Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted-center Loss for Facial Expressions Recognition

Deep learning-based facial expression recognition (FER) has gained attention in computer vision and artificial intelligence fields. Automatic FER can be found in various applications including human-computer interaction, virtual reality, medical treatment, and entertainment. However, the performance of the models degrades due to the imbalance in the training dataset. Weighted-softmax is usually used to tackle the imbalanced data problem, but it simply forces features of different classes to remain apart without paying attention to intra-class compactness. Center loss, on the other hand, increases the intra-class compactness but does not take the imbalanced data problem into consideration. In this paper, we proposed a new loss function called weighted-center loss, which simultaneously enhances intra-class compactness and mitigates the effect of an imbalanced dataset. Experimental results on the AffectNet dataset demonstrate the effectiveness of weighted-center loss.
A 2-D Capacitance Solver with Finite Difference Method

In this paper, we present a capacitance solver based on finite difference method (FDM). It simulates the cross section of interconnect structures and computes the capacitances per unit length. The techniques of forming symmetric coefficient matrix and nonuniform FDM grids are developed. And, with a sparse direct solver based on Cholesky factorization the presented solver exhibits high runtime efficiency with good accuracy. Experiments on pattern structures show that the presented solver is 3X faster than Raphael rc2, and is capable of accurately extracting structures with trapezoidal cross-section conductors and conformal dielectrics.
Object Detection in Artworks Using Data Augmentation

Object detection is useful for authenticating artworks, and object detection methods based on deep learning have been actively studied in recent years. However, existing object detection methods cannot perform well on artworks, due to the lack of labeled artwork datasets. To overcome the problem, we propose a novel approach for object detection on artworks. The proposed approach consists of two steps. First, we apply neural style transfer to the existing images for data augmentation. Second, we train an object detection method on the augmented dataset. We evaluate the proposed approach on Brueghel and People-Art datasets. The experimental results show that our proposed approach successfully detects objects in artworks. Moreover, our approach achieved 40.6 mAP, which is 7.8 mAP higher than the conventional approach without data augmentation. It is expected that the proposed approach is of great benefit to art historians for analyzing a large number of artworks.
Post-Prandial Blood Glucose Determination by Quantitative Mid-Infrared Spectroscopy

The multivariate calibration method of partial least-squares (PLS) was applied to the mid-infrared spectra of whole blood for quantitatively determining blood glucose concentrations. Separate calibration models were developed on the basis of spectra of whole blood obtained from six diabetic subjects from either in vitro glucose-supplemented blood or blood obtained from the same subjects in the post-prandial state during meal tolerance tests. The cross-validated PLS calibrations yielded average errors in glucose concentration of 11 and 13 mg/dL, respectively. It is desirable to use the calibration models based on the in vitro glucose-supplemented blood for determining glucose concentrations in unknown blood samples. However, when these multivariate calibration models based upon in vitro blood spectra were applied to the spectra of the postprandial blood samples, a subject-dependent concentration bias was observed. The source of this bias was not identified, but when the glucose determinations were corrected for the bias, average concentration errors were found to be 14 mg/dL. Changes in spectrometer design or calibrations based on large numbers of subjects are expected to eliminate the presence of this bias. If these measures do not succeed in eliminating the bias, then methods are demonstrated that significantly reduce the bias while retaining the sensitive outlier detection capabilities of the PLS methods. These latter methods require that the infrared spectrum and reference glucose levels be obtained from a single blood sample from each subject.
A Method for Generating a Sample of Two Uniformly Distributed Random Variables With Prescribed Coefficient of Correlation

An efficient method for generating samples of two uniformly distributed random variables (RVs) on the interval (− 1,1) with prescribed correlation coefficients is proposed in this article. The need for research arises during the application of the Monte Carlo method (MCM) in determining uncertainty when the input quantities are correlated. The results of the experiment in determining the voltage ratio of a resistive divider are presented. Uncertainty was determined using the guide to the expression of uncertainty in measurement (GUM). The results were confirmed by MCM using the proposed FOLD method.
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding

Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals. For neural audio codecs, however, the objective nature of the loss function usually leads to suboptimal sound quality as well as high run-time complexity due to the large model size. In this work, we present a psychoacoustic calibration scheme to re-define the loss functions of neural audio coding systems so that it can decode signals more perceptually similar to the reference, yet with a much lower model complexity. The proposed loss function incorporates the global masking threshold, allowing the reconstruction error that corresponds to inaudible artifacts. Experimental results show that the proposed model outperforms the baseline neural codec twice as large and consuming 23.4% more bits per second. With the proposed method, a lightweight neural codec, with only 0.9 million parameters, performs near-transparent audio coding comparable with the commercial MPEG-1 Audio Layer III codec at 112 kbps.
Power Flow Reversal Control of Hybrid HVDC Transmission System With LCC and FBMMC

A power flow reversal control strategy is proposed for bidirectional power transmission of LCC and FBMMC hybrid HVDC transmission system. The power flow reversal sequence is designed with integrating DC voltage control and active current control firstly. The proposed dual-loop control strategy for FBMMC effectively eases the FBMMC AC-side power and submodule capacitor voltage fluctuations during the power flow reversal process. The inner current control loop of FBMMC model based on the sliding mode variable structure control theory is established to suppress the influence of the reference value mutation on the system. Finally, a simulation model of hybrid HVDC transmission system with 12-pulse LCC and 51-level FBMMC is built in MATLAB/Simulink, and the improvement of the system dynamic performance is demonstrated.
A Scalable Name Lookup Service for NDN

A lookup service has been identified as an essential part of NDN. NDNS as a name resolution service was proposed since NDN in itself does not have the feature. The NDNS architecture follows the concept of DNS. However, we argue that the architecture will suffer from the scalability issue in the end since it requires orders of magnitude higher performance to deal with content names than with domain names. In this paper, we propose a novel scalable name lookup service for NDN, called NDN Content Identity Service or NCIS, in order to solve it. NCIS allows multiple copies of a content with different names across the network utilizing two different types of distributed hash table. We present the benefits of adopting NCIS into NDN.
Neural Spike Detection Based on 1T1R Memristor

Neural spike detection is an important step to study the working principles of the brain and construct useful neuroprosthetics for patients with neurological diseases. However, the high power consumption of conventional electronic hardware hinders the way towards highly efficient neuroprosthetics. In this paper, an energy-efficient neural spike detector based on 1T1R memristor is presented. 69.12% TPR and 19.35% FPR can be achieved when benchmarked with a conventional method. Compared with traditional neural spike detectors, our memristor-based spike detector has shown significant advantages in both power consumption (84.5 nW compared to 815 nW in conventional hardware) and data transmission bandwidth (~200× compression of raw data).
A Novel FDTD Algorithm for Analyzing Imperfect Microwave Filters

In this paper, we propose a novel finite difference time domain (FDTD) algorithm to design microwave filters with imperfect conductors. For this purpose, we developed a new mesh function to describe the thin film material. Then, we applied our algorithm for the analysis of a microwave resonator performance. The effects of finite conductivity and thin film thickness having also been investigated. Computed results are in good agreement with simulated data using CST EM tool.
Approach of a Solution Construction Method for Mesh Router Placement Optimization Problem

The Wireless Mesh Networks (WMNs) consists of mesh routers and mesh clients connected by multi-hop radio communication. Mesh router provides serves as an access point and communicates with the other mesh routers and mesh clients. On the other hand, placement of the mesh routers affects transmission loss, installation cost, operational complexity, etc. However, optimizing the placement of the mesh routers can resolve these problems and increases the network performance. In our previous work, we considered meta-heuristic based mesh router placement optimization system. In this paper, we propose a solution construction method for mesh router placement optimization problem.
Spatial Harmonic Wave Reduction for Magnetic Field near the Surface of Halbach Array

PMSMs sometimes cause noise and vibration. In many cases, they are due to spatial harmonics contained in the magnetic flux density distribution in the air gap. Halbach array has attracted attention because it can generate a sinusoidal magnetic flux density distribution, however there are small spatial harmonics near the magnets. To reduce them, we considered inserting a non-magnetic partition with a certain width between the magnets. The analytical results of this method show that it reduces the spatial harmonic near the Halbach array.
Multiuser Access via OFDMA Technology in High Density IEEE 802.11ax WLANs: A Survey

IEEE 802.11ax known as HEW (High Efficiency WLANs: Wireless Local Area Networks) is the new standard designed to meet the objectives of the next generation of high density wireless networks, such as: airports, bus stations, stadiums, etc. The key innovative feature introduced in IEEE 802.11ax is the OFDMA (Orthogonal Frequency Division Multiple Access) technology. The basic principle of OFDMA at PHY (physical) layer is dividing a radio channel into smaller sub-channels, and hence enabling multiple and independent transmissions between multiple wireless devices. At MAC (Medium Access Control) layer, the OFDMA technology is thus intended to allow a massive connection of users, while reducing access delay and increasing individual throughput. This is why, several OFDMA MAC protocols, based on different approaches, have been designed and implemented for the purpose of efficiently using the radio resources supplied by the OFDMA technology at PHY layer. In this paper, we aim at: (i) reviewing the main existing OFDMA MAC protocols available in the literature with the goal of highlighting the strengths and weaknesses of each protocol, (ii) proposing a new classification of OFDMA MAC protocols, and (iii) drawing some perspectives for future research.
Impact of Multiple DG penetration in Energy Market

This paper presents the techno-economic benefits of committing single and multiple Distributed Generations (DGs) in the deregulated power market along with their impact on nodal prices. In a deregulated power system due to Transmission Open Access (TOA), it becomes an uphill task for Independent System Operators (ISO) to maintain security and reliability of power system. The DG implementation improves the efficiency of the system by reducing the energy cost. This paper analyses the contributions of single and multiple DGs with different cost functions in the electricity market. The objective of this work is to minimize the total generation cost with conventional and distributed generation along with maximum DG profit. The favorable location for DG incorporation is identified by Linear Programming Optimal Power Flow (LPOPF). The higher LMP method is used to find the candidate location for DG placement. The proposed methodology for the penetration of multiple DG is based on the ratio of the LMPs of the candidate node. The Type1 DG is considered in this study, i.e. can feed in both active and reactive power. The study is performed on the IEEE WSCC 9 bus system.
Test-Driven Requirements Engineering

Requirements and testing belong together. Historically, testers have often seen the requirements only after the system has already been partially implemented. This had two serious disadvantages. On the one hand, insufficient requirements quality came far too late to the table. On the other hand, it was quite a lot of extra work without deriving suitable test cases in the context of the requirements definition. A lot of additional work and long correction loops were the result.
LDPC Codeword Size Determination Using Convolutional Neural Networks

This paper discusses the design and performance of a forward error correction (FEC) code classification system that is used to determine the size of an unknown codeword from a stream of bits. The classification system is a deep neural network that is trained and tested on half rate low density parity check (LDPC) codes. Tests were performed on streams of codewords using codes of up to 250 different sizes. The CNN based classifier performs very well.
PM Hub Motor Design for Electric Two-Wheelers Based on Measured Driving Cycles

A high-efficiency hub motor design targeting the whole speed range for electric 2-wheelers is proposed in this paper, along with the design criteria obtained from the measured driving cycle, which reduces FE calculation time while simultaneously maintaining accuracy. Introducing the vehicle dynamical approach, torque demand and power consumption can be obtained. Representative working points are extracted from the driving cycle through the Butterworth filter and clustering algorithm. The motor design applied these representative points has the potential to achieve high efficiency against whole operating conditions compared to design based on single points.
Quarantine Quibbles: A Sentiment Analysis of COVID-19 Tweets

With the advent of the COVID-19 pandemic, people have flocked to social media in order to stage their thoughts surrounding these unusual circumstances. This paper aims to uncover public sentiment regarding the novel coronavirus pandemic on the microblogging platform Twitter. This is done through a proposed algorithm that builds off of existing aspect-based sentiment analysis approaches and opts for a Naïve-Bayes route to classify existing Tweets that have been atomized into n-grams. This research concludes that overall sentiment regarding the COVID-19 outbreak over July 2020 is a combination of pessimism and dejection as our quarantine denizens take to their online platforms in airing their polemic opinions.
Complex Placement Region Handling Based on Electrostatic System Modeling

With the additional fence region constraints in modern circuit designs, the VLSI placement problem has become more complex and challenging. A placement solution without considering fence region constraints may cause many problems in the legalization stage and result in an inferior placement. In this paper, we present an effective placement region handling method based on the two-dimensional electrostatic system modeling. Under the fence region constrains, we first generate multiple density maps for assigning cells to their respective fence regions. Then, we further add proper fence filler nodes to each density map, which is able to squeeze fence cells to be placed closer. The placement performance is validated through experiments on ISPD 2015 benchmarks. Experimental results show that, compared with the state-of-the-art placer NTUplace4dr, our proposed method not only reduces the wirelength by 9.9% but also achieves 5x faster runtime.
A Seamless, Reconfigurable, and Highly Parallel In-Memory Stochastic Computing Approach With Resistive Random Access Memory Array

A seamless, reconfigurable, and highly parallel in-memory stochastic computing (SC) approach is proposed and experimentally demonstrated in resistive random access memory (RRAM) array. By utilizing the probabilistic switching behavior of RRAM and parallel writing, stochastic bit streams (SBS) are generated in parallel. By employing in-memory stateful logic, parallel stochastic logic is directly performed within the same memory array. This in-memory SC approach has three merits: 1) true stochastic number generator (SNG) can be obtained with low cost, 2) the signal conversion overhead between SNG and stochastic logic is eliminated, 3) it is highly parallel and the stochastic logic is reconfigurable. This work provides an efficient and low-cost alternative approach to achieve SC.
Personalised Food Classifier and Nutrition Interpreter Multimedia Tool Using Deep Learning

Food plays a vital role in our day-to-day life to get all the required nutrients for a healthy lifestyle. In recent years, obesity has become one of the major concerns among humans. Therefore, it is necessary for each individual to keep track of the nutrition intake in order to have a balanced diet. This has scaled up the implementation of automatic food analysis and semantic food detection using different image classification approaches, among which Deep Learning has brought a series of breakthroughs in this field. We have proposed the Food Classifier and Nutrition Interpreter (FCNI), a user-friendly tool that classifies various food types with a different graphical representation of food nutrients values in terms of calorie estimation along with a multimedia audio response. FCNI improves state-of-the-art food detection by a considerable margin on achieving about 96.81% accuracy.
A Series Hybrid Active Power Filter with the function of Adjustable Fundamental Impedance

This paper proposed a series hybrid active power filter (SHAPF) with the function of adjustable impedance. The principle of SHAPF based on magnetic flux compensation is addressed. A linear transformer with air gap and multiple secondary windings is chosen, which are injecting specific currents into multiple secondary windings respectively. The fundamental current of primary winding is detected and function as the initial reference signal. And the initial reference signal would be phase shifted to constitute the second type of current reference signals. The multiple secondary windings are also divided into two types, corresponding to be controlled by the two reference currents, so that the primary winding can almost present short circuit for harmonic currents and continuously variable impedance for fundamental current. Besides, the stability condition is derived and analyzed in detail. The simulation results have verified the effectiveness of the proposed SHAPF with adjustable fundamental impedance.
Application of a Bevel Etch Process for Improving Particle Performance in CMOS Image Sensor Manufacture

Due to the continuing improvements of CMOS image sensor (CIS) technology, the back side-illumination (BSI) structure was involved to overcome optical characteristics deterioration in smaller pixels. In BSI structure, bonding loop is necessary to adhere two wafers. However, multiple dielectric and metal layer on the bevel of the device wafer possibly to be the source of defect as it is so loose that peeling particles will fall on the wafer after trimming step and bubble defect will formed at last. The main objective of the work is to improve peeling particle performance. After insertion of bevel etch, the number of peeling particles was reduced to less than 5ea compared with more than 300ea without bevel etch. Ultimately, no more bubble defect was found due to peeling particles clear off. What's more, we also discuss the mechanism of peeling particles and bubble defect forming. And the roles of different components of the gas mixtures in the bevel etch process.
Investigate Axial Dynamization Movement by Using Genetic Algorithm Method Based on a Unilateral External Fixator

Unilateral external fixators are commonly used to stabilize the fractured tibia bone. Compared with static fixation, axial dynamic motion can be used for promoting callus formation, improving bone healing at fracture sites. Moreover, non-axial motions are not conductive to promoting bone healing and remodeling. However, a fixator-bone system with 7(DOFs) can not achieve both fracture correction and axial dynamic motion. Maybe it is easy to think that this problem could be solved by adding one DOF for fixator structure. However the increased structural flexibility could not be able to meet the demands of lower cost and size, and higher stiffness. Thus, how a fixator-bone system with 7 DOFs not only can realize fracture correction, but also complete axial dynamic motion as far as possible. So far, researchers have never solved this problem. Therefore, we establish a mathematical model based on the 7 DOFs Orthofix fixators, and try to use the genetic algorithm method to reduce the tangential displacements in the process of axial dynamization after fracture correction. The results suggest that the use of genetic optimization algorithm can effectively reduce tangential displacement. This study helps facilitate appropriate and flexibility application of fixators to better control axial dynamic motion and decrease tangential displacements.
Development of Biofeedback Device Using Optimal EMG Frequency Band for Prevention Muscle Fatigue

In recent years, the population is aging in Japan, the cost of disease prevention and personal medical care is increasing. There, elderly people are required to have physical strength and walking ability as much as possible. In people with limited mobility, muscle strength declines with age, and walking movement becomes insufficient, making walking difficult in daily life. So, to prevent weakness in the elderly, gait is promoted and light exercise is recommended. In previous studies examining fatigue with MPF, but there is no device that react the condition in real time and feeds back the fatigue condition. The purpose of this research is “Development of Biofeedback Device on Optimal EMG Frequency Band for Prevention Muscle Fatigue”. In this study, MPF was examined in several sections in order to examine the optimum frequency band.