Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the level of acceptance of a crowdsourcing solution to monitor infectious diseases propagation

This work presents a study for assessing the technology acceptance of a contact tracing app, also proposed by us, which is a hybrid crowdsensing application (opportunistic and participatory). The goal of the app is that users are notified if they were in contact with others infected. It also allows creating a heat map identifying streets, squares, and commercial locations to which contaminated users were, allowing more assertive hygiene actions and eliminating infectious disease outbreaks. Our methodology aimed on finding whether people would be willing to share their location, as well as their health issues related to COVID-19. It is composed by a survey for verifying the interest of the proposed application; the prototype of the application; and the use of Technology Acceptance Model (TAM). We can see that the vast majority of respondents to the first survey were interested in using a contact tracking application, even though they need to share their location and report when they become infected. In addition, the proposed RISCOVID application proved to be accepted for use by participants in the second survey.
SuperSlash: A Unified Design Space Exploration and Model Compression Methodology for Design of Deep Learning Accelerators With Reduced Off-Chip Memory Access Volume

Deploying deep learning (DL) models on resource-constrained embedded devices is a challenging task. The limited on-chip memory on such devices results in increased off-chip memory access volume, thus limiting the size of DL models that can be efficiently realized in such systems. Design space exploration (DSE) under memory constraint, or to achieve minimal off-chip memory access volume, has recently received much attention. Unfortunately, DSE alone cannot reduce the amount of off-chip memory accesses beyond a certain point due to the fixed model size. Model compression via pruning can be employed to reduce the size of the model and the associated off-chip memory accesses. However, in this article, we demonstrate that pruned models with even the same accuracy and model size may require a different number of off-chip memory accesses depending upon the pruning strategy adopted. Thus, mainstream pruning techniques may not be closely tied to the design goals, and thereby hard to be integrated with existing DSE techniques. To overcome this problem, we propose SuperSlash, a unified solution for DSE and model compression. SuperSlash estimates off-chip memory access volume overhead of each layer of a DL model by exploring multiple design candidates. In particular, it evaluates multiple data reuse strategies for each layer, along with the possibility of layer fusion. Layer fusion aims at reducing the off-chip memory access volume by avoiding the intermediate off-chip storage of a layer’s output and directly using it for processing of the subsequent layer. SuperSlash then guides the pruning process via a ranking function, which ranks each layer according to its explored off-chip memory access cost. We demonstrate that SuperSlash not only offers an extensive design space coverage but also provides lower off-chip memory access volume (up to 57.71%, 25.83%, 47.73%, and 29.02% reduction for VGG16, ResNet56, ResNet110, and MobileNetV1, respectively) as compared to the state-of-art.
Joint Array Diagnosis and Channel Estimation for RIS-Aided mmWave MIMO System

In this paper, we consider a reconfigurable intelligent surface (RIS) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) system. The system can obtain the huge gain via joint active beamforming at the base station (BS) and passive beamforming at the RIS. However, due to weather and atmospheric effects, outdoor RIS antenna elements are subject to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These blockages can cause an approximate squared power/SNR loss for the system. Different from the conventional array diagnosis, the RIS has no signal processing capability. Thus, we propose the joint array diagnosis and channel estimation techniques containing two stages to solve the problem. At the first stage the channel parameters at user equipment (UE) and BS are estimated using an iterative reweighted (IR) method. At the second stage, the array blockage coefficient vector and the effective sparse channel parameters at RIS are jointly estimated via solving a two-timescale non-convex optimization problem. We propose two algorithms, i.e., a batch algorithm (BA) and a two-timescale online joint array diagnosis and channel estimation (TOJADCE) algorithm to solve the problem and compare the performance of these two algorithms. Finally, to speed up the convergence of long-term variable and improve estimation performance, we propose a noise reduction (NR) algorithm. The simulations verify the effectiveness of our proposed algorithms.
Comparison of Si and SiC EMI and Efficiency in a Two-Level Aerospace Motor Drive Application

Power electronics are crucial elements in more-electric aircraft (MEA) applications. Existing hardware is almost exclusively silicon (Si)-based. However, recent advances in semiconductor technology yielded silicon carbide (SiC) components with current ratings suitable for aerospace applications. These devices have much strength that can increase power density and improve performance over Si-based hardware. High switching speeds are characteristic of SiC devices, and there is much concern about their impact on electromagnetic interference (EMI). This may necessitate larger EMI filters to comply with EMI standards, which would decrease power density and negate some of SiC’s benefits. The following research explores the tradeoff between efficiency and EMI. It compares the performance of two high-power three-phase converters: one built from SiC MOSFETs and the other from Si IGBTs. The test setup consists of a 250-kW drive stand capable of sourcing and regeneratively sinking power as well as a 40-kVA aerospace rated machine. Experimental results are obtained at a bus voltage of 270-Vdc with power levels exceeding 1.5 p.u. The findings demonstrate comparable EMI performance for Si and SiC under MIL-STD-461 with the SiC converter achieving higher efficiency.
Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder.
A Dual Phase Shedding Method for the Improvement of Efficiency and Reduction of Regulating Requirements in Series-series Inductive Power Transfer

In inductive power transfer (IPT) applications, especially in wireless charging, the variation of system load is a common issue that affects both the output power and efficiency. One conventional solution is to use a power converter for output regulation, and, optionally, another power converter for efficiency optimization. However, the regulating requirement can be large that the regulating power converter is required to operate with a heavy modulation depth, which leads to a higher loss and cost. On the other hand, the efficiency optimizing converter requires additional hardware and control. This paper proposes a dual phase shedding method for series-series compensated IPT that can effectively improve system efficiency while reducing the regulating requirement. The proposed method has a low requirement of additional hardware. Simulation and experimental results are provided for verification.
Aggressive Fine-Grained Power Gating of NoC Buffers

Power gating is effective for networks-on-chip (NoCs) to reduce the excessive leakage power dissipated by idle network components. Most existing NoC power-gating approaches rely on the routing algorithms to mitigate the power-gating blocking latency problem. When the network becomes faulty and fault-tolerant routing algorithms are applied, these approaches are no longer applicable or can seriously degrade the performance. Other approaches propose fine-grained buffer power gating, but they are too conservative in power saving due to the buffer backpressure flow control. To address these problems, we propose an aggressive fine-grained power gating of flit-sized buffer entries by adopting backpressureless flow control in an input-buffered network. The power-gating decisions are made based on the flit deflection rate. However, directly applying the backpressureless flow control leads to the difficulties of multiflit packet truncation and protocol deadlocks. Therefore, we modify the packet injection architecture to avoid packet truncation. This is done by chaining the local input port with a randomly chosen input port. Finally, we design a progressive recovery framework to handle both livelocks and protocol deadlocks. It does not need to truncate packets or strictly separate different message classes when the network is free of livelocks or protocol deadlocks. The experimental results show that with a hardware overhead of 9.6%, our design can save up to 59% network power consumption in both a fault-free and a faulty NoC with little zero-load latency penalty. Our design also approaches an ideal energy-proportional NoC because it can constantly reduce power consumption over a wide range of injection rates.
Elimination of 2f Ripple in a Current Shared Interleaved Current Fed Switched Inverter

High gain inverters like Interleaved Current Fed Switched Inverters (ICFSI), are prevalent in the applications, such as roof top PV installations. The wide acceptance of ICFSI in the applications having low input DC source voltage is attributed to its high boost factor, inherent shoot-through protection and high efficiency due to interleaving. In this paper, a control-based active current sharing technique is proposed to ensure current sharing between two interleaved modules of ICFSI along with DC voltage control. In converters like ICFSI, the input current possesses second harmonic component along with switching frequency component. Typically, the second harmonic component is reduced by increasing the size of the passive elements, which makes the converter bulky. This paper incorporates a control-based approach to eliminate the second harmonic component, which ensures smaller size of passive elements. The proposed control schemes are verified experimentally and also by PLEXIM simulation software.
Exploring the Impact of Gender on Character Mechanics in Multiplayer Online Battle Arena (MOBA) Games

Frequently, game characters have abilities that conform to the expectations of their gender, this correlation between character abilities and gender has not been fully explored. Previous research has identified that throughout various media, females are represented as secondary and submissive characters, this representation can affect the way that women are treated in real life. This impact has been found to influence their decision to pursue studies in a Science, Technology, Engineering and Mathematics (STEM) field. In this research, we intended to explore whether the gender of a character has an impact on their in-game mechanics and abilities. Our research finds that there are differences here, namely, male characters are more likely to have abilities which enable them to be self-sufficient, whereas female characters have more abilities for aiding other champions in-game, inherently placing them into a submissive role. This paper provides insights on gender representation in Multiplayer Online Battle Arena (MOBA) games from a novel perspective and discusses the impact on in-game experiences.
A Solution for the Full-bridge Grid-tie Inverter Using Single Virtual Ground Capacitor with UPWM

A single-phase transformerless full-bridge (FB) photovoltaic (PV) grid-tie inverter is presented. It achieves the ground leakage current mitigation by single Virtual Ground (VG) capacitor without extra switching devices. A hybrid pulsewidth modulation (HPWM) scheme is proposed to profile the output current and prevent sudden changes in the common-mode voltage. The HPWM is effectively the same as unipolar PWM (UPWM), but with soft voltage transition modulation (SVTM) introduced around the zero crossings. Apart from low switching losses, the output filter is implemented with a minimum number of components to achieve high attenuation of ripple current. Three commonly used PWM schemes for PV inverters are described. The intrinsic reason for oscillation phenomenon in bridgeless inverters with single VG capacitor is presented. A 340W, 380Vdc / 110Vac, 50Hz prototype has been built and evaluated.
Exploring Visuo-haptic Feedback Congruency in Virtual Reality

Visuo-haptic feedback is an important aspect of virtual reality experiences, with several previous works investigating its benefits and effects. A key aspect of this domain is congruency of crossmodal feedback and how it affects users. However, an important sub-domain which has received surprisingly little focus is visuo-haptic congruency in an interactive multisensory setting. This is especially important given that multisensory integration is crucial to player immersion in the context of virtual reality video games. In this paper, we attempt to address this lack of research. To achieve this, a total of 50 participants played a virtual reality racing game with either congruent or incongruent visuo-haptic feedback. Specifically, these users engaged in a driving simulator with physical gear shift interfaces, with one treatment group using a stick-shift gearbox, and the other using a paddle-shift setup. The virtual car they drove (A Formula Rookie race car) was only visually congruent with the stick-shift setup. A motion simulator was also used to provide synchronous vestibular cues and diversify the range of modalities in multisensory integration. The racing simulator used was Project CARS 2, one of the world’s most popular commercial racing simulators. Our findings showed no significant differences between the groups in measures of user presence or in-game performance, counter to previous work regarding visuo-haptic congruency. However, the Self-evaluation of Performance PQ subscale was notably close to significance. Our results can be used to better inform games and simulation developers, especially those targeting virtual reality.
The Site Frequency Spectrum under Finite and Time-Varying Mutation Rates

The diversity in genomes is due to the accumulation of mutations and the site frequency spectrum (SFS) is a popular statistic for summarizing genomic data. The current coalescent algorithm for calculating the SFS for a given demography assumes the μ → 0 limit, where μ is the mutation probability (or rate) per base pair per generation. The algorithm is applicable when μN, N being the haploid population size, is negligible. We derive a coalescent based algorithm for calculating the SFS that allows the mutation rate μ(t) as well as the population size N(t) to vary arbitrarily as a function of time. That algorithm shows that the probability of two mutations in the genealogy becomes noticeable already for μ = 10-8 for samples of n = 105 haploid human genomes and increases rapidly with μ. Our algorithm calculates the SFS under the assumption of a single mutation in the genealogy, and the part of the SFS due to a single mutation depends only mildly on the finiteness of μ. However, the dependence of the SFS on variation in μ can be substantial for even n = 100 samples. In addition, increasing and decreasing mutation rates alter the SFS in different ways and to different extents.
Model Reference Adaptive Control of Pulse Amplitude Modulated PM Motor Drive for High Power Transport Drone Applications

For moving applications such as high power transport drones, the motor drive always requires high demanded power density and efficiency for better cursing range. To maximize power output under limited battery voltage (50V~100V), surface permanent magnet (PM) motor with low phase inductance is preferred because of the low back electromotive force (EMF) voltage. Under this effect, the motor speed and the power density can be maximized. However, low inductance PM motors result in considerable switching reflected current harmonics, leading to performance degradation on the brushless DC (BLDC) drive. To solve this issue, this paper improves the BLDC drive on low inductance PM motors by adding a front-end DC/DC converter for the DC bus regulation, e.g. pulse amplitude modulation (PAM). In order to maximize the overall dynamic response, a model reference adaptive control (MRAC) is proposed to regulate the DC bus voltage. According to experimental results, the proposed PAM drive with MRAC achieves the better performance than conventional BLDC between the current total harmonic distortion (THD) and the dynamic operation. This proposed drive is implemented in a 5kW surface PM motor in a high power transport drone.
A Light-Weight Neural Network for Wafer Map Classification Based on Data Augmentation

In the semiconductor industry, the testing section has always played an important role. The testing section often requires engineers to judge the defect, which wastes a lot of time and cost. The accurate classification can provide useful information for engineers through neural networks. In this paper, we present a method for wafer map data augmentation and defect classification. Data augmentation is based on CNN encoder-decoder and the classification is based on depthwise separable convolutions. There are two datasets used, one is open dataset WM-811K and the other is built with a Taiwan company. We train two models with mobilenetV1 and V2 for the different datasets. The light-weight deep convolution can reduce model parameters and calculations, which is very efficient for the testing house with large production volumes. On two different data sets, our proposed method can reduce the number of parameters by 30% and 95%, and reduce the amount of calculation by 75% and 25%, respectively. The test accuracy of the first dataset is 93.95%. The second dataset test accuracy is 87.04%. After the data augmentation, accuracy is increased to 97.01% and 95.09%, respectively.
Unified Contamination-Aware Routing Method Considering Realistic Washing Capacity Constraint in Digital Microfluidic Biochips

To fully utilize the dynamic reconfigurability of digital microfluidic biochips, most of electrodes would be shared by different droplets. Thus, contaminations caused by liquid residues among droplets are inevitable which lead to lethal errors in bioassays. To remove the contaminations, washing operations are introduced as an essential step to ensure the correctness of bioassay. However, existing works have oversimplified assumptions on the washing droplet’s behavior and constraints which cannot clean all contaminations with erroneous outcomes. Moreover, straightforward integration of washing operations with droplet routing may increase the execution time of a bioassay which is not feasible for timing-critical bioassay. To effectively remove contaminations and minimize the execution time of a bioassay, this article proposes a unified contamination-aware routing method, which addresses the above issues simultaneously. Firstly, we present a top-down scheme to generate candidates of routing paths, then construct a shortest-path model to select desirable routing solution for all subproblems. With a decision diagram of droplets, we further propose an integer linear programming (ILP) formulation to compact the execution time. Finally, contamination removal by washing droplets with realistic washing capacity is considered for all subproblems. Tested on real-life benchmarks, our proposed method can significantly reduce 72% contamination spots and save 11% execution time.
A Dynamic Diffusion Algorithm for Distributed Secondary Control of DC Microgrids

This paper proposes a dynamic diffusion algorithm based distributed secondary control method in DC microgrids (MGs). Rather than conventional consensus algorithm, distributed communication between neighboring converters is implemented using a diffusion algorithm. The proposed approach improves the accuracy of power sharing in the absence of a central controller. In order to accurately represent the system behavior, a discretetime model is established considering the digital implementation of controllers and communication network. The influence of communication rate, the observation weight of diffusion as well as secondary control parameters on the system stability are investigated using the developed z-domain model of the whole MG. Case studies in MATLAB/Simulink verify the performance of the proposed method under load changes, link failure and converter faults. It has been demonstrated that the proposed dynamic diffusion algorithm shows the advantages over consensus in the aspect of convergence speed.
A Study on the Optimal Location of Injection Substation in Nigeria: A Review

One of the problem facing the power industry is improper placement of the injection substations. This paper thus, presents an in-depth review of the various methods adopted in overcoming the menace. Intelligent based metaheuristic optimization techniques has become a tool in solving numerous power system problems and has been critically considered. The aim of this research is to make available, relevant citations for learning progression on available editorials in the area of power distribution enhancement leading to further research interest.