Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Overcomplete and Sparsifying Transform With Approximate and Exact Closed Form Solutions

This paper addresses the learning problem for data-adaptive transform that provides sparse representation in a space with dimensions larger than (or equal to)the dimensions of the original space. We present an iterative, alternating algorithm that has two steps: (i)transform update and (ii)sparse coding. In the transform update step, we focus on novel problem formulation based on a lower bound of the objective that addresses a trade-off between (a) how much are aligned the gradients of the approximative objective and the original objective, and (b)how much the lower bound is close to the original objective. This allows us not only to propose approximate closed form solution but also gives the possibility to find an update that can lead to accelerated local convergence and enables us to estimate an update that can lead to a satisfactory solution under a small amount of data. Since in the transform update, the approximate closed form solution preserves the gradient and in the sparse coding step, we use exact closed form solution, the resulting algorithm is convergent. On the practical side, we evaluate on image denoising application and demonstrate promising denoising performance together with advantages in training data requirements, accelerated local convergence and the resulting computational complexity.
A Matching Theory Framework for Tasks Offloading in Fog Computing for IoT Systems

Fog Computing (FC) is an emerging paradigm that extends cloud computing toward the edge of the network. In particular, FC refers to a distributed computing infrastructure confined on a limited geographical area within which some Internet of Things applications/services run directly at the network edge on smart devices having computing, storage, and network connectivity, named fog nodes (FNs), with the goal of improving efficiency and reducing the amount of data that needs to be sent to the Cloud for massive data processing, analysis, and storage. This paper proposes an efficient strategy to offload computationally intensive tasks from end-user devices to FNs. The computation offload problem is formulated here as a matching game with externalities, with the aim of minimizing the worst case service time by taking into account both computational and communications costs. In particular, this paper proposes a strategy based on the deferred acceptance algorithm to achieve the efficient allocation in a distributed mode and ensuring stability over the matching outcome. The performance of the proposed method is evaluated by resorting to computer simulations in terms of worst total completion time, mean waiting, and mean total completion time per task. Moreover, with the aim of highlighting the advantages of the proposed method, performance comparisons with different alternatives are also presented and critically discussed. Finally, a fairness analysis of the proposed allocation strategy is also provided on the basis of the evaluation of the Jain’s index.
An Integrated Sensor IBC Implant Transceiver

This paper presents the design and implementation of an integrated sensor transmitter and receiver for a galvanically coupled intra-body communication (gcIBC). The transceiver was designed with two main objectives, first to integrate existing capacitive sensors into a single sensor-transceiver platform and to assess channel noise. A common variable capacitance range of 0–50 pF (as used in glucose sensors) is considered as a sensor message. A direct sequence spread spectrum (DSSS) scheme is used to provide noise robust and multiuser capability. Experimental assessment of bit error rate (BER) over communication distance is associated with the theoretical and simulated BER versus signal to noise ratio (SNR) to quantify noise as a function of communication distance.
Vector Gaussian CEO Problem Under Logarithmic Loss

In this paper, we study the vector Gaussian Chief Executive Officer (CEO) problem under logarithmic loss distortion measure. Specifically, $ K\geq 2$ agents observe independently corrupted Gaussian noisy versions of a remote vector Gaussian source, and communicate independently with a decoder or CEO over rate-constrained noise-free links. The CEO wants to reconstruct the remote source to within some prescribed distortion level where the incurred distortion is measured under the logarithmic loss penalty criterion. We find an explicit characterization of the rate-distortion region of this model. For the proof of this result, we obtain an outer bound on the region of the vector Gaussian CEO problem by means of a technique that relies on the de Bruijn identity and the properties of Fisher information. The approach is similar to Ekrem-Ulukus outer bounding technique for the vector Gaussian CEO problem under quadratic distortion measure, for which it was there found generally non-tight; but it is shown here to yield a complete characterization of the region for the case of logarithmic loss measure. Also, we show that Gaussian test channels with time-sharing exhaust the Berger-Tung inner bound, which is optimal. Furthermore, we also show that the established result under logarithmic loss provides an outer bound for a quadratic vector Gaussian CEO problem with determinant constraint, for which we characterize the optimal rate-distortion region.
Dynamic Gradient Play for NE Seeking with Disturbance Rejection

In this paper, we propose a control theoretic framework for game problems subject to external disturbances. We consider two cases: the classical setting with full information on the others' decisions, and the partial-decision information setting. The proposed agent dynamics has two components: a gradient-play component and a dynamic internal-model one, which is a reduced-order observer of the disturbance. In the case of partial-information, there is an additional component that drives agents to reach the consensus subspace, where all decision estimates are the same. In both cases, we prove that agents' dynamics converge to the Nash equilibrium, irrespective of the disturbance. Our proofs rely on input-to-state stability properties, under strong monotonicity of the pseudo-gradient and Lipschitz continuity of the extended pseudo-gradient.
Effect of mobility on RFID reader-to-reader interference

In an RFID network, the signal transmitted by a reader to query tags may interfere with the communication of other surrounding readers. As a consequence, the readers are not able to detect the weak reply from tags. The reader-to-reader interference affects readers located within a given distance, called interference range. This phenomenon can be estimated when deploying an RFID system, because the initial positions of the readers are known. It is also possible to completely avoid it by placing the readers far enough each other. However, if the readers are mobile, the distances among readers change and the probability of reader-to-reader interference dramatically increases. A theoretical analysis is conducted in this paper in order to estimate the probability of interference among static and mobile readers. The random waypoint model, one of the most common mobility models, is adopted to describe the movements of the readers. The theoretical results are validated with simulations.
SRAM Dosimeter for Characterizing the TRIUMF Proton and Neutron Beams

An array of 30 16-Mbit static random access memories manufactured using 65-nm technology has been designed to characterize the proton and neutron beams used for radiation testing at TRIUMF. Measurements include single-event upset cross sections, beam profiles, collimator design, and shielding effects. The sensitivity of this dosimeter allows for quick measurements of these parameters, which are of interest in radiation test measurements.
Quasi-Static and Time-Selective Channel Estimation for Block-Sparse Millimeter Wave Hybrid MIMO Systems: Sparse Bayesian Learning (SBL) Based Approaches

This paper develops schemes for block-sparse channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity inherent in such channels. Initially, a novel sparse Bayesian learning (SBL) based block-sparse channel estimation technique is developed for a mmWave hybrid MIMO system with multiple measurement vectors, which overcomes the shortcomings of the existing orthogonal matching pursuit-based framework. This is subsequently extended to a temporally correlated block-sparse mmWave MIMO channel. Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mmWave MIMO channel. Bayesian Cramér–Rao bounds are also derived for the proposed static and time-selective mmWave MIMO channel estimation schemes followed by precoder/combiner design employing the SBL-based imperfect channel estimates. Simulation results are presented to demonstrate the improved performance of the proposed SBL-based channel estimation techniques in comparison to the popular OMP-based scheme proposed recently.
Detection of Black Regions in the Forex Market By Analyzing High-Frequency Intraday Data

The Foreign Exchange (Forex) market is one of the most exciting, and fast-evolving financial markets in the world. The evolution of the prices of the Forex market depends on a variety of factors including unexpected news, rumors, scheduled news announcements, and micro-blogging posts. Hence accurate prediction of prices, as well as high-quality backtesting, is more challenging as it is greatly controlled by these anomalous time periods (also called black regions) made by user generated content in social media and news websites. Most of the existing literature related to anomaly detection in financial time series are supervised learning approaches. They rely on a training dataset and have lesser generalizing power. This research proposes an unsupervised pipeline based on ARIMA, GARCH and LOF techniques to detect black regions in the Forex Market. The proposed method demonstrates high accuracies compared to other benchmark methods.zh
Combining Metrics for Route Selection in SDWSN: Static and Dynamic Approaches Evaluation

The Software Defined Networking paradigm has been applied to Wireless Sensor Networks (WSN) aiming to provide flexibility, enable resource sharing and reuse, and improve management. Routing protocol plays an important role in energy consumption given that the radio is the most power hungry component. Previous works show that using link quality as a metric usually decreases latency while using energy might balance the network energy consumption, thus increasing the network lifetime. We here apply and evaluate energy-aware route selection to Software Defined WSNs through dynamic and static combination of link quality and energy metrics. Dynamic combination is achieved using Fuzzy logic methods. Simulation results show that using energy as a metric achieves the best network lifetime and good energy consumption. Route selection using dynamic combination at CENTROID SCENARIO as a metric shows the best results for packet delivery rate, end-to-end delay and distribution of the nodes energy consumption among the network, where neighbors within the same hop count from the sink or controller have uniform energy consumption, besides a good network lifetime.
Performance of Selective Decode-and-Forward SWIPT Network in Nakagami-M Fading Channel

In this paper, the bit error probability of cooperative selective decode-and-forward (SDF) relaying network with an energy harvesting (EH) relay is derived by a unified analysis for power-splitting (PS), time-switching (TS) and ideal EH protocols in Nakagami-m fading channels. The effects of EH parameters $\rho$ and $\alpha$ for PS and TS protocols, respectively, on the bit error rate (BER) performance are investigated and the BER results are obtained for the optimum values of $\alpha$ and $\rho$ with respect to signal-to-noise ratio. The non-EH cooperative SDF and EH dual-hop DF networks are considered for comparison purposes.
Performance Comparison of Multiobjective Evolutionary Algorithms on Problems with Partially Different Properties from Popular Test Suites

A Multiobjective Evolutionary Algorithm (MOEA) is one of the effective approaches for solving Multiobjective Optimization Problems (MOPs). The performance of MOEAs is evaluated mainly by scalable MOP test suites where the number of objectives can be arbitrarily specified. However, the number of scalable MOP test suites is quite limited and their properties are similar. Thus, there is a risk that the current research on MOEAs is specialized for some properties (i.e., a shape of feasible regions, a shape of the Pareto front, and a distance function) of existing scalable MOP test suites. In this paper, we focus on the above properties of two popular MOP test suites (i.e., DTLZ and WFG). Based on DTLZ and WFG, we create 12 MOPs which have partially different properties from those of DTLZ and WFG. Computational experiments show that the search performance of the state-of-the-art MOEAs strongly depends on three properties.
Multilayer Balanced-to-Unbalanced Power Divider With Wideband Transmission Characteristic and Common-Mode Suppression

This paper presents a wideband multilayer balanced-to-unbalanced (BTUB) power divider consisting of three metallic layers and two substrate layers with inherent common-mode (CM) suppression. With very simple structure, the working mechanism of the proposed power divider is investigated through even- and odd-mode equivalent circuit analysis, and the designed procedure is also summarized. For demonstration, an example of wideband BTUB power divider is implemented through standard printed circuit board process. The bandwidth of 63% of the fabricated BTUB power divider can be obtained with the best differential-mode transmission coefficient of −3.45 dB. The isolation bandwidth of approximately 63% can also be achieved. Good agreement between the simulated and measured results is observed, where the measured −15-dB CM suppression bandwidth is about 95%. Moreover, the performance is stable after adding the shielding cavity.
Generalized Weighted Sum Subtract Joint Detection for a Class of Multihead Multitrack Channels

Weighted sum subtract joint detector (WSSJD) was proposed by Fan et al. as an alternative way to implement maximum-likelihood (ML) detection for multihead multitrack (MHMT) systems. Different from the conventional ML detector, the trellis used in WSSJD is independent of the intertrack interference (ITI); therefore, WSSJD could efficiently adapt to the time-varying ITI environment. The channel transformation developed for WSSJD also naturally leads to a reduced-complexity implementation for the ML detector. In this paper, we generalize WSSJD to MHMT systems whose interference matrices satisfy the WSSJD property. The implementation details are given for a symmetric 3H2T system, as an example. Other interference matrices where WSSJD is applicable are also presented.
The Short-Term Impact of an Item-Based Loyalty Program

Given the prevalence of loyalty programs' implementation in service industries, in order to create a difference in the eyes of the customer from other competitors, examining new loyalty program designs become more and more important for most firms. Compare with Zhang and Breugelmans' research of the item-based loyalty program (IBLP), this research studies a more complicated IBLP design, in which customers can earn different extra points for purchases made on different items. The main purpose of this research is to examine the short-term impact of items with different points in this new IBLP design on different types of customers' purchase behavior. Using data from a Japanese grocery store chain, this study shows that those customers who were heavy customers at the beginning of the IBLP are more affected by this new IBLP design. Then, instead of higher-point items, a middle-level-point, 25-point items has the highest impact on customers' purchase behavior. These findings suggest this special tactic can enhance the value of firm's loyalty program, and help managers to further improve the effect of the IBLP by arranging more targeted items to different types of customers.
Too Noisy at the Bottom? —Random Telegraph Noise (RTN) in Advanced Logic Devices and Circuits

In this paper, the recent advances of our studies on RTN are presented from device, circuit, and EDA perspectives. RTN characteristics in FinFETs are investigated and compared with planar devices. The AC RTN effect is discussed for understanding RTN impacts in practical circuit applications. Then, a new and efficient circuit simulation platform for RTN is presented for the first time, which has been implemented in HSPICE using OMI/TMI. In addition, some open questions related to RTN are discussed with outlooks.
Integral Input-to-State Stability of Delay Systems Based on Lyapunov-Krasovskii Functionals with Point-Wise Dissipation Rate

We show that a Lyapunov-Krasovskii functional whose dissipation rate involves solely the current instantaneous value of the state norm is enough to guarantee integral input-to-state stability (iISS). This result generalizes existing sufficient conditions for iISS, where the dissipation rate involves the whole Lyapunov-Krasovskii functional itself, and simplifies their applicability. Moreover, it provides a more natural bridge with the classical condition for global asymptotic stability of input-free systems. The proof strategy we employ relies on a novel characterization of global asymptotic stability, which may be of interest on its own.
A Review of Biosensors for Non-Invasive Diabetes Monitoring and Screening in Human Exhaled Breath

Exhaled breath acetone has been identified as a diabetes biomarker for non-invasive diagnosis. Its detection using biosensors features has many advantages over the conventional means. This paper reviews the recent literature on the detection of exhaled breath acetone and acetone vapor of diabetic interest. The biosensors have been classified based on their transduction methods. The performance characteristics of the biosensors have been explored for comparison. The future trends are also highlighted.
Gradient compression for communication-limited convex optimization

Data-rich applications in machine-learning and control have motivated an intense research on large-scale optimization. Novel algorithms have been proposed and shown to have optimal convergence rates in terms of iteration counts. However, their practical performance is severely degraded by the cost of exchanging high-dimensional gradient vectors between computing nodes. Several gradient compression heuristics have recently been proposed to reduce communications, but few theoretical results exist that quantify how they impact algorithm convergence. This paper establishes and strengthens the convergence guarantees for gradient descent under a family of gradient compression techniques. For convex optimization problems, we derive admissible step sizes and quantify both the number of iterations and the number of bits that need to be exchanged to reach a target accuracy. Finally, we validate the performance of different gradient compression techniques in simulations. The numerical results highlight the properties of different gradient compression algorithms and confirm that fast convergence with limited information exchange is possible.
Coding Theorem for Systematic LDGM Codes Under List Decoding

This paper is concerned with three ensembles of systematic low density generator matrix (LDGM) codes, all of which were provably capacity-achieving in terms of bit error rate (BER). This, however, does not necessarily imply that they achieve the capacity in terms of frame error rate (FER), as seen from a counterexample constructed in this paper. We then show that the first and second ensembles are capacity-achieving under list decoding over binary-input output symmetric (BIOS) memoryless channels. We point out that, in principle, the equivocation due to list decoding can be removed with negligible rate loss by the use of the concatenated codes. Simulation results show that the considered convolutional (spatially-coupled) LDGM code is capacity-approaching with an iterative belief propagation decoding algorithm.
Convolutional 3D Attention Network for Video Based Freezing of Gait Recognition

Freezing of gait (FoG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is a typical symptom of Parkinson's disease (PD) and has a significant impact on the life quality of PD patients. Generally trained experts need to review the gait of a patient for clinical diagnosis, which is time consuming and subjective. Nowadays, automatic FoG identification from videos provides a promising solution to address these issues by formulating FoG identification as a human action recognition task. However, most existing human action recognition algorithms are limited in this task as FoG is very subtle and can be easily overlooked when being interfered with by irrelevant motion. In this paper, we propose a novel action recognition algorithm, namely convolutional 3D attention network (C3DAN), to address this issue by learning an informative region for more effective recognition. The network consists of two main parts: Spatial Attention Network (SAN) and 3-dimensional convolutional network (C3D). SAN aims to generate an attention region from coarse to fine, while C3D extracts discriminative features. Our proposed approach is able to localize attention region without manual annotation and to extract discriminative features in an end-to-end way. We evaluate our proposed C3DAN method on a video dataset collected from 45 PD patients in a clinical setting for the quantification of FoG in PD. We obtained sensitivity of 68.2%, specificity of 80.8% and accuracy of 79.3%, which outperformed several state-of-the-art human action recognition methods. To the best of our knowledge, our work is one of the first studies detecting FoG from clinical videos.
Negative-Capacitance FinFET Inverter, Ring Oscillator, SRAM Cell, and Ft

In this work, we use thermal-ALD to prepare ferroelectric HfZrO2 (HZO) thin film with thickness from 3 to 7 nm for the NC-FinFET's gate stack. The subthreshold swing (SS) was as low as 5 mV/dec (SSmin) over 4 orders of ID. Lower thermal budget process, CO2 far-infrared laser activation and 400°C Ni silicide are employed in the 2-level metal backend integration for maintaining the orthorhombic phase in HZO thin film and minimizing the hysteresis in IV. NC-FinFET inverter has 77% higher voltage gain compared to FinFET-inverter employing HfO2 gate dielectric. NC-FinFET ring oscillator exhibit small speed and power advantages over FinFET oscillator. For the first time, NC-FET cut-off frequency (Ft) frequency is measured, 23.1 GHz or 23% higher than the control FET Ft. NC-FinFET SRAM was observed to exhibit large noise margin.
An Equidistantly Stepped Waveguide TE11 -TE01 -Mode Converter for Millimeter Wave Radar Applications

In this paper, we present a stepped circular waveguide TEll-TEol-mode converter for millimeter wave radar applications in the range of 69 to 91.7 GHz resulting in a relative bandwidth of approximately 28%. The converter is milled and copper/gold-plated into 34 printed circuit boards (PCB) with identical thickness, resulting in a compact setup that grants an alternative way of manufacturing in comparison to precision mechanic processes. To achieve optimal conversion, three-dimensional electromagnetic field simulations with CST Microwave Studio 2018 are performed. Moreover, measurements with a realized back-to-back converter configuration, a frequency-modulated continuous-wave (FMCW) radar, and a commercial power meter confirm the outstanding functionality of the converter and its robustness against varying production process parameters.