Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiation Effects in XOR Logic Gates at 16nm CMOS and FinFET Technology

Circuits are becoming more susceptible to radiation effects due to technology scaling. This work presents a comparative analysis of radiation sensitivity for different topologies of XOR gates at 16nm. The gates are implemented considering two devices: Complementary Metal-Oxide Semiconductor (CMOS) and Fin Field-Effect Transistor (FinFET) and two logics: Complementary Logic (CMOS logic) and Pass-Transistor Logic (PTL). To allow a more detailed comparison, this work also discusses the delay and power results for each XOR version. XOR gates based on PTL have shown more robustness against radiation effects when compared with the gates implemented with CMOS logic, with a Linear Energy Transfer (LET) threshold being almost 30% higher for CMOS devices and up to 20% greater for FinFET devices. Furthermore, FinFET-based circuits are about three times more robust than CMOS technology, with an improvement on the LET threshold for both logic families evaluated.
Doppler Resilient Sequences with Low Local Ambiguity Functions for Fully Polarimetric Radar Systems

In this paper, sequences with low local ambiguity functions are presented by analyzing the matrix-valued ambiguity function in a fully polarimetric radar system, and then generated by majorization minimization algorithm. The two sequences, formed from the obtained Quasi Z-complementary pair whose transmission orders are determined by the Prouhet-Thue-Morse sequence and the Alamouti matrix, can result in a low local autoambiguity function in a modest Doppler shift interval and a very low local cross-ambiguity function among all Doppler shifts.
Predicting Palm Oil Price Direction using Random Forest

The palm oil market in Myanmar greatly depends on the world palm oil price changes, especially on the price changes in the export countries of palm oil to Myanmar. As palm oil market in Myanmar represents the back bone of Myanmar Edible Oil Dealers Association (MEODA), we propose the predictive model to aid in decision making process of palm oil importers whether they should conduct import transaction today or not in this paper. This prediction of palm oil price condition in Myanmar has been taken on the previous dataset supported by MEODA to eliminate ever-increasing risks and uncertainties in the future. This model will forecast whether the price of palm oil in Myanmar will rise or not in 14 days from today, the length of period is necessary to be ready to trade imported palm oil in local market for Myanmar importers. Our model is trained using C4.5 Random Forest Classification Algorithm on the palm oil market dataset from MOEDA. Hyperparameter tuning techniques are conducted to analyze whether the predictive performance can be enhanced. From the obtainable dataset in Myanmar palm oil market, the predictive model with chosen hyperparameters set achieves the prediction accuracy of 91.11% on the test dataset.
Auxiliary Diagnostic Model of Atrial Fibrillation Based on Decision Tree

Atrial Fibrillation(AF) is one of the main causes of multiple cardiovascular disease's birth and death, which has prompted researchers to study more accurate predictive models to assist doctors in screening patients with AF. In this paper, the RCDT (Random-Cost-Decision Tree) algorithm is presented, and it is applied to the data mining of AF, which construct an assistant diagnostic model of atrial fibrillation . Compared with the scores of the four types of high-risk groups for the prediction of AF, the model proposed in this paper had the best performance, with the accuracy rate reaching 88.76%, the sensitivity reaching 95.5%, and the area under the ROC curve reaching 92.77%.
Effects of Semantic Gaps on Arabic WordNet-Based Similarity Measures

The EnglishWordNet (EnWN) is extensively used by computational linguists as a lexical-semantic resource for Natural Language Processing and Knowledge Engineering. EnWN was extended with multilingual information in several non-English wordnet projects; including the Arabic WordNet (ArWN). ArWN is still limited in terms of semantic structure and coverage of the Arabic language. This paper focuses on the improvement of ArWN semantic structure, the Hypernymy relation. An interactive cross-lingual mapping approach is used to enrich ArWN with new synsets lexicalized in Arabic and mapped to the target synsets in EnWN. The paper also studies to which extent the richness of the semantic structure of the wordnets affects the semantic evidences, which can be utilized in wordnet-based applications. A set of experiments in the context of semantic similarity is conducted on Arabic word pairs dataset to examine the effectiveness and usability of the enriched ArWN resource. The experimental performance measures showed that filling in the semantic gaps significantly improves the quality of the similarity scores between concepts.
Development of an Electromyography-Based Hand Gesture Recognition System for Upper Extremity Prostheses

The loss of a limb or a fraction of it, which is either due to a congenital malformation or a surgical amputation over an accident or a disease, puts an enormous amount of strain on the person both emotionally and financially speaking. The overall aim of this work is to contribute in the development of a low-cost hand prosthesis. In this paper, a system for identification of two gestures (wrist flexion and wrist extension) by decoding surface electromyography (EMG) signals of amputees is proposed. The acquisition circuit developed has 2 simultaneous channels and provides good quality resulting signals on output. A Database has been created and filled with signals acquired from 20 healthy subjects. For analysis of the signals, five level DWT decomposition was used, and for classification ANN was employed. After the analysis of data, we were able to identify and detect the movements. The method demonstrated has shown an overall suitable accuracy around 80% and can further be used for the prosthesis.
Device-to-System Level Simulation Framework for STT-DMTJ Based Cache Memory

This paper presents a comparative study on non-volatile cache memories based on nanoscaled spin-transfer torque (STT)-magnetic tunnel junctions (MTJs). In particular, the impact of using double-barrier MTJs (DMTJs) instead of conventional single-barrier MTJs (SMTJs) is evaluated through a device-to-system level simulation framework. Simulation results demonstrate that DMTJ-based STT-MRAMs are promising competitors for the next generation of non-volatile cache memories.
A Heterogeneous Graph Embedding Framework for Location-Based Social Network Analysis in Smart Cities

In recent years, with the advancement of wireless communication and location acquisition technology in the context of modern smart cities, and the increasing popularity of mobile devices with location capabilities based on Social Internet of Things, we can now easily introduce location services into traditional social networks. How to effectively use these massive data to provide decision support for smart cities is an emerging task. It is necessary to find an efficient way to effectively extract and represent useful information from location-based social networks (LBSNs) by dealing with the data heterogeneity. Against this background, this article proposes a heterogeneous graph embedding framework for LBSN analysis named location based social network embedding (LBSNE). LBSNE framework first constructs the heterogeneous neighborhood of a node by formalizing a metapath-based random walk on LBSNs. Then, it leverages the learned heterogeneous neighborhood sequence to do network embedding by employing a heterogeneous skip-gram model. The effectiveness of the proposed model is evaluated on the tasks of location recommendation and visitor predict on two LBSN datasets.
Research on Network Evolutionary Spreading Model of Literary and Artistic Works

This paper proposed a new type of propagation model from the perspective of complex networks, and discusses the propagation characteristics of literary and artistic works in complex networks with users' relationship. In this paper, the dynamic propagation model SIDR is constructed and simulated, to analyze the propagation mechanism and evolution process of the information dynamics. Moreover, the paper studies the influence mechanism of media propaganda and user preference in the process of spreading, which is a significant contribution in predicting and controlling the dissemination trend of literary and artistic works.
Human Gait Recognition Based on Multi-Feature Fusion and Kinect Sensor

Gait recognition has gained increasing attention from biometric scholars because of its applications in video surveillance. A novel approach to recognize human gait is presented based on multi-feature fusion and nearest neighbor classifier. The developed method firstly extracts 3 kinds of features from gait skeletal data by exploiting Kinect followed by calculating distances between any 2 gait sequences corresponding to these features. In what follows, weighted summation can be applied to acquire one fusion that is used in nearest neighbor classifier to recognize any gait sequence. The effectiveness of this gait recognition algorithm is verified on UPCV gait database and the experimental results show that the method has encouraging recognition rate.
A Fuzzy Adaptive Control Strategy for Active Suspension Systems with Unknown Dynamics

A fuzzy adaptive control method is developed for active suspension systems with unknown dynamics. The controlled object is a single-input multiple-output five-order active suspension system with electromagnetic actuator. Fuzzy logic systems (FLSs) are utilized to identify the unknown nonlinear dynamics. Based on the back-stepping design principle and Lyapunov stability theory, the proposed control scheme is proved to guarantee that the closed-loop system is stable. Simulation results are presented to demonstrate the effectiveness of the developed control strategy.
Joint Subchannel Allocation and Power Control in OFDMA Femtocell Networks

In this paper, we investigate the problems of joint sub-channel allocation and power control in Orthogonal Frequency Division Multiple Access (OFDMA) based on two-tier networks. First, we propose the power control problem to minimize the transmission power of macrocell in half-duplex (HD) mode. Then we investigate the joint sub-channel allocation and power control problem to maximize the total throughput of femtocells in full-duplex (FD) mode, subject to constraint of quality-of-service of delay-sensitive users (DS) and constraint of co-tier, cross-tier and other interferences caused by full-duplex transmissions. Also, femtocells are able to switch between HD and FD modes. Finally, to solve these problems, we propose suboptimal and distributed algorithms for macrocell and femtocells. The simulation results demonstrate that the proposed algorithms improve the total throughput compared to existing algorithms.
Topology-Based Clusterwise Regression for User Segmentation and Demand Forecasting

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
Properties of Knuth’s Quadratic Congruential Sequences with Modulus m = 2W p

In this paper, we give some important characteristics of Knuth’s quadratic congruential sequences with a particular modulus as a pseudorandom number generator. For modulus m = 2W p where p is an odd prime, we derive some periodic relations of the sequence by using analysis results shown by Iwasaki and Umeno for their one-stroke polynomial. Accordingly, we have theoretical analysis for randomness of these sequences and evaluate pseudorandom number outputs by these sequences by the NIST statistical tests.
Fuzzy Neural Networks (FNNs) Training Algorithm With Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS)

Fuzzy neural network (FNN) often suffers from overfitting problem, especially when FNN has large number of parameters. In the FNN system, there are two types of adjustable parameters, one is control parameters, and the other is link weights of consequent part. To improve convergent rate, Dropout technique is first adopted for Fuzzy neural network. A new training algorithm with dropout technique for FNN is proposed via its equivalent F-CONFIS. Illustrative examples are provided for checking the validity of the proposed method. Simulation attained satisfactory results. Proposed method for Fuzzy neural network via F-CONFIS has its rising values in all practical applications, such as system identification, expert System and image information processing system …, etc.
Clustering based Image Segmentation via Weighted Fusion of Non-local and Local Information

In this paper, we introduce a novel and effective clustering model combining the non-local and local information for the image segmentation. Recently, the non-local information has attracted much attention in the area of image processing for its excellent ability to handle the noise. Specifically, in this new model, we do an automatically weighted fusion of the non-local and local information of the image in the objective function of K-means. Thus, in the smoothing areas, the non-local information reduce the impact of noise in the region; and in the edge of regions, the local information help to keep the image details. The proposed model is a general optimization problem which can be solved by the iterative refinement technique like fuzzy c-means or K-means, and it can automatically balance the contribution of non-local and local information. Verified by the experimental results on image segmentation, the proposed model is effective to improve the performance of clustering.
100 Gb/s/ $\lambda$ Duo-Binary PAM-4 Transmission Using 25G Components Achieving 50 km Reach

We demonstrate 100 Gb/s/<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> duo-binary PAM-4 (DB-PAM-4) transmission over 50 km standard single-mode fiber (SMF) in the O-band using a 20G silicon-based transmitter and a 25G optically-amplified PIN+TIA-based receiver for 100G PON downstream applications. Receiver sensitivities of −15, −14.5 and −12 dBm are achieved at a bit error rate (BER) below the low-density parity-check forward error correction (LDPC-FEC) threshold (i.e., <inline-formula> <tex-math notation="LaTeX">$1\times 10^{-2}$ </tex-math></inline-formula>) in back-to-back (B2B), and after 20 and 50 km, respectively using a 131-tap linear feed-forward equalizer (FFE) at the receiver. According to the results, a power budget of 20 dB can be achieved for 20 km transmission. Furthermore, we report the performance of 99 Gb/s (33 Gbaud) PAM-8 transmission and show that DB-PAM-4 provides 5.3 dB better sensitivity compared to PAM-8 at the LDPC-FEC threshold.
Persian Classical Music Instrument Recognition (PCMIR) Using a Novel Persian Music Database

Audio signal classification is an important field in pattern recognition and signal processing. Classification of musical instruments is a branch of audio signal classification and poses unique challenges due to the diversity of available instruments. Automatic expert systems could assist or be used as a replacement for humans. The aim of this work is to classify Persian musical instruments using combination of extracted features from audio signal. We believe such an automatic system to recognize Persian musical instruments could be very useful in an educational context as well as art universities. Features like Mel-Frequency Cepstrum Coefficients (MFCCs), Spectral Roll-off, Spectral Centroid, Zero Crossing Rate and Entropy Energy are employed and work well for this purpose. These features are extracted from audio signals out of our novel database. This database contains audio samples for 7 Persian musical instrument classes: Ney, Tar, Santur, Kamancheh, Tonbak, Ud and Setar. In feature selection part, Fuzzy entropy measure is employed and classification task takes place by Multi-Layer Neural Network (MLNN). It should be mentioned that this research is one of the first researches on Persian musical instrument classification. Validation confusion matrix made of true positive and false negative rates along with true and false observations numbers. Acquired results are so promising and satisfactory.
License Plate Location Based on Combination of Deep Learning and Feature Fusion

License plate location is the key step of license plate recognition. In order to improve the precision of license plate location, a license plate location method based on deep learning and feature fusion is proposed in this paper. Firstly, the color feature of license plate is extracted by histogram backprojection method, and the texture feature of license plate is extracted by Sobel Operator. Combining these two kinds of features, the obtained connectivity regions are used as the candidate regions of license plate. And then ResNet20 convolutional neural network is used to identify the candidate regions and locate the license plate. The test results show that the method can satisfy the precise license plate location under the different shooting angles and illuminations, and lay a foundation for improving the accuracy and robustness of license plate recognition in the future.
Empirical Testing of Automotive Cyber-Physical Systems with Credible Software-in-the-Loop Environments

Automotive cyber-physical systems are constantly increasing in complexity, especially due to innovations like sophisticated advanced driver assistance features. The increase in system complexity, in turn, gives rise to complex distributed software which creates challenges for verification. Front-loading tests that are regularly performed in prototype vehicles or Hardware-in-the-Loop (HiL) to simulation and Software-in-the-Loop (SiL) environments can be used to validate design decisions and to significantly reduce overall development costs. Novel Automated Driving features, and the Open Context problem, however, move the challenge from the state-to-the-art to a knowledge problem (know-what instead of know-how). The ISO/PAS 21448:2019 for Safety of the Intended Functionality (SOTIF) acknowledges this change but no guidance is provided to the industry to making verification processes ready for operating vehicles in an Open Context environment that may require functional changes during the useful life of a vehicle. Since verification with HiL or vehicles will be all but impractical, in this paper we provide insights into the design of credible SiL environments that address functional and non-functional verification and validation concerns of software related automotive system in a continuous life-cycle. With the help of a use-case we demonstrate the significance of the novel approach compared to traditional automotive industry methods.
Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

The bi-directional linkage between the power grid and electric vehicles (EVs) enables flexible, cheap and fast-responding use of vehicle batteries in the grid. However, the battery aging effects due to the additional operation cycles caused by Vehicle-to-Grid (V2G) service and the concern of the battery degradation are the main reason that keeps the customer from being the named prosumer of the grid. This paper proposes a novel active battery anti-aging V2G scheduling approach. Firstly, to evaluate the battery aging effect in V2G service, the battery degradation phenomenon is quantified by a novel use of rain-flow cycle counting (RCC) algorithm. Then, the V2G scheduling is modeled as a multi-objective optimization problem, in which the minimal battery degradation and grid load fluctuation are designed as the optimization objectives. Finally, a multi-population collaborative mechanism, which is particularly designed for the V2G scheduling problem, is also developed to improve the practicability and performance of the heuristic optimization based V2G scheduling method. The proposed methodologies are verified by numerical analysis, which highlights that the proposed V2G scheduling method can minimize battery charge/discharge cycles by optimizing the time and scale of each V2G participant while providing the same services to the grid as expected.
Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.
Evaluating the Impact of Accuracy Relaxation in the Reliability of GPU Register Files

Thanks to the high computing power, GPUs have joined application domains where reliability is a major concern. Faults on electronic components are mainly caused by energized particles, which may cause malfunction and make them result in incorrect output. Errors may be unacceptable for most applications but a small margin of error can be considered safe in some cases. This work uses an approximate computing perspective to analyze the influence of application accuracy relaxation in GPU register files reliability. We perform a fault injection campaign in a Kepler GPU to identify registers' vulnerability and the impact on resulting data. Results show increase in register file reliability in an average of 71.6% for 1% of application accuracy relaxation.
Psychological Explanation of Phubbing Behavior: Smartphone Addiction, Emphaty and Self Control

Phubbing is the behavior of ignoring someone in a social environment by turning attention to the telephone (such as to reply to messages or see notifications in their smartphone). This study aims to explain the psychological explanation for phubbing behavior. We hypothesized some factors that contribute to phubbing are smartphone addiction, empathy, and self-control. The participants in this study were 246 college students who use smartphones in their daily life. Hypothesis test results show the value of RSquare = 0.496, meaning that the proportion of the variance of phubbing behavior explained by all independent variables is 49.6%, while 50.4% is influenced by other variables. There are three sub-variables that have a significant positive influence on phubbing behavior, namely norms, withdrawal, and tolerance, and one variable that has a negative significant influence, namely self-control.