using this characteristic. A combined beta-ray and gamma-ray source was prepared, and its beta-ray distribution was successfully obtained using the proposed method.

Description: Environmentally friendly 99.998%-pure indium iodide (InI), one candidate materials for the room-temperature operating radiation detector, was purified more than 250 times using the zone-refining method to reduce the impurities. Segregation coefficient of major positive and negative impurities of the purified InI ingot was analyzed using time-of-flight secondary ion mass spectroscopy. Electrical and spectroscopic properties of the purified Pd/InI/Pd detector were also determined. Planar Pd/InI/Pd detector showed the 59.5-keV gamma peak of Am-241 clearly. However, low-energy gamma peaks were buried in the noise. Mechanical or electrical degradation under an ambient condition was not observed for six months. Electrical resistivity and electron mobility-lifetime product of the multiple-refined InI were $4times 10^{11},,Omega cdot textrm {cm}$ and $1.3times 10^{-3},,textrm {cm}^{2}/textrm {V}$ , respectively.

Description: Optically read out time projection chambers (TPCs) based on gaseous electron multipliers (GEMs) combine 3-D event reconstruction capabilities with high spatial resolution and charge amplification factors. The approach of reconstructing particle tracks from 2-D projections obtained with imaging sensors and depth information from photomultiplier tubes is limited to specific cases such as straight particle trajectories. A combination of optical and electronic readout realized by a semitransparent anode placed between a triple-GEM stack and a camera in an optically read out TPC has been realized and used to reconstruct more complex particle tracks. High spatial resolution 2-D projections combined with a low number of charge readout channels enable accurate 3-D event topology reconstruction. Straight alpha tracks as well as more complex cosmic events have been reconstructed with the presented readout concept. Relative depth information from electronically read out charge signals has been combined with drift time information between primary and secondary scintillation pulses to absolute alpha track reconstructions.

Description: The mass thickness and atomic number of materials shielding radioactive sources emitting multiple resolvable gamma-ray energies can be characterized by measuring the attenuation and Compton scatter of emitted gamma rays in recorded spectra against estimated values for a suite of materials and thicknesses. Compton imaging using a maximum-likelihood expectation maximization-based reconstruction can be used to separate angular spectra allowing simultaneous characterization of multiple shielded sources. Using the described algorithm on experimental 133 Ba data, we demonstrate estimation of mass thickness and atomic number for iron, tin, and lead shields with another bare source in the field of view with average standard error of 0.6 g/cm 2 and 1.5, respectively, while an aluminum shield is reconstructed with ambiguous atomic number but correct thickness.

Description: Radiation measurements and monitoring are very important for particle physics accelerators, hadron therapy institutes, and nuclear facilities. An Application Specific Integrated Circuit (ASIC) able to digitize current generated from ionization chambers was designed and characterized in order to be used as the front-end of the new radiation monitoring system at CERN. The design of the Utopia 2 ASIC was motivated by the need to measure input currents as low as 2 fA and over a wide dynamic range of 9 decades. This paper presents the challenges, the design procedure, the architecture, and the measurement results of the front-end that was fabricated in AMS 0.35- $mu text{m}$ technology. The main limitation was related to the leakage currents that are injected into the input of the ASIC from various sources and are added to the signal of the detector. By active leakage current compensation, the ASIC can measure current down to 1 fA, and by introducing a multiple range architecture, the ASIC can digitize current up to $5~mu text{A}$ .

Description: AREVA Mines and the Nuclear Measurement Laboratory of CEA Cadarache are collaborating to improve the sensitivity and precision of uranium concentration measurement by means of gamma-ray logging. The determination of uranium concentration in boreholes is performed with the Natural Gamma Ray Sonde (NGRS) based on a NaI(Tl) scintillation detector. The total gamma count rate is converted into uranium concentration using a calibration coefficient measured in concrete blocks with known uranium concentration in the AREVA Mines calibration facility located in Bessines, France. Until now, to take into account gamma attenuation in a variety of boreholes diameters, tubing materials, diameters and thicknesses, filling fluid densities, and compositions, a semiempirical formula was used to correct the calibration coefficient measured in Bessines facility. In this paper, we propose to use Monte Carlo simulations to improve gamma attenuation corrections. To this purpose, the NGRS probe and the calibration measurements in the standard concrete blocks have been modeled with Monte Carlo N-Particles (MCNP) computer code. The calibration coefficient determined by simulation 5.3 $text{s}^{-1}cdot text {ppm}_{U}^{-1}$ with 10% accuracy is in good agreement with the one measured in Bessines (and for which no uncertainty was provided), 5.2 $text{s}^{-1}cdot text {ppm}_{U}^{-1}$ . The calculations indicate that the concrete blocks used for measuring the calibration coefficients measured in Bessines are underestimated by about 10%. Based on the validated MCNP model, several parametric studies have been performed. For instance, the rock density and chemical composition proved to have a limited impact on the calibration coefficient. However, gamma self-absorption in uranium leads to a nonlinear relationship - etween count rate and uranium concentration beyond approximately 1% of uranium weight fraction, the underestimation of the uranium content reaching more than a factor 2.5 for a 50% uranium weight fraction. Parametric studies have also been performed with different tubing materials, diameters, and thicknesses, as well as different borehole filling fluids representative of real measurement conditions, in view to validate gamma attenuation corrections based on the semiempirical formula. In addition, a multilinear analysis approach has been tested to further improve accuracy on uranium concentration determination, leading to only a few percent uncertainties on a large range of configurations.

Description: Thallium-bromide (TlBr) is currently under investigation for use as an alternative room-temperature semiconductor gamma-ray spectrometer. Performance of better than 1% full-width at half maximum at 662 keV can be achieved when TlBr detectors are cooled to −20 °C. The theoretical limit of energy resolution is determined by the ionization energy in semiconductor detectors, and accurately measuring it is important for determining the best possible performance. One method to determine the ionization energy of semiconductor detectors compares pulse heights obtained from the semiconductor to pulse heights from a silicon detector. Due to their higher trapping, the charge collection efficiency (CCE) of TlBr is significantly lower than it is in silicon, therefore a correction to the ionization energy must be made. In this paper, we present the theory and measurement for accurately determining the CCE using the Shockley-Ramo theorem and apply it to measurement of ionization energy in pixelated TlBr detectors. The ionization energy of two TlBr detectors is measured and found to be 4.83(8) and 5.49(10) eV for the two samples at room temperature and −20 °C, respectively.

Description: Cone-beam computed tomography (CBCT) plays a critical role in both medical imaging and industrial nondestructive testing with high efficiency and precision. However, scattering poses a significant limitation to image quality. In order to get the complete scattering information when the object is scanned, a simple scattering correction method based on compressed sensing (CS) is proposed, which is based on the assumption that the object scattering (OS) is eliminated by the lead collimator. In the process, the additional scattering from shadow area is first estimated, and then the OS is obtained by using the information in the irradiated area (with the strips pattern) extracted from the primary projection (without the strips pattern). Then, the estimated scatterings are subtracted from each primary projection data to yield an estimate for CBCT reconstruction, and the scattering-corrected CBCT volume is reconstructed by using an alternating direction method of multipliers algorithm. Finally, experimental studies are performed to evaluate the performance of the proposed schemes which has reduced the scattering artifacts. For an aluminum sample, the average gradient (AG) and contrast-to-noise ratio (CNR) were increased by 60% and 80%; for titanium, the AG and signal-to-noise ratio (SNR) were increased by 60% and 20%; and for iron, the AG, CNR, and SNR were increased by 30%–50%. Moreover, this paper studies long-span information sensing by replacing the interpolation algorithm with CS theory; it can provide a certain theoretical guidance for the sparse data recovery and mining.

Description: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Description: A continuous wave Nd-based laser with simultaneous narrow spectral 1052, 1071, and 1083 nm output was achieved by using temperature-controlled volume Bragg gratings to balance the gain competition between each wavelength and stabilize the laser output. Each output wavelength was characterized thoroughly and showed good stability in both frequency and power.

Description: For the first time, we propose and experimentally verify a novel low-complexity orthogonal least square (OLS) based radial basis function (RBF) neural network (NN) nonlinear equalizer for high-speed coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. Its ability to compensate nonlinear impairments as well as linear impairments is comprehensively evaluated and compared with the linear equalizer. The impact of training overhead on system performance is also investigated. Results show that in a single-channel 40-Gb/s 16-quadrature amplitude modulation CO-OFDM system, with the training overhead of 4%, the maximum transmission distance is extended to 800 km at Q threshold of 8.7 dB, and RBF-NN outperforms the linear equalizer by 2.8 and 5.6 dB Q -factors after 800 and 600 km transmissions, respectively.

Description: This paper presents a low-cost non-invasive millimeter-wave surface-height measurement sensor of dielectric and polymer films on glass and quartz substrates. The surface-height profiler utilizes near-field resonance measurement technique operating at 96 GHz implemented by using a single complementary split-ring resonator (CSRR) integrated with a tailor-made WR10 rectangular waveguide. By placing a glass or quartz substrate uniformly coated with SU-8 photoresist on top of the CSRR, the thickness of the SU-8 polymer can be extracted based on the reflected and transmitted electromagnetic-wave energy interacting at the electrical boundary between the substrate and polymer film. Uniform single layers of SU-8 polymer with thicknesses from 3 to $13~mu text{m}$ coated on top of the glass substrate are measured and characterized. The extracted polymer-film thicknesses from the sensor in this paper show an agreement of higher than 95% as compared with the commercial surface profiler instrument, while offering various advantages, such as non-invasion, ease of measurement setup, low-cost, and miniaturization.

Description: This paper proposes a method of using a microwave multiple-input multiple-output (MIMO) array to recognize different people; it allows smart home applications to dispense with contact and wearable devices. This method captures and processes the time-variant channel generated by a human body present within the field of an MIMO antenna. The time-variant channel is measured, and the spatial and temporal correlation is used for identification by comparing the captured data to reference data. The proposed method identifies the subject as the registrant with maximum correlation. Experiments involving eight known subjects and four imposters demonstrate that the proposed method offers accurate identification with no false positives. The results show that an $8times 8$ array can achieve the equal error rate of 0% when the number of identification channels is five and above.

Description: A novel magnetic field sensing system based on a ferrofluid-coated multimode interferometer (MI) integrated into a fiber-loop ring-down cavity is proposed and demonstrated. The MI is composed of a piece of no-core fiber (NCF) sandwiched by two sections of single-mode fiber. Through appropriately designing the length of the NCF, the ferrofluid-coated MI not only works as the sensing unit but also can help to filter the unwanted amplified spontaneous emission arising from the erbium-doped fiber amplifier in the fiber loop. The proper length of NCF can be determined by simulations. Corresponding experiments are conducted, and the experimental results demonstrate the feasibility of the sensing system. A sensitivity of $- 0.83,,mu text{s}$ /Oe in the linear region is achieved in experiments.

Description: A Doppler shift estimation system with relatively high dynamic range is conceived and demonstrated. The scheme is based on dual mixing the modulated radar echo and frequency to voltage mapping. Hence, it needs no radio frequency auxiliary part. This makes the system capable of becoming integrated into any photonic radar, which would positively affect the cost and size of such system. Since the mixing procedure is all-optical, the system is able to identify the Doppler frequency of a radar echo with carrier frequency as high as 40 GHz independent of its amplitude. The system dynamic range can be as high as 42 dB at 10-GHz radar carrier with an acceptable error of 5%. This dynamic range was achieved using lock-in amplification technique.

Description: Resistive random-access memory (RRAM) technology has been gaining importance due to scalability, low power, non-volatility, and the ability to perform in-memory computing. The RRAM sensing applications have also emerged to enable single RRAM technology platforms which include sensing, data storage, and computing. This paper reports on sol-gel drop coated low-power $mu $ -thick Ag/TiO 2 /Cu memristor, named MemSens, developed for radiation sensing. MemSens exhibits a bipolar memristive switching behavior within a small voltage window, ranging up to +0.7 V for the turn-ON, and down to −0.2 V for the turn-OFF. Under these operating conditions, MemSens has 67% less switching voltage, 20% drop in ON switching current, 75% reduced active area and > 3x improved device endurance, compared to the best characteristics reported in the literature for $mu $ -thick memristors. The device is tested under direct exposure to ionizing Cs-137 662keV $gamma $ -rays, during which a significant increase in the electrical conductivity of the device is observed. MemSens circuit is proposed to allow a relatively real time and cost-effective radiation detection. This provides a first insight to the advancement of reliable memristors that could potentially be deployed in future low-power radiation sensing technologies for medical, personal protection, and other field applications.

Description: Variable-flux biaxial vibration energy harvesting (VBH) is a novel architecture for electromagnetic vibrations energy harvesting. In this paper, we present VBH prototypes for low-frequency (<10 Hz) and low-amplitude vibrations energy harvesting. The transduction mechanism uses stationary magnetic circuit, a coil, and a moving ferromagnetic ball. The ferromagnetic ball disrupts the magnetic field and induces a voltage in the coil. We analyzed the performance of the VBH experimentally, and the results exhibited the efficient operation of the system in harvesting energy at center frequencies as low as 6.5 Hz with harvesting bandwidth as wide as 5.8 Hz and an output power of $154~mu text{W}$ .

Description: A simple contactless optical fiber interferometric sensor for real-time measurement of low-level of water content in ethanol is proposed and demonstrated. The sensing head consists of a multi-cavity Fabry–Pérot interferometer formed with a fiber tip in close contact with a quartz spectrophotometer cell. Ethanol–water solutions introduced into the cell cavity produce a change in the optical path length of one cavity of the interferometer. The increment of water content induces refractive index changes, but more important the absorption of the light increases. Both effects produce a significant change in the interference spectra that can be easily monitored and quantified in the Fourier domain. We demonstrate that our technique can detect water content (0%–60%) in ethanol with a detection limit as low as 0.5%.

Description: Traditional WiFi fingerprint positioning normally uses a time consuming and labor intensive process called site survey. This paper proposes a crowdsourcing and multisource fusion-based fingerprint sensing (CMFS) to replace the traditional site survey approach. In CMFS, motion sensors are used to construct the radio map by volunteers and pedestrian dead reckoning based on motion sensors equipped in smartphones is used for positioning. In positioning phase, an extended Kalman filter (EKF)-based multisource fusion algorithm is further developed to tackle the nonlinear fusion so that both positioning accuracy and robustness can be improved. Both experimental and simulation results verify that the performance of proposed schemes is comparable to the traditional fingerprint approaches. Further the EKF-based fusion scheme is found to improve smoothness and stability of CMFS greatly.