Miracle-dz/UniFAR-FTUs huggingface dataset:
Dark Lock-in Thermography Identifies Solder Bond Failure as the Root Cause of Series Resistance Increase in Fielded Solar Modules

Correlating the electrical performance of photovoltaic modules to the spatially resolved photoluminescence, electroluminescence, and dark lock-in thermography (DLIT) images is an important long-term goal for developing solar cell technology. These images offer highly sophisticated and detailed information about the spatial distribution and (if imaged at successive time intervals) temporal degradation of local series and shunt resistances. There have been extensive studies to correlate these imaging techniques to local characteristics at the cell level. However, it has been difficult to extract and quantify module-level information from the techniques. In this study, we interpret module current-voltage (I-V) measurements along with corresponding DLIT images within a module-scale simulation framework, demonstrating that series resistance degradation of the module I-V characteristics, in this case, can be attributed to solder bond failures. Our simulations highlight how current crowding associated with a failed solder joint (or a section of the solder pad) translates to the characteristic point-like (asymmetric doublet) heating pattern in neighboring solder joints (or the neighboring regions of the solder pad). The correlation between series resistance and solder joint degradation could inform expedited diagnosis of field degradation of solar modules.
Reducing Energy Consumption and Decentralizing Computing through Heat Redistribution

A byproduct of computations performed by semiconductor devices is heat. Many compute intensive functions are currently performed in the cloud by hardware placed in data centers, where heat must be removed at additional energy cost. Granite Mountain Technologies is exploring an energy conserving model and technology for decentralizing computation from data centers to areas where heat is required. This compute model can provide vast energy savings benefits, while also redistributing monetary gains from large organizations capable of running data centers to individuals. We report on a decentralized computing appliance implemented with relative timed integrated circuits in the 12nm technology node. The appliance can accept calculations at a variable rate which allows heat created from computations to achieve the desired temperature of a room. The benefits of applying this approach to residential heating is evaluated.
SMART BEAR: A large scale pilot supporting the independent living of the seniors in a smart environment

SMART BEAR is a multinational European research project implementing an innovative, secure and privacy-preserving platform with market available wearable and medical devices, along with smart home sensors. The solution will target the elderly population who suffer from Hearing Loss, Cardio Vascular Diseases, Cognitive Impairments, Mental Health Issues and Balance Disorders. The platform will be tested and validated in five large scale pilots, in six different countries and 5100 individuals, providing evidenced-based interventions on lifestyle, medical significant risk factors, and chronic disease management. The present work demonstrates the initial requirements collection and analysis, along with the architecture and the Decision Support System, which will provide the evidence on the recommendations to the individuals.
All-Digital CMOS Time-to-Digital Converter With Temperature-Measuring Capability

This brief presents an all-digital CMOS pulse-shrinking time-to-digital converter (TDC) with temperature-measuring capability for higher circuit value and cost saving. A pulse generator is proposed to generate either a time-added pulse or a temperature-sensing pulse. A cyclic delay line (DL) composed of a temperature-sensing DL and a pulse-shrinking DL becomes area efficient and achieves sufficiently wide dynamic range. A time subtractor that eliminates the effect of the offset error enhances accuracy. The proposed TDC is fabricated with TSMC 0.35- $\mu \text{m}$ CMOS process and has a small area of approximately 0.019 mm2. The effective time resolution is approximately 45 ps, with an integral nonlinearity of −0.5 to 0.85 least significant bit (LSB). The achieved temperature resolution is nearly 0.1 °C/LSB, with an inaccuracy of −1.35 °C to 0.7 °C for a range of 0 °C–90 °C. Experimental results prove that we have proposed the first TDC that successfully measures time and temperature.
Cuffless Blood Pressure Estimation Using Features Extracted from Carotid Dual-Diameter Waveforms

The major challenges in deep learning approaches to cuffless blood pressure estimation is selecting the most appropriate representative of the blood pulse waveform and extraction of relevant features for data collection. This paper performs an analysis of a novel dataset consisting of 71 features from the carotid dual-diameter waveforms and 4 blood pressure parameters. In particular, the analysis uses gradient boosting and graph-theoretic algorithms to determine (1) features with high predictive power and (2) potential to be pruned. Identifying such features and understanding their physiological significance is important for building blood pressure estimation models using machine learning that is robust across diverse clinical environments and patient sets.
Antialiased Transmitter-Side Digital Backpropagation

Digital backpropagation (DBP) is an electronic scheme for compensating nonlinear distortions in fiber transmission systems. Due to the nonlinearity-induced spectral broadening, the data must be oversampled to avoid aliasing, which increases the complexity and power consumption of the scheme. In this work, we show that aliasing can alternatively be prevented by distributed antialiasing filters, at a lower complexity. We proposed a new modified split-step Fourier method (SSFM) with easy-to-implement low-pass filters (LPFs) in the linear steps to avoid aliasing due to spectral broadening. Both the forward fiber propagation and a transmitter-side DBP are simulated using the modified SSFM. High-order modulation formats such as 256-ary quadrature-amplitude-modulation (256-QAM) and 1024-QAM transmissions at 28 Gbaud and 64 Gbaud over 1000 km fiber are considered, and our results show that the complexity of the DBP can be reduced by up to 50%. The optimal bandwidth of the LPFs is studied for both forward propagation and the DBP.
An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

The bidding strategy plays the most important role to help the Demand Side Platforms (DSPs) making bidding decisions on a large number of bid requests in Real Time Bidding (RTB) to satisfy the different objectives of campaigns under the lifetime and budget constraints. In this paper, we focus on branding campaign whose objective is to obtain as many impressions as possible under the lifetime and budget constraints. To achieve the objectives of branding campaigns, we propose a novel expected win rate-based bidding strategy for branding campaign under the lifetime and budget constraints by utilizing a model-free reinforcement learning model. Specifically, to prevent missing good opportunities resulting from submitting extremely low bid prices, the concept of the base winning price is introduced to determine the lower bound of expected winning price. In addition, to obtain more impressions, the concept of the DSP-specified budget spending plan is proposed to determine the proper winning prices. The base expected win rate is then calculated based on the base winning price and the winning price determined by the DSP-specified budget spending plan. Since RTB is a dynamic environment, we propose a novel expected win rate-based bidding strategy named EWDQN which utilizes Deep Q Network (DQN) to dynamically determine the expected win rate according to the base expected win rate and the current status of the RTB market, and then determines the bid price according to the expected win rate. To the best of our knowledge, this is the first research applying the reinforcement learning technique on the bidding strategies for branding campaign. To measure the performance of EWDQN, several experiments are conducted on two real datasets. Experimental results show that EWDQN outperforms the-state-of-the-art bidding strategies for branding campaign in terms of the number of obtained impressions and CPM (cost per thousand impressions).
Comparative Study of a Biomechanical Model-based and Black-box Approach for Subject-Specific Movement Prediction*

The performance and safety of human robot interaction (HRI) can be improved by using subject-specific movement prediction. Typical models include biomechanical (parametric) or black-box (non-parametric) models. The current work aims to investigate the benefits and drawbacks of these approaches by comparing elbow-joint torque predictions based on electromyography signals of the elbow flexors and extensors. To this end, a parameterized biomechanical model is compared to a non-parametric (Gaussian-process) approach. Both models showed adequate results in predicting the elbow-joint torques. While the non-parametric model requires minimal modeling effort, the parameterized biomechanical model can lead to deeper insight of the underlying subject specific musculoskeletal system.
Power Control for Two-Way AF Relay Assisted D2D Communications Underlaying Cellular Networks

As a key technology of the 5G communication system, device-to-device (D2D) communications have drawn significant research interests. However, the advantage of D2D communications may be limited when D2D users are far away from each other or the communication environments are harsh. In order to extend the range of D2D communications, a promising way is to use relaying technique to assist the communications. In this paper, we use a two-way amplify-and-forward (AF) relay to assist the underlay D2D communications and investigate the power control problem. Specifically, we formulate the power control problem as the optimization of the performance of the D2D link while fulfilling the quality-of-service (QoS) requirement of the cellular link. Two optimization objectives are considered for the D2D link, i.e., maximization of the minimum SINR at D2D users and maximization of the sum-rate of D2D users. For the first optimization objective, we can compare the possible optimal solutions for the three boundary conditions and obtain the optimal solutions. For the second optimization objective, we can convert the objective function into a concave function based on difference of convex (D. C.) structure, and use an iterative algorithm to solve the optimization problem. The simulation results show that the proposed power control scheme can improve the performance of the D2D link.
A Three-way Classification with Game-theoretic N-Soft Sets for Handling Missing Ratings in Context-aware Recommender Systems

Context-aware recommender system (CARS) plays a vital role to paved way for improving traditional recommendation phenomena. A key issue in CARS is the selection of influencing contextual group which is most suitable for the recommendation of an item. In general, the selection of suitable contextual group in CARS is faced with challenges due to uncertainty in the classification of items with missing non-binary ratings. The underlying reaction of each user towards an item is implicitly assumed to be binary in nature because of which uncertainty occurs in the item classification. In particular, such a situation where ratings are missing for an item, make the exploitation of appropriate contextual group difficult for an item recommendation. In this article, we address the problem of the inappropriate classification of items into irrelevant contextual groups due to missing non-binary ratings. To this extent, we propose a three-way classification model using game-theoretic N-soft sets for improving the classification process by handling missing ratings. In particular, a game is formulated using game-theoretic N-soft sets to determine the effective threshold configuration used to induce three-way classification of items with missing non-binary ratings. Moreover, a thorough evaluation of our proposed model is carried out on the datasets of LDOS-CoMoDa and InCarMusic, where outcome signifies the effectiveness and performance of the proposed model.
Attention Networks for Multi-Task Signal Analysis

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks have demonstrated encouraging results and an ability to capture complex patterns and dependencies in the data. However, representations that capture the entirety of the raw signal are suboptimal as not all portions of the signal are equally important. As such, attention mechanisms are proposed to divert focus to regions of interest, reducing computational cost and enhancing accuracy. Here, we evaluate attention-based frameworks for the classification of physiological signals in different clinical domains. We evaluated our methodology on three classification scenarios: neurogenerative disorders, neurological status and seizure type. We demonstrate that attention networks can outperform traditional deep learning models for sequence modelling by identifying the most relevant attributes of an input signal for decision making. This work highlights the benefits of attention-based models for analysing raw data in the field of biomedical research.
An advanced laboratory for research and training of smart grid control and protection schemes

This work introduces the design and implementation of a realistic simulation platform to teach distribution management systems (DMS) and smart grids research testing. The laboratory known as GridTeractions has a client/server structure that allows users to interact as customers in the distribution network or as managers in the control center. The laboratory integrates a co-simulation framework with data processing software, data visualization, and control tools oriented to build a DMS environment, which allows facing educational challenges in applying advanced optimization to smart grids. A volt/var control solution and the implementation of a fault location engine (FLE) are presented. The proposed laboratory contributes to training and research of advanced distribution automation approaches where the experimental test could be easily implemented.
The Space-Time Variation of Phase Imbalance for GF-3 Azimuth Multichannel Mode

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technology that can prevent the minimum antenna area constraint and provide high-resolution and wide-swath SAR images. The calibration of phase imbalance is an important topic in the AMC SAR signal processing since it has a significant impact on the image quality. For one SAR image, the phase imbalance is usually considered as a constant. However, because of the attitude errors, antenna position errors, target elevation, target motion, and phase mismatch of the antenna pattern, the actual phase imbalance is time-varying in azimuth and space-varying in range. Although the space-time variation of phase imbalances is too tiny to make a little effect on the product quality, it is still meaning to study the variation. On the one hand, it is possible to quickly estimate the phase imbalance of the whole observing operation and eliminate the influences of target motion, on the phase imbalance. On the other hand, some parameters, such as motion, can be retrieved by the phase imbalance. This article first establishes the signal models between phase imbalances and attitude errors, antennal position errors, and target elevation. Then, the signal model is verified by some simulations based on the parameters of Gaofen-3 (GF-3). In addition, the phase imbalance of the real data acquired by GF-3 is processed. Finally, based on the procession results of GF-3 real data, this article makes some discussions and points out the direction for the future work.
Sex Differences in Heart Rate Nonlinearity by Multifractal Multiscale Detrended Fluctuation Analysis

Recent developments of detrended fluctuation analysis (DFA) provide multifractal/multiscale (MFMS) descriptions of the heart rate self-similarity, a promising approach to cardiovascular complexity. However, it is unclear whether the MFMS DFA may also describe the nonlinear components of heart rate variability. Our aim is to define MFMS DFA indices for quantifying the short-term and long-term degree of the heart-rate nonlinearity and to apply these indices to detect possible sex-related differences.We recorded the inter-beat-interval (IBI) series in 42 male and in 42 female healthy participants sitting at rest for about 2 hours. For each series j, we generated 100 phase-randomized surrogate series. We applied the MFMS DFA to estimate the self-similarity coefficients α over scales τ between 8 and 512 s and moment orders q between –5 and +5, obtaining coefficients for the original series, αO,j (q, τ), and for each surrogate, αi,j (q, τ) with 1≤i≤100. We first evaluated πj(q, τ), percentile of αi,j (q, τ) distribution in which was αO,j (q, τ). Then we calculated the percentages of scales where πj(q, τ) was <5% for 8≤τ≤16 s (short-term nonlinearity index NL1(q)) and for 16≤τ≤512 s (long-term nonlinearity index NL2(q)). We found that NL1(q) was generally greater than 50% at all q≥0 but q=2 (i.e., moment order of the monofractal DFA), while at q<0 it was high in males only, with significant sex differences at q=-1 and q=-2. Results indicate that the multifractal DFA may highlight nonlinear heart-rate components at the short scales that are not revealed by the traditional monofractal DFA and that appear related to gender differences.Clinical Relevance— This supports the use of MFMS DFA to integrate the linear information from traditional spectral methods of heart rate variability in clinical studies aimed at improving the stratification of the cardiovascular risk.
A Neurodynamic Approach to L0-Constrained Optimization

$L_{0}-$constrained optimization problem has been widely applied to sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) in signal processing, statistics, and related fields. However, it is an NP-hard problem to obtain the optimal solution of the $L_{0}-$constrained optimization problem in the recovery of compressive sensed signals. In this paper, we apply neurodynamic approach to solve the $L_{0}-$constrained optimization problem. Based on an inverted Gaussian function, a function of L0 approximation can be obtained and a three-layer projection-type neurodynamic model is further constructed. The L0 approximation constraint condition and a convexity objective function under which the neurodynamic optimization method is guaranteed to achieve the locally convergent optimal solution. Experimental results illustrate the effectiveness of the proposed neurodynamic approach.
The Effect of Basic Microshapes on hNT Astrocytes Cytoplasmic Process Outgrowth

Astrocytes are a non-homogeneous cell type, highly mobile which constantly extend and retract their cytoplasmic processes in what would seem random in direction. In this paper, we investigate how simple geometric microshapes can be used to control the outgrowth of human astrocytes cytoplasmic processes. We investigate the effect of how five regular microshapes: the circle, triangle, square, pentagon and hexagon control astrocyte cytoplasmic process outgrowth. For all the different microshape types, we observe that it is the corners of the shapes that that cause the astrocyte to produce spontaneous outgrowth except for the circle where the outgrowth occurs at a random radial position. This work suggests that the geometry of cell adhesive regions effects the outgrowth of hNT astrocytes.
Propagation Operator Based Boundary Condition for Finite Element Analysis

A new efficient boundary condition of finite element method (FEM) by using propagation operator is proposed. In this method, input, and output ports are terminated on their own boundaries instead of using perfectly matched layer (PML) which requires expanding the computational window. Moreover, this boundary condition can consider all modes including radiation modes without mode expansion. The propagation operator is efficiently calculated by Denman-Beavers iteration (DBI). The electromagnetic field on the POM boundary can be accurately propagated outside the boundary by using the propagation operator. In addition, we present a technique based on scattering operator method which can reduce the computational complexity of FEM. Three numerical results show that the present scheme is more accurate, and stable than conventional approximate boundary conditions such as using Padé approximation in both TE, and TM modes.
Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton

This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.
Power Allocation in NOMA Using Cournot Game Theory

The fifth Generation wireless communication performance can be improved by NOMA technology where the same time and frequency is allocated to a different user. NOMA technology deals with multiple user hence it’s also known as Multiple Input Multiple Output (MIMO). Power allocation in NOMA is one of the emerging fields among the researcher. The power allocation can be done using different optimization technology; game theory is one of them. In this article, Cournot game-theoretic approach is used to observe the power allocation in NOMA technology.
A Mechanized Pediatric Elbow Joint Powered by a De-Based Artificial Skeletal Muscle*

To increase the acceptability of exoskeletons, there is growing attention toward finding an alternative soft actuator that can safely perform at close vicinity of the human body. In this study, we investigated the capability of the dielectric elastomer actuators (DEAs), for muscle-like actuation of rehabilitation robots. First, an artificial skeletal muscle was configured using commercially available stacked DEAs arranged in a 3x4 array of three parallel fibers consisting of four DEAs connected in series. The shortening and force generation capabilities of this artificial muscle were then measured. An alternate 3x5 version of this muscle was mounted on the forearm of an upper extremity phantom model to actuate its elbow joint. The actuation capability of this muscle was then tested under various tensile loads, 1 N to 4 N, placed at the center of mass of the forearm+hand of the phantom model. The active range of motion and angular velocity of the phantom model’s tip of the hand were measured using a motion capture system. The 3×4 artificial muscle produced 30.47 N of force and 5.3 mm of maximum shortening. The 3x5 artificial muscle was capable of actuating the elbow flexion 19. 5º with 16.2 º/s angular velocity in the sagittal plane, under a 1 N tensile load. The active range of motion was substantially reduced as the tensile loads increased, which limits the capability of these muscles in the current upper extremity exoskeleton design.