Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy-efficient Processing-in-memory Architecture for Long Short Term Memory in Spin Orbit Torque MRAM

Many recent studies have focused on Processing-in-memory (PIM) architectures for neural networks to resolve the memory bottleneck problem. Especially, an increased interest in Spin Orbit Torque (SOT)-MRAMs has emerged due to its low latency, high energy efficiency, and non-volatility. However, the previous work added extra computing circuits to support complicated computations, which results in large energy overheads. In this work, we propose a new PIM architecture with relatively small peripheral circuit, which produces the highest energy efficiency for processing a Long Short Term Memory (LSTM) among the PIM architectures. We improve the efficiency with a new computing method for logical operations, which exploits characteristics of SOT-MRAMs. We reduce the number of word lines (WLs) activated concurrently to one from two in the previous works. As a result, the energy for driving WLs is saved, and the sensing current for computation is reduced. Moreover, we propose efficient methods for additions, multiplications and non-linear activation functions in memory to process an LSTM. Accordingly, we achieve 1.26x energy efficiency with the proposed computing method for logical operations compared to the previous study based on SOT-MRAMs and up to 5.54x energy efficiency over the previous PIM architectures based on other memories.
Bimodal Skin Cancer Image Segmentation Based on Different Parameter Shapes of Gamma Distribution

Cancer describes a type of malady distinguished by uncontrolled growth and division of abnormal cells. Skin cancer is one the most commonly diagnosed cancer. Image-based computer aided diagnosis (CAD) systems have significant potential for screening and can help discover cancer in its earlier stages. The bottleneck of the CAD system is the skin image segmentation. Image thresholding techniques are the most used for image segmentation. Statistical approaches are widely used in image thresholding. Due to the simplicity of its mathematical formula, different skin image thresholding techniques used Gaussian distribution as a model of data image. However, that distribution has a limitation when the histogram modes of the image has non-symmetric shapes. Gamma distribution has symmetric and non-symmetric shapes and has been used to improve the skin image thresholding. That technique assumed that the shape of each mode in the histogram is constant. However, the shape of each mode in the skin image histogram can be vary inside the image itself. In this paper, our contribution is to use different parameter shape for each mode in order to improve the quality of skin cancer image segmentation. Experimental results showed that the improved technique has better results than existing techniques using performance measures.
A General Logic Synthesis Framework for Memristor-based Logic Design

Memristor-based logic design gives an alternative solution to improve the energy efficiency of computing systems, benefiting from combining the memory with computing units. Inspired by this thought, previous work has demonstrated various memristor-based logic families with different attributes and computation patterns. Besides, some logic synthesis tools are designed for specific memristive logic implementations. However, the poor universality and the neglect of realistic constraints in memory largely restrict the utility of these logic synthesis tools. In this paper, we propose a general logic synthesis framework for memristor-based logic design, containing a universal abstract description method for memristive logic, a mapping rules generator, and a synthesis and mapping flow. The proposed logic synthesis framework is suitable for various types of existing memristor-based logic families and takes the memory status into consideration. It is also possible to handle future memristive devices and logic families by providing the universal abstraction interface. Furthermore, we also design a circuit-partitioning-based synthesis acceleration strategy to tackle with the long synthesis time problem. Experimental results show that, our framework can generate mapping results under the restriction of limited resource, while the existing synthesis tools may fail under the same restriction, and achieve comparable synthesis results with the same resource as the existing synthesis tools, which is enough for computation and storage. And the proposed acceleration scheme can achieve ∼ 1000× speedup compared with the initial one.
Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders.
Affine Projection Algorithm by Employing Maximum Correntropy Criterion for System Identification of Mixed Noise

The adaptive algorithms have been widely studied in Gaussian environment. However, the impulsive noise and other non-Gaussian noise may largely deteriorate the performance of algorithm in practical applications. To address this problem, in this paper, we propose two novel adaptive algorithms for system identification problem with mixed noise scenarios. Both proposed algorithms are based on the framework of the affine projection (AP) algorithm. The first proposed algorithm, termed as VS-APMCCA, combines variable step-size (VS) strategy and maximum correntropy criterion (MCC) to obtain improved performance. For further performance improvement, the VC-VS-APMCCA is developed, which is based on the variable center (VC) scheme of MCC. The convergence analysis of the VC-VS-APMCCA is conduced. Finally, simulation results demonstrate the superior performance of the VS-APMCCA and VC-VS-APMCCA.
Double-Tiered Cell Balancing System With Switched-Capacitor and Switched-Inductor

By considering the impact of parasitic resistance of inductors and capacitors employed in buck-boost and switched-capacitor types of cell balancing systems, a double-tiered cell equalizer is developed in this work. The first tier of this system is derived from buck-boost cell equalizer and the second tier is implemented by a switched-capacitor equalizer which has fast equalizing speed. The two tiers share a common switch array controlled by one pair of complementary square wave signals. The number of switches is therefore reduced significantly and there is no voltage nor current signals needing to be sensed for complex close-loop control. Circuit configuration, modeling and design considerations are illustrated in detail. Both simulation and experimental results are provided to demonstrate the feasibility of the proposed cell balancing system.
Low Power Wide Area Network Technologies for Smart Cities Applications

There are several existing Low Power Wide Area Network (LPWAN)-technologies competing to provide M2M connectivity and secure niche in the Internet of Things (IoT) universe. Most of well-known LPWAN approaches are quite recognizable thanks to sizable marketing efforts though most of it practically is not the right choice for the robust long-range and large-scale networks. We have compared the famous LPWAN technologies to provide a complete overview of the different perspective. From the comparison, the considering candidate to be used in the Smart Cities Application is then revealed based on its advantages.
Toward improving collaborative behaviour during competitive programming assignments

Competitive gamification has been successfully used to improve the learning experience in computer science courses. Assignments based on high-performance and parallel programming contests have been shown to be highly effective to promote the interest and involvement of students. Nevertheless, during programming-contest assignments the competitive approach sometimes diminishes the usual collaboration between students to solve basic questions and common difficulties; for example, those related to the usage of the programming languages or tools. In this paper, we present an approach to integrate collaborative gamification techniques with the competitive elements of programming contests, in order to balance both aspects and to enhance the overall learning experience. The results show that this approach effectively promotes participation and collaborative behavior during a programming contest. They also reveal potential improvements that can be considered for this approach.
Integrating a Real Vehicle Into a Physics-Based Driving Simulator for Human-Machine Interaction Research

Driving simulators allow quick, safe and repeatable way of testing how a particular system behavior or design would be accepted by drivers. This is especially important when testing Human-Machine Interaction (HMI). As the fidelity of a driving simulator is increased, the quality of obtained data increases as well. However, high-fidelity driving simulators typically require significant financial investments and may not be very flexible in integrating with existing systems. In this paper we intend to reach middle ground by proposing a medium-fidelity driving simulator, which provides both flexibility and reusability of the commonly used automotive components.
Condition Monitoring of Insulators for Ageing Detection Via Magnetic Field Measurements

The insulators have proved to be the major part of the transmission and distribution system in terms of mechanical and electrical protection. The temperature variation and atmospheric behavior are responsible for rising the aging phenomenon of insulators. Arcing or chattering is one of the faults occurring a phenomenon in the rainy seasons or polluted environment which is vulnerable to the power system operation and for the population in the closed premises. Many methods for the ageing of insulators have been developed which are either destructive or non-destructive. This paper emphasizes to sense the magnetic field variations in the polluted environment. The tool used for environment development is Ansys Maxwell. The 3d model has been designed and the excitations are given by Maxwell circuit editor. Various sensor positions have been shown and the calculations of sensors at these positions have been discussed for the optimal placement of the sensor. Moreover, the sensors have been discussed for enhancing the importance of work in terms of designing a testbed.
Deep Learning on VR-Induced Attention

Some evidence suggests that virtual reality (VR) approaches may lead to a greater attentional focus than experiencing the same scenarios presented on computer monitors. The aim of this study is to differentiate attention levels captured during a perceptual discrimination task presented on two different viewing platforms, standard personal computer (PC) monitor and head-mounted-display (HMD)-VR, using a well-described electroencephalography (EEG)-based measure (parietal P3b latency) and deep learning-based measure (that is EEG features extracted by a compact convolutional neural network-EEGNet and visualized by a gradient-based relevance attribution method-DeepLIFT). Twenty healthy young adults participated in this perceptual discrimination task in which according to a spatial cue they were required to discriminate either a "Target" or "Distractor" stimuli on the screen of viewing platforms. Experimental results show that the EEGNet-based classification accuracies are highly correlated with the p values of statistical analysis of P3b. Also, the visualized EEG features are neurophysiologically interpretable. This study provides the first visualized deep learning-based EEG features captured during an HMD-VR-based attentional task.
Dynamic Diagnosis Approach of Multi-state Degradation System Using Hidden Markov Model

A methodology for developing dynamic diagnosis of multi-state degradation system was proposed in this paper. Wavelet packet energy entropy was employed to characterize the uncertainty and complexity of the signal. Current state evaluation and multi-state recognition had been implemented by hidden Markov model. The recognition performance was verified by a bearing vibration experiment, and the effects of decomposition levels and wavelet mother functions on the recognition performance were taken into account. Compared with classifiers of K-means, BP neural networks (BP-NN) and support vector machine (SVM), hidden Markov model (HMM) achieved a better recognition performance for multi-state degradation system and provided theoretical explanation of the system failure evolution.
Modeling of Passive Intermodulation Distortion Using the Neural Networks and the Cubic Volterra Filter

In this paper, modeling of the third-order passive intermodulation distortion (PIMD) caused by the downlink signal in the frequency division duplex (FDD) wireless communication system is developed with the Focused Time Delay Neural Network (FTDNN) and the cubic Volterra Filter (CVF). PIMD is an interference signal generated due to the nonlinearity of a passive element in a wireless communication environment, and degrades the noise level of a receiver, resulting in a deterioration of communication quality. Estimation and performance evaluation of the proposed approaches are investigated with measured PIMD signals and transmitted downlink signals. It is observed that both FTDNN and CVF are suitable for modeling non-linearity of the PIMD generation process.
Integrated Interleaved Boost LLC Resonant DC-DC Converter with Voltage Quadrupler Rectifier Circuit for PV Power Systems

In this paper an integrated interleaved Boost-LLC resonant dc-dc converter with voltage quadrupler circuit is proposed for PV power system. It is composed on interleaved boost converter and a full bridge LLC circuit. Voltage Quadrupler circuit is used at the secondary side to boost the secondary side voltages. The operational principal of proposed circuit and its main characteristics such as input current ripple, zero voltage switching (ZVS) and gain are also presented in this paper. The converter has high gain & high power conversion efficiency with less current ripples & voltage stresses. Power MOSFETs are used as switches and proposed scheme is compared with the existing conventional converter circuits. This proposed IIB-LLC-VQ converter with 40-60V input voltage and 400V output voltages is simulated using ORCAD PSPICE simulation software and results are presented in this paper.
GAN-Based Image Augmentation for Finger-Vein Biometric Recognition

Deep learning methods, and especially convolutional neural networks (CNNs), have made a considerable breakthrough in various fields of machine vision, basically by employing large-scale labeled databases. However, deep learning methods applied in finger-vein area are basically implemented on small-scale datasets, which are probably faced with challenges such as overfitting, susceptible to finger position, unstable performance on various datasets and son on. In this study, we present a lightweight and fully convolutional Generative Adversarial Network (GAN) architecture, which is named FCGAN, using preliminary batch normalization, and tightly-constrained loss function for implementing finger-vein image augmentation. In addition, we present a novel scheme FCGAN-CNN for finger-vein classification, which reveals that synthetic finger-vein images using FCGAN are capable of improving the property of CNN for finger-vein image classification. The experiment of sample augmentation shows that the training accuracy using FCGAN-augmented samples could go beyond 99%, which is higher than 96.34% obtained using only classic sample augmentation. Furthermore, the well-trained CNN is further evaluated on a totally different dataset, which indicates that the proposed scheme FCGAN-CNN is capable of improving the ability of CNN to extract deep features. We consider that the proposed method for sample augmentation could be extended to other biometric systems.
Uncertainty-aware Deep Learning Forecast using Dropout-based Ensemble Method

A probabilistic forecast technique provides useful information showing how confident a point forecast is. However, building a good probabilistic forecast technique that is easily applicable to existing deterministic forecast techniques is not easy because it may require a case-specific or complicated method, as we observed in existing studies. In this paper, we propose a probabilistic forecast technique that is generally applicable to existing deterministic forecast techniques. Focusing on deep learning, which has shown promising results in various areas including forecasting, we achieve a generality by using a dropout technique that is widely applied in deep learning architectures. We first show how the dropout technique is utilized to realize an ensemble method. Then, through experiments using the real-world data, we describe an uncertainty metric, which is related to the forecast accuracy, and show that a lower uncertainty metric value is likely to indicate a higher forecast accuracy.
Optimized Shuttlecock Propulsion Machine to Facilitate Badminton Training

This research project is based on designing a Shuttlecock Propulsion Machine to train badminton players. Badminton requires at minimum two players and it is impossible for an individual to practice badminton without support of another player. To allow an individual to train independently, a Shuttlecock Propulsion Machine has been developed. The Machine comprises a shuttlecock holder, a feeding mechanism, an ejecting portion and a tripod stand. This machine utilizes a flywheel mechanism in the ejecting portion for the propulsion of the shuttlecock. The machine involves two flywheels rotated by two high speed DC motors. The shuttlecock holder consists of a PVC duct and ascends in an inclined sequence to tilt shuttlecocks with hemispherical cork side over the feeding mechanism. The shuttlecock would be captured by the Robotic Jaw Gripper and fed into the ejecting portion. The flywheel diameter has been kept large enough to store maximum inertia for the propulsion of shuttlecock. The main aspect of the machine is that it can move in multiple axes. This machine design optimizes the speed of launching a shuttlecock and can be manufactured at a reduced cost compared to the similar machines available in the market.
Android Malware Similarity Clustering using Method based Opcode Sequence and Jaccard Index

Recently, Android malicious code shows a trend to spread a large number of variants in a short time. This type of malicious code is mainly produced by reusing existing code, and the number of malicious codes has increased year by year despite the increased security of the OS. In this paper, we propose a clustering and classification method for Android malicious codes using a similarity algorithm in a lightweight manner. In this paper, among the APK internal files, Dex file is disassembled to extract Opcode Sequence in Method unit, and clustered similar Android malicious code with Jaccard Index using N-gram. Clustering results of Android malicious code collected in 1Q 2019 are presented and analyzed.
An Enhanced Random Access Algorithm Based on the Clustering-Reuse Preamble Allocation in NB-IoT System

As a new wireless access technology, Narrow Band IoT (NB-IoT) enables low-power wide-area coverage services and can be applied to many domains. Emerging applications such as smart traffic, shared bicycles and shared cars have put forward more stringent requirements for connectivity. In order to get higher QoS, an enhancement access algorithm is proposed for the complex service scenario of NB-IoT, in which the network terminals are clustered based the distance from base station and terminal traffic intensity, and allocated reuse preamble code to improve network access capacity. The capacity is modeled and analyzed to evaluate the enhanced access algorithm based on clustering-reuse preamble allocation (ERA-CRPA). The experimental results show that the ERA-CRPA can effectively reduce the probability of random access preamble collision, further improve the access efficiency and increase the equivalent capacity of NB-IoT network.
3-Phase High Gain PFC Converter Implementing Switched Capacitors

In this paper a novel topology of three phase Power Factor Correction (PFC) implementing switched capacitor (SC) is proposed. This converter comprises of separate switched capacitor cell for each phase. This converter has single switch per phase, transformer less but attains high static gain with switched capacitor cells, attains good power factor, reduces total harmonic distortion (THD) and each switch is subjected to a voltage stress equal to half the output voltage. In this converter all the active components are subjected to voltage stress equal to half of the output voltage. The rectifier input of pole voltage has three level voltages. Dynamic resonant perturbation (DPR) method is employed to stabilize the sub harmonics and to attain the unity power factor and low harmonic distortion. This paper presents circuit topology, its operation and control for the proposed three phase PFC converter. Simulation studies have been performed to ascertain the operation of the converter.
The Role of Trust in Determining Consumers’ Intention to Click on Online Personalized Ads

This paper aims to see the effect of trust on retailers in moderating private responses, namely perceived usefulness, reactance, and privacy concern when faced with personalized advertising among consumers of portable/ wearable electronic products in Jabodetabek. To achieve the goal, we proposed a conceptual model that combines Ducoffe’s advertising value model and model proposed by Bleier and Eisenbeiss. Based on data collected from 438 respondents who were selected based on several considerations, the model is being tested using a Structural Equation Modeling (SEM) estimation with LISREL 8.51 software. The results showed that the level of trust in retailers significantly increased positive internal responses, such as perceived usefulness and reduced negative internal responses such as reactance and privacy concerns. These innate responses cannot be directly translated into click intention on the respective digital ads since consumers still have certain limitations in disclosing their private information. Even for the retailers, they believe before the ads considered as an act of privacy intrusion or personal freedom revocation. This study provides results that allow advertisers, marketers, and scholars to understand how trust plays a crucial role in determining consumers’ intention in visiting a personalized digital ad, specifically in a region that currently does not possess a comprehensive data privacy law.