Miracle-dz/UniFAR-FTUs huggingface dataset:
New energy-efficient hybrid wide-operand adder architecture

The consumer electronics markets have increased the demand for high-speed and low-power adders with large operands to be integrated in modern portable systems. Traditional fast adder architectures, such as parallel-prefix adders, exhibit high-power consumption for large operands. The hybrid design is one of the most promising techniques to achieve a trade-off between the delay and power-consumption for the addition of large operands. This study presents a new hybrid adder architecture, specifically designed for large operands, based on the premise that in large parallel-prefix adders the least-significant carries are produced much sooner than the most-significant ones. Therefore, the authors avoid the incorporation of fast architectures related with the application of carries to the final summation least-significant bits, with no impact on the critical path. This leads to a reduction in the area of the summation blocks in the least-significant positions without compromising the speed. Moreover, the complement of the carries is generated and propagated inside the carry network of the proposed adder in order to decrease the delay. VLSI implementation results on the 65-nm-TSMC technology show that the proposed adder achieves >25% of energy savings, and a reduction of over 30% of the area-delay-product in comparison with state-of-the-art wide-operand adders.
Variational Bayes Block Sparse Modeling with Correlated Entries

This paper addresses the problem of Bayesian Block Sparse Modeling when coefficients within the blocks are correlated. In contrast to the current hierarchical methods which do not exploit correlation structure within the blocks, we propose a three level hierarchical estimation framework. It employs heavy-tailed priors for block sparse modeling and variational inference for Bayesian estimation. This paper also describes the relationship between proposed framework and some of the existing Block Sparse Bayesian Learning (SBL) methods and show that these SBL methods can be viewed as its special cases. Extensive experimental results for synthetic signals are provided, demonstrating the superior performance of the proposed framework in terms of failure rate, relative reconstruction error, to name a few. We also demonstrate the applicability of this framework in telemonitoring of Fetal Electrocardiogram.
Body Area Networks in Healthcare: A Brief State of the Art

A body area network (BAN) comprises a set of devices that sense their surroundings, activate and communicate with each other when an event is detected in its environment. Although BAN technology was developed more than 20 years ago, in recent years, its popularity has greatly increased. The reason is the availability of smaller and more powerful devices, more efficient communication protocols and improved duration of portable batteries. BANs are applied in many fields, healthcare being one of the most important through gathering information about patients and their surroundings. A continuous stream of information may help physicians with making well-informed decisions about a patient’s treatment. Based on recent literature, the authors review BAN architectures, network topologies, energy sources, sensor types, applications, as well as their main challenges. In addition, the paper focuses on the principal requirements of safety, security, and sustainability. In addition, future research and improvements are discussed.
Simultaneous Measurement of Axial Strain, Bending and Torsion With a Single Fiber Bragg Grating in CYTOP Fiber

This paper presents the development of a three-dimensional (3-D) displacement sensor based on one fiber Bragg grating (FBG). In order to obtain higher sensitivity and dynamic range, the FBG is inscribed in low-loss, multimode, cyclic transparent amorphous fluoropolymers using the direct-write, plane-by-plane femtosecond laser inscription method. The proposed sensor is based on the influence of each displacement condition, namely, axial strain, torsion, and bending on the FBG reflection spectrum. Such influence is analyzed with respect to the FBG wavelength shift, reflectivity, and full width half maximum. The operation principle and theoretical background of the proposed approach is numerically analyzed by means of finite element analysis for the strain along the grating length and coupled-mode theory with a modified transfer matrix formulation for the FBG spectrum. The sensor is experimentally characterized and validated in which the results show good agreement between the applied axial strain, bending, and torsion with relative errors below 5.5%. Thus, the proposed sensor is an interesting alternative for measuring displacements in 3-D applications, such as movement analysis and the instrumentation of novel soft robots.
Statistical Detector of Resampled TIFF Images

Resampling is a common manipulation in creating tampered digital images. The goal of this paper is to develop an efficient detector to distinguish between a resampled TIFF image from an original TIFF image. To this end, we first propose a statistical model for resampled TIFF images by analyzing the complete processing process from a RAW image to a resampled TIFF image. Next, we formulate the detection problem as a likelihood ratio test between the models of original and resampled TIFF images. The test power is analytically evaluated in the context that all model parameters of original TIFF images are unknown. Numerous numerical experiments justify the performances of the detector.
Modified Harmonic Reduction Pulse Width Modulation (mHRPWM) for Switched Excitation of Resonant HIFU Transducers

High Intensity Focused Ultrasound (HIFU) is reliant on carefully designed excitation systems capable of high power, and low distortion. The use of array transducers additionally requires amplitude control for apodization and attenuation compensation plus phase control for inter element timing for beamstearing, focusing and correction. Power amplifiers possess all the required characteristics except they can be expensive and bulky thus placing barriers to adoption especially for large channel count arrays. Switched mode excitation systems are capable of fulfilling all requirements except minimizing distortion can be problematic due to the lack of availability of suitably high speed MOSFETs placing restrictions of waveform design. This work presents an modification to the Harmonic Reduction Pulse Width Modulation (HRPWM) method specifically designed to minimize harmonic distortion in systems constrained by MOSFET performance in HIFU excitation systems. The proposed method is of significance as the use of external filtering and transformer coupling of the excitation waveform is not required. The proposed method is demonstrated through experimental hydrophone testing with a 1.1 MHz HIFU transducer and demonstrates the successful generation of excitation waveforms with continuously variable amplitude control and low third harmonic output.
A Dynamical System With Fixed Convergence Time for Sparse Recovery

The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog circuits, which is usually faster. In this paper, we focus on the continuous method and propose a fixed-time convergence dynamical system. Compared with the existing system, it dynamically allocates the exponent according to time-varying elements of the system state, avoiding possible mismatches between the fixed exponent and some elements.
Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory

This paper proposes a new self-scheduling framework for demand response (DR) aggregators, which contributes over the existing models in the following aspects. The proposed model considers the uncertainties posed from consumers and electricity market prices. Further, the given model applies the information-gap decision theory (IGDT) in the self-scheduling problem, which guarantees the predefined profit by the aggregator and avoids computational burdens caused by scenario-based methods, such as stochastic programming approaches. The DR aggregator procures DR from two proposed programs, i.e., reward-based DR and time-of-use. Then, the obtained DR is offered into day-ahead and balancing markets. An IGDT-based profit function is proposed, which leads to a bilevel program. The given bilevel model is then transformed into an equivalent single-level model by developing a non-KKT method, which is solved through commercial solvers available in general algebraic modeling system. The feasibility of the problem is studied using a case study with realistic data of electricity markets.
Cost-Effective and High-Efficiency Variable-Speed Switched Reluctance Drives With Ring-Connected Winding Configuration

This paper presents a novel converter topology for six-phase switched reluctance motor (SRM) drives, which reduces the number of switches and diodes by half, compared with the conventional asymmetric half-bridge converter, but needs no additional energy storage component. A dynamic model of a six-phase SRM is developed in the MATLAB/SIMULINK environment and conventional current chopping and angle position control techniques are applied to the proposed converter, demonstrating successful operation across the full speed range with modified conventional control techniques, lower converter losses, and higher system efficiency compared with the asymmetric half-bridge converter. Experimental tests comparing two versions of the proposed converter with an asymmetric half-bridge are described and verify the predictions of the simulations.
WGAN Based Edge Preserved De-Blurring Using Perceptual Style Similarity

Generative Adversarial Network (GAN) is an effective generative model and can be used for de-blurring. In this paper, we propose an edge preserved de-blurring method using Wasserstein generative adversarial network with gradient penalty (WGAN-GP), which is based on conditional GAN. Also, since detailed-edge is the most important factor in de-blurred image, in order to preserve detailed-edge and capture its perceptual similarity, the style loss function is added to represent the perceptual information of the edge. Consequently, the proposed method improves the similarity between sharp images and blurred images by minimizing Wasserstein distance, and well captures the perceptual similarity using style loss function, considering the correlation of features in Convolutional Neural Network. Experiments depict that the proposed method achieves 0.98 in SSIM that is higher performance, compared to other conventional methods such as filter based methods and content based method.
Design and Optimization of Inductive-Coupling Links for 3-D-ICs

Recent research in the field of 3-D system integration has looked to the use of inductive-coupling links (ICLs) to provide vertical connectivity without incurring the inflated fabrication and testing costs associated with through-silicon vias. For power-efficient ICL design, optimization of the utilized physical inductor geometries is essential, but currently must be performed manually in a process that can take several hours. As a result, the generation of optimized inductor designs poses a significant challenge. In this paper, we address this challenge in three main contributions: 1) a novel, nonuniform planar inductor layout that exhibits enhanced performance when compared with conventional uniform inductors; 2) a rapid solver for evaluating inductor layouts; and 3) a high-speed optimization algorithm for determining best performing coil pairs. These three contributions are combined as a CAD tool for optimization of ICLs for 3-D-ICs (COIL-3-D). Results demonstrate that COIL-3-D achieves an average accuracy within 7.8% of finite-element tools consuming a small fraction of the time ( $1.5\times 10^{-3}$ %), significantly ameliorating the design of ICL-based 3-D-ICs. We also demonstrate that using COIL-3-D to optimize ICL inductor layouts can yield significant performance (up to 41.5% bandwidth improvement) and power (up to 8.1% power improvement) benefits, when compared with layouts used in prior ICL implementations. For these reasons, this paper unlocks new potential for low-cost, power-efficient 3-D integration using ICLs.
Optimization of Three-Terminal Perovskite/Silicon Tandem Solar Cells

We use simulations to optimize perovskite/silicon tandem solar cells in a novel three-terminal configuration, with one terminal at the front and two at the rear. We consider configurations in which the top cell has either the inverted or the same polarity as the bottom cell. Our goal is to minimize the optical losses, to compare the performance of both configurations and to determine the realistically achievable efficiency. Optical simulations show that if the hole-transporting material is in front of the perovskite, it gives rise to parasitic absorption losses. If it is behind the perovskite, these losses are avoided, however, at the cost of increased reflection losses. We systematically minimize these reflection losses. This increases the tandem's total implied photocurrent density from 34.4 to 41.1 mA/cm2. To determine the corresponding power conversion efficiency of these three-terminal tandems, electrical circuit simulations are performed based on existing 22.7% efficient perovskite and 24.9% efficient silicon cells. These simulations show that tandem efficiencies up to 32.0% can be obtained.
Loading Analysis of Channelized SATCOM System with Link-Margin Degree Optimization

In this letter, we investigate the loading analysis of digital channelized satellite communication (SATCOM) system operating in frequency-division multiple access (FDMA) mode, multi-frequency time-division multiple access (MF-TDMA) mode and overlay combined multiple access (OCMA) mode. In an effort to enhance link stability of these systems, a max-min optimization objective for link-margin degree (LMD) is firstly established under the constraint that all loading links are supportable. Basing on fully using the power of transmitting terminals and directly reducing the difference among all LMDs to enhance the minimum LMD as much as possible, an effective maximum value back-off searching (MVBS) algorithm is proposed for the optimization model. Finally, numerical simulations reveal that, under the constraint of link supportability, the proposed algorithm brings about considerable improvement of the minimum LMD for enhancing link stability, which effectively demonstrates the correctness of our scheme.
An algorithm for ship wake detection from the SAR image using the Radon transform and morphological image processing

Using the Rador transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linesr texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.
Novel High Gain, High Efficiency DC–DC Converter Suitable for Solar PV Module Integration With Three-Phase Grid Tied Inverters

This paper proposes a novel configuration for high gain, high efficiency dc–dc converter comprising a single switch, two intermediate capacitors, and a coupled inductor for low voltage solar PV module fed applications. The high voltage gain is achieved by charging the intermediate capacitors through the coupled inductor in parallel and discharging in series. In a two winding coupled inductor, considered in the presented work, maximum two intermediate capacitors can be integrated with the secondary winding. A passive lossless clamped circuit is also provided in the converter, which recovers the leakage energy to improve the efficiency and alleviate large voltage spike. The structure of the circuit is such that the power device voltage stress is reduced thereby increasing the efficiency. The maximum power point tracking at various irradiation levels is implemented in the proposed converter. Laboratory prototype of a 300-W system with 30–45-V input and 700-V output was built to validate the theoretical claims. All the detailed analysis, simulation, and experimental waveforms are presented. A maximum converter efficiency of around 95% is achieved.
Performance Analysis of Omni-Directional and Directional Power Delay Profile for Millimeter Wave 5G Cellular Networks in LOS Environment

This paper analyses the performance of Directional Power Delay Profile (PDP) and Omni-Directional Power Delay Profile in Line-Of-Sight environment for Millimeter Wave (mmWave) Fifth Generation (5G) wireless communication networks at operating frequency 15, 28, 37, 60, 64, 71, and 73 GHz. All the simulation has done by open source Matlab based simulation software NYUSIM developed by New York University (NYU), USA. Performance has been evaluated for Directional and Omni-Directional PDP in terms of path loss, path loss exponent and receive power which are important Radio propagation mechanisms parameters for mmWave 5G Cellular networks. The output of this paper shows Omni-Directional PDP has lower path loss and path loss exponent compare to Directional PDP in LOS environment. Moreover, result also shows Directional PDP has higher received power at receiver end compare to Omni-Directional PDP at 15, 28, 37, 60, 64, 71, and 73 GHz operating frequencies.
Background Calibration of Comparator Offsets in SHA-Less Pipelined ADCs

Sample-and-hold amplifier-less (SHA-less) pipelined analog-to-digital converters (ADCs) are well suited for high-resolution, high-speed and low-power applications. Apart from the comparator static offset, a dynamic offset is induced due to the timing skew and bandwidth mismatch between the multiplying digital-to-analog converter and the comparator input paths in the first stage, which brings a serious design challenge. This brief presents a novel background calibration technique for these offsets. First, the comparator’s static offset and dynamic offset are discussed and analyzed. Then, a new evaluation technique is proposed to synchronously extract the values of the static offset and dynamic offset through the residue output at decision points. In this brief, the new calibration method is validated using behavioral models. The effective number of bits is improved from 5.04 bits to 11.96 bits, while the spurious free dynamic range is improved by 50.7 dB from our simulation. Thanks to the background calibration, comparator offset errors exceeding the built-in redundancy of the architecture become acceptable. The proposed method relaxes comparator design requirements effectively. More importantly, the calibration can maximize the input frequency of the SHA-less pipelined ADCs.
user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code

In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.
Challenges of P300 Modulation Using Transcranial Alternating Current Stimulation (tACS)

The P300 component of the event-related potential (ERP) is a well investigated phenomenon in the human electroencephalogram (EEG) and has been related to stimulus processing and attentional mechanisms. Event-related oscillations (ERO) represent a potential mechanism responsible for generating the ERP. In particular, oscillatory activity in the delta and theta frequency range has been associated with the generation of the P300 component. Transcranial Alternating Current Stimulation (tACS) is capable of modulating oscillatory brain activity in a frequency-specific manner. In this study, we aimed to modulate P300 amplitude using tACS by stimulating the individual ERO involved in the generation of the P300 component. TACS was applied precisely in time to the target P300 occurring in a visual oddball task. In order to achieve an appropriate current distribution, we designed an electrode configuration consisting of two clusters of stimulation electrodes on central-parietal locations. We could not demonstrate a group difference in P300 amplitude after applying tACS in the stimulation condition (N = 17) vs. the sham condition (N = 11). TACS condition and sham condition did not differ regarding their reaction times in response to target stimuli or their event-related spectral perturbation (ERSP) at stimulation frequency. Although a significant influence of stimulation could not yet be revealed on a statistical level, we suggest that the proposed method of using tACS for modulating EROs merits further investigation. Modulation of the P300 component in the ERP could help to gain further insights in the role of EROs generating ERPs and the functional relevance of the P300 component. In this study, we propose a novel approach of applying tACS and provide advice on using tACS for the modulation of EROs.
Survey on Brain-Computer Interface

A brain-computer interface (BCI) provides a way to develop interaction between a brain and a computer. The communication is developed as a result of neural responses generated in the brain because of motor movements or cognitive activities. The means of communication here includes muscular and non-muscular actions. These actions generate brain activities or brain waves that are directed to a hardware device to perform a specific task. BCI initially was developed as the communication device for patients suffering from neuromuscular disorders. Owing to recent advancements in BCI devices—such as passive electrodes, wireless headsets, adaptive software, and decreased costs—it is also being used for developing communication between the general public. The BCI device records brain responses using various invasive and non-invasive acquisition techniques such as electrocorticography (ECoG), electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI). In this article, a survey on these techniques has been provided. The brain response needs to be translated using machine learning and pattern recognition methods to control any application. A brief review of various existing feature extraction techniques and classification algorithms applied on data recorded from the brain has been included in this article. A significant comparative analysis of popular existing BCI techniques is presented and possible future directives are provided.