This paper presents a new dual-inductor hybrid boost converter (DI-HBOC) with two inductors located at the output. This structure allows continuous current delivered to the load, thus, reducing the output filtering capacitor size and the output voltage ripple. By relocating the inductor at the output, which is the lower current path, the conduction loss on the inductor can be significantly reduced. The right half plane zero (RHPZ) in the control-to-output transfer function can also be eliminated; therefore, a simple pulse-width modulation (PWM) voltage-mode controller can be used for the proposed DI-HBOC while still achieving high closed-loop bandwidth and fast transient response. The distinct features of the proposed converter are analytically demonstrated. A 12-to 24 V DI-HBOC and a conventional BOC (CBOC) using low-RON GaN switches with PWM voltage-mode controller are also implemented in PSIM for verification and comparison. The simulated peak power efficiency is 97.4 % that is 1.17 % higher than the CBOC. At 3 A load current, the power efficiency is improved by 9.7 % and the output ripple is only 17.5 mV, 6x lower than in CBOC.

This paper presents the capacity analysis of unmanned aerial vehicles (UAVs) communications supported by flying intelligent reflecting surfaces (IRSs). In the considered system, some of the UAVs are equipped with an IRS panel that applies certain phase-shifts to the incident waves before being reflected to the receiving UAV. In contrast to existing work, this letter considers the effect of imperfect phase knowledge on the system capacity, where the phase error is modeled as a von Mises random variable with parameter k. Analytical results, corroborated by Monte Carlo simulations, show that the achievable capacity is dependent on the phase error, however, the capacity loss becomes negligible at high signal-to-noise ratio (SNR) and when k>6.

We consider the capacity optimization of submarine links when including a realistic model of the gain-flattened constant-pump erbium doped fiber amplifiers (EDFA). While Perin et al. [1] numerically attacked this optimization for Constant-Gain (CG) amplified links, we extend the analysis also to more realistic submarine constant power-spectral-density (CPSD) links. As in [1], we concentrate on a single spatial mode of a spatial division multiplexed (SDM) link at low EDFA pump power Pp, and thus consider only the impairments of amplified spontaneous emission noise. Here we adopt a novel semi-analytical approach which consists of fixing the inversion x1 of the first EDFA (the state-variable of the link) and analytically finding capacity C(x1) by searching over the x1-feasible input wavelength division multiplexed (WDM) PSD distributions. Then the optimum inversion x1 that maximizes C(x1) is numerically obtained. This approach enables us to get both approximate (for CG links) and exact (for CPSD links) capacity-maximizing WDM input distributions, which vary inversely with the EDFA gain profile. For CG links the optimal WDM allocation is called the gain-shaped water-filling. Other practical allocations are analyzed, such as the signal to noise ratio equalizing allocation (CSNR), and the constant input power (CIP) allocation which uses a flat WDM distribution. We find that, for typical submarine span attenuations around 10dB and when the link works at the optimal inversion x1, CIP and CSNR achieve essentially the same capacity as the optimal allocation. At sufficiently large pump Pp (>= 30 mW) the optimal inversion x1 is such that the EDFA gain at 1538nm equals the span attenuation, for EDFA emission and absorption as in [1]. When span attenuations increase to 20dB, then we start seeing an advantage of the optimal allocation. Another key finding is that optimized CG and CPSD links behave roughly the same, with a slightly superior capacity for CPSD.

An equivalent-circuit topology for two-port lossy non-symmetric reciprocal networks is proposed. The circuit topology is based on the eigenstate decomposition. The proposed circuit consists of two immittances and two transformers with a single complex turns ratio, all obtained from the eigenvalues and eigenvectors of the admittance or impedance matrix of the network in a straightforward way. The interconnection of the circuit elements is simple and compact, and the real parts of its immittances are always positive. To verify its behavior, the equivalent circuit of two different leaky-wave antenna unit cells is extracted: a series-fed coupled patch radiating element, and the complementary strip-slot. Simulated and measured data are provided for the study cases, highlighting the satisfactory behavior of the equivalent circuit over a very broad bandwidth. A simple asymmetry model is also proposed that can be used advantageously in the analysis and design of non-symmetric two-ports, like unit cells of leaky-wave antennas.

The role of the source geometry in the the radiation of focusing beams by conformal antennas is examined by the comparison of their directivity functions at different maximum directions. An inverse source problem approach is adopted, where solutions stable with respect to data uncertainties are to be found by relying on the analysis of the pertinent operator by the Singular Values Decomposition. This general framework allows to connect the mean value of the mazimum directivity function to the Number of Degrees of Freedom of the conformal source, which depends only on its electrical length. For each source geometry the focusing far field ensuring the maximum directivity for every pointing direction and the corresponding source current are obtained. The comparison with the ones achieved by the usual phase compensation technique of the source current reveals their optimal behavior. The usefulness of the approach as a tool in antenna synthesis is shown by comparing different geometries for those applications where identical beams are required to be rdiated for the coverage of a larga angular domain.

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM) New journal paper

This paper describes an innovative e-learning project which is the development of a digital workbook that helps teaching handwriting at school. In this work, we propose a new qualitative and quantitative analysis process of cursive handwriting. This process detects automatically mistakes, gives a real-time feedback and helps teachers evaluate children’s writing skills. The main aim of this digital workbook is to help children learn how to write correctly. The proposed process is composed of five main criteria: shape,direction, stroke order, position respect to the reference lines and kinematics of the trace. It analyzes the handwriting quality and gives automatically feedback based on the Beta-Elliptic Model using similarity detection (SD) and dissimilarity distance (DD) measure. Our work apprehends dynamic and visual representation of the acquired traces and selects efficient features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbols drawing. It demonstrates that beta-elliptic is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Our application offers two interactive interfaces respectively dedicated to learners, and experts or teachers who can adapt it easily to the specificity of each child. The validation of the proposed system is done on a database collected in Tunisia primary schools with 400 children. Experimental results show that the efficiency and robustness of our suggested framework that do help teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

Presenting a novel set generalizing the usual base representation of floating point numbers. This “base-prime” set was identified in patented research on nonuniform data sampling and self-stabilizing computer arithmetic.

We proposed RTP, a composite neural network model that captures knowledge from remote transportation pollution events (RTPEs) to improve the local PM2.5 prediction. To the best of our knowledge, this is the first deep learning work to include knowledge from remote pollutants for PM2.5 prediction. RTP consists of two neural network components: a pre-trained base model and STRI model. The base model captures knowledge from local factors that influence PM2.5 concentrations and STRI captures knowledge from RTPEs by learning spatial-temporal characteristics of Satellite base AOD data and weather features from remote areas. In addition, given the size of the STRI model, to facilitate training and improve results we divide the full STRI model into two components: STRI\_fe, which is used to extract spatial-temporal features from remote areas, and STRI\_p, which predicts local PM2.5 concentrations using both remote and local features. The prediction results from STRI\_p show that the prediction error is reduced when remote features are added to the model, demonstrating that the STRI model indeed captures knowledge from RTPEs. To characterize the occurrence of RTPEs in northern Taiwan, we also developed an algorithm to classify PM2.5 concentrations attributable to RTPEs. We use the STRI model for the prediction of two EPA stations located at the northern tip of Taiwan and apply the classification algorithm to the results. This yields improvements in accuracy when remote features are added to the model, which demonstrates the impact of RTPEs at the stations.

Web applications are exposed to many threats and, despite the best defensive efforts, are often successfully attacked. Reverting the effects of an attack on the state of such an application requires a profound knowledge about the application, to understand what data did the attack corrupt. Furthermore, it requires knowing what steps are needed to revert the effects without modifying legitimate data created by legitimate users. Existing intrusion recovery systems are capable of reverting the effects of the attack but they require modifications to the source code of the application, which may be unpractical. We present Sanare, a pluggable intrusion recovery system designed for web applications that use different data storage systems to keep their state. Sanare does not require any modification to the source code of the application or the web server. Instead, it uses Matchare, a new deep learning scheme we introduce to learn the matches between the HTTP requests and the database statements, file system operations and web services requests that the HTTP requests caused. We evaluated Sanare with three open source web applications: WordPress, GitLab and ownCloud. In our experiments Matchare achieved precision and recall higher than 97.5%.

In this paper, we address the problem of computing the dimension of data space in phase retrieval problem. Starting from the quadratic formulation of the phase retrieval, the analysis is performed in two steps. First, we exploit the lifting technique to obtain a linear representation of the data. Later, we evaluate the dimension of data space by computing analytically the number of relevant singular values of the linear operator that represents the data. The study is done with reference to a 2D scalar geometry consisting of an electric current strip whose square amplitude of the electric radiated field is observed on a two-dimensional extended domain in Fresnel zone.

Two-coil wireless power transfer (WPT) systems are composed of two circuits tuned at the same resonance frequency, one containing the source, and other containing the load, both connected to each other by the mutual inductance. The power delivered to the load circuit (Po) divided by the total power supplied by the source (PT) and by the maximum ideal amount of power which can be delivered to the load circuit are usual figures of merit known as efficiency (n) and power transfer capability (P*), respectively. Additionally, it can be defined a third figure of merit (I*) as the power dissipated at the source circuit divided by PT. It has been recently demonstrated that n and P* are related to I* . In this paper, it is presented a simple method to monitor I*, allowing consequently the determination of n and/or P* without any direct measurement at the load circuit. The qualities and limitations of the proposed method are discussed in details. Practical results are included to verify the proposal.

Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

Blockchain technology has the potential to revolutionize industries by offering decentralized, transparent, data provenance, auditable, reliable, and trustworthy features. However, cross-chain interoperability is one of the crucial challenges preventing widespread adoption of blockchain applications. Cross-chain interoperability represents the ability for one blockchain network to interact and share data with another blockchain network. Contemporary cross-chain interoperability solutions are centralized and require re-engineering of the core blockchain stack to enable inter-communication and data sharing among heterogeneous blockchain networks. In this paper, we propose an application-based cross-chain interoperability solution named appXchain which allows blockchain networks of any architecture type and industrial focus to inter-communicate, share data, and make requests. Our solution utilizes the decentralized applications as a distributed translation layer that is capable of communicating and understanding multiple blockchain networks, thereby delegating requests and parameters among them. The architecture uses incentivized verifier nodes that maintain the integrity of shared data facilitating them to be readable by the entities of their network. We define and describe the roles and requirements of major entities of inter-operating blockchain networks in the context of healthcare. We present a detailed explanation of the sequence of interactions needed to share an Electronic Medical Record (EMR) document from one blockchain network to another along with the required algorithms. We implement the appXchain solution with Ethereum-based smart contracts for two hospitals and also present its cost and security analysis. We have made our smart contracts code and testing scripts publicly available.

Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.

Inspired by compressive sensing principles, we propose novel error control coding techniques for communication systems. The information bits are encoded in the support and the non-zero entries of a sparse signal. By selecting a dictionary matrix with suitable dimensions, the codeword for transmission is obtained by multiplying the dictionary matrix with the sparse signal. Specifically, the codewords are obtained from the sparse linear combinations of the columns of the dictionary matrix. At the decoder, we employ variations of greedy sparse signal recovery algorithms. Using Gold code sequences and mutually unbiased bases from quantum information theory as dictionary matrices, we study the block error rate (BLER) performance of the proposed scheme in the AWGN channel. Our results show that the proposed scheme has a comparable and competitive performance with respect to the several widely used linear codes, for very small to moderate block lengths. In addition, our coding scheme extends straightforwardly to multi-user scenarios such as multiple access channel, broadcast channel, and interference channel. In these multi-user channels, if the users are grouped such that they have similar channel gains and noise levels, the overall BLER performance of our proposed scheme will coincide with an equivalent single-user scenario.

This article demonstrates how experiential learning could be used to develop argumentative essay writing skills in STEM students. Written feedback, when delivered in a timely manner, is an effective way of advancing students' understanding of the writing process. Unfortunately, large class sizes and the limited backgrounds of instructors do not always make formative feedback possible. STEM students are especially disadvantaged since approaches to teaching written communication tend to differ from the trial-and-error strategies compatible with many STEM areas. An experiential learning approach to writing instruction can have a positive impact on developing writing skills in STEM learners. Implementing algorithms for providing STEM students with immediate, dependable, formative feedback is expected to improve their performance in writing. This paper discusses an experiential learning project for teaching argumentative writing was delivered to computer science and engineering freshmen. Also discussed are automated analysis of content and argumentation in the essays, using NLP methods.