Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Circuit Implementation of a two-layer Memristive Neural Network for Pattern Recognition

In this paper, a fully circuit implementation of Memristive Neural Network (MNN) is proposed. The forward calculation of the network is based on winner-take-all (WTA) mechanism. The weight updating is achieved through the difference of pre-spike and post-spike, which is more close to the biological weight adjustment mechanism. The network is implemented in a full circuit without additional control units. The designed circuit consists of four modules. The memristive crossbar array module with one-memristor (1M) unit structure can effectively calculate the vector-matrix multiplication with only one step. The switch S is replaced by the transistor in the designed leaky-integrate-and-fire (LIF) module, which can control the integration and leakage of the membrane voltage and realize the lateral inhibition between output neurons. Connecting the integrated monostable trigger and the difference circuit, the post-spike generating module can output the required post-spike. The signal switch module realizes the switching of signals connected to memristors by using voltage-controlled switches. The combination of two modules validly realizes weight updating. The functions of four modules are verified separately. We performed a simulation experiment of 5×3 pixels image classification based on the designed circuit in PSPICE. The circuit output results and the high classification accuracy prove the circuit can be effectively applied in pattern recognition. The noise experiment shows the robustness of the designed circuit.
An Architecture for Grant-Free Random Access Massive Machine Type Communication Using Coordinate Descent

An implementation of activity detection for grant-free massive machine type communication is presented. The implemented algorithm is based on coordinate descent which shows a rapid convergence time. A number of modifications to the original algorithm is proposed to allow efficient implementation in hardware. In addition, the implementation is based on fixed-point representation, and, hence, exhaustive word length simulations have been performed for the different processing steps.
Domain-Specific Scientific Impact and its Prediction

In today’s interdisciplinary research society where researchers are interested to participate in more than one discipline and they collaborate across disciplines, it is essential to understand the domain-specific impact of scholars. This study explores the impact of scholars who work in multiple disciplines and predicts scientific impact using multiple features. A Miner dataset is selected as a testbed and 10 sub-domains of computer science are analyzed. Despite today’s cross-discipline collaboration trend, there is a high number of scholars who work in a single domain as compared to those who work in multiple domains. Results show that among the 10 disciplines of computer science, “Network Control” gained high impact in terms of received citations. Various features are used to predict the future impact and “total papers” yield the highest R2 value followed by “topic-based citations” and “total citations”.
A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

Humans can identify a speaker by listening to their voice, over the telephone, or on any digital devices. Acquiring this congenital human competency, authentication technologies based on voice biometrics, such as automatic speaker recognition (ASR), have been introduced. An ASR recognizes speakers by analyzing speech signals and characteristics extracted from speaker’s voices. ASR has recently become an effective research area as an essential aspect of voice biometrics. Specifically, this literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past, present, and future research trends in this area. This paper briefly describes all the main aspects of ASR, such as speaker identification, verification, diarization etc. Further, the performance of current speaker recognition systems are investigated in this survey with the limitations and possible ways of improvement. Finally, a few unsolved challenges of speaker recognition are presented at the closure of this survey.
A Novel Wear Detection Method Based on FPC: A Case Study on the Hoisting Cage Sliding Sleeve

Under severe working conditions in mines, common wear detection methods are difficult to apply to the hoisting cage sliding sleeves. This article proposes a wear detection method based on flexible printed circuit (FPC), which is conceived for the wear detection of sliding sleeves or the edge of complex curved surfaces under severe conditions. First, a new type of sensor based on FPC is proposed, and its detection principle is presented. Then, according to the wear mechanism of sliding sleeves, a wear detection system is designed and manufactured. In order to express wear value, a formula about the relationship between the number of broken wires and wear value is established. Finally, a series of verification experiments are carried out, and the obtained results confirm the feasibility of proposed approach and explain the causes of voltage fluctuations.
Efficiency of primary chlorination, clarification and final disinfection on Pseudomonas aeruginosa under laboratory conditions in raw water

In order to inactivate Pseudomonas aeruginosa in raw waters, we investigate the efficiency of primary chlorination alone and combined chlorination and clarification in surface water naturally containing P. aeruginosa . Final disinfection was performed in clarified surface water and groundwater both contaminated with P. aeruginosa . The results obtained under laboratory conditions show that coagulant aluminum sulfate (AS) and cationic polymer polyelectrolyte contribute significantly to reducing turbidity with the inactivation of cultivable cells of P. aeruginosa in different water samples. Our findings also suggest that while chlorination may be a satisfactory method for controlling and preventing P. aeruginosa growth in water intended for human consumption, it does not eradicate all cultivable bacteria in raw surface water. P. aeruginosa was nonetheless shown to be relatively sensitive to sodium hypochlorite, and the effectiveness of chlorine was greater in natural groundwater than in clarified surface water.
WaveNetEQ — Packet Loss Concealment with WaveRNN

We present WaveNetEQ, a novel packet loss concealment method based on a WaveRNN architecture. The model is conditioned on a log-mel spectrogram of the past signal to extract slow moving features, like voice characteristics and prosody and achieves significantly better quality than pattern based methods for medium and long term packet loss. Through aggressive sparsification the model is efficient enough to run on a phone.
Functionally Graded Viscoelastic Artificial Boundary Layer In Time Domain Analysis Of Elastic Waves

In this paper, the concept of functionally graded (FG) viscoelastic artificial boundary layer is proposed for the numerical simulation of elastic waves. The governing equation of longitudinal wave in rod composed by elastic rod and FG viscoelastic rod is solved by Wentzel-Kramers-Brillouin method. The variation of material property in FG viscoelastic rod is obtained by no reflecting condition. Based on the analytical results, the setting of FG viscoelastic artificial boundary in the numerical simulation is recommended and applied in two examples, namely longitudinal wave excitation in an infinite rod and local excitation in an infinite half space. Numerical results show that the use of FG viscoelastic boundary should decrease the error caused by artificial boundary. These methods should be expanded in the numerical simulation of wave in other infinite structures.
A Review on Object Detection Algorithms for Ship Detection

Object recognition joins computer vision technique for spotting and tracking down objects in images and videos. This paper enlightens object recognition algorithms that are currently available. The algorithms analyzed include Multi-Input Convolutional Long Short Term Memory (Multi-Input convLSTM) and Rotational Libra Region Based Convolutional Neural Network (R-Libra RCNN). A two-path convLSTM pyramid and a personalized convLSTM module referred to as multi-input convLSTM are proposed as a part of a web causal video object detector to detect off-shore ships. This module stores and exchanges temporal contextual data at different scales between adjacent modules of convLSTM within the two-path convLSTM pyramid by using two input feature maps which are of nonidentical resolution. Within the multichannel convLSTM, we have employed reconfigurable convolution to the sourcefunction map. This adaptively adjusts sampling location and therefore receptive field in accordance with the object’s scale and shape. Additionally, R-Libra R-CNN is proposed where the vessel tracking system is made up of trio accelerators: Intersection over Union (IoU)-Balanced Sampling (BS), Balanced Feature Pyramid (BFP) and Rotational Area Detection segment with healthy loss. We finally reached a conclusion on best algorithm for ship detection.
Predicting Elections Results using Social Media Activity A Case Study: USA Presidential Election 2020

Utilizing social media to ascertain its user’s opinions over an entity, more specifically Twitter to forecast Trends is a popular field of research. Employing Twitter for election campaigns, to monitor predict election results, capturing the say of both voters candidates in real-time has escalated over the years. Users share their views, preferences on Twitter voluntarily and are publicly accessible. ‘Tweets’ are quick, brief real-time user updates and can be extracted through Twitter API. Downloading replies on the most recent Tweets of the candidates can serve our purpose. Location parameters can help in obtaining more accurate credible results. in this paper, we have compared four methods from machine learning and deep learning domains named as “textblob”, “naive bayes method “support vector machine” and “BERT based deep learning approach” for sentiment analysis. we found that BERT based approach is superior to others. To demonstrate the performance of these methodologies we have taken a recent USA presidential election 2020 as a case study.
90 nm CMOS Resistive Feedback LNA with 34 dB Gain for 25 GHz Applications

The paper presents a 90 nm CMOS Resistive feedback LNA with high gain designed for digital communication receivers operating at 25 GHz. The proposed LNA is based on four-stage configuration. The first and the last stage consist of CS transistors with resistive feedback topology while the second and third stage uses cascode configuration. At 25 GHz, this LNA attains a peak gain of 34.1 dB. With an input of 0.9V, the power consumed by the circuit is 17.3 mW. For achieving a good noise Figure of 2.7 dB at the output port, a $\pi$ matching network is used. The values of S11 (input return loss) and S22 (output return loss) at 25 GHz are -22.3 dB and -23.7 dB respectively.
Resource Allocation Optimization with Guaranteed Security Level for Anti-traceable Access

Internet visitors can be well protected by hiding their traces. The purpose of traditional traceability is to obtain the user's IP address and physical location through some technical means. However, the essence of anti-traceability is to hide information browsing and search intention through trace hiding technology. Inspired by the PPM (Probability Packet Marking) strategy, an effective method of anti-tracing is to prevent the attacker from reconstructing the user's access path in a VPN network. We investigate a systematic study of anti-tracing to capture the relationship between resource allocation and security level guarantee in this paper. Furthermore, we propose a novel resource allocation and link scheduling algorithm with guaranteed security level. Numerical results verify that our proposed resource allocation solution can achieve good performance in terms of anti-tracing and security level guarantee.
Iterative MMSE Space-Time Zero-Crossing Precoding for Channels With 1-Bit Quantization and Oversampling

Systems with 1-bit quantization and oversampling at the receiver are promising for IoT applications due to low hardware complexity and low energy consumption. Zero-crossing precoding implies that the information is conveyed in the time instance of a zero-crossing within the symbol time interval. In this context, this study proposes an iterative spatial temporal MMSE precoding algorithm. In comparison to the joint space time MMSE precoder, the proposed method shows a significantly lower computational complexity and a comparable MSE performance.
Smart Irrigation And Crop Protection Using Arduino

This paper aims at designing and executing the advanced development in communication system for smart irrigation and crop protection from animals that invaded to farms like cows, goat, elephants, etc. and The famers can’t protect the entire farm by staying in farm for all day. So, the PIR sensor is kept in the field to watch out the animal motions. When it detect any movement recorded in the PIR senor. it starts alarming. And therefore soil moisture sensor detects moisture level in soil when it starts getting wet. it will be automatically starts water pump. However more amount of water in farm will spoil the crops so, servo motor opens the canal when heavy rain flows detected by rain sensor. added upon gas sensor is used to detect the fire in farm.
Apprehending Mirai Botnet Philosophy and Smart Learning Models for IoT-DDoS Detection

This paper aims at imparting acquaintance to the researchers an insight into the IoT metamorphosis from a security point of view. This paper presents a state-of-the-art apprehension of the IoT botnet landscape with a close analysis of Mirai. We have elucidated the characterization of the IoT-specific network behaviors such as limited endpoints, sleep time between packets, packet size, etc. that have turned out to be of substantial efficacy to contemporary learning algorithms, including neural networks. The learning algorithms have been reliable to be efficient enough for distributed denial of service (DDoS) attacks detection. We have evaluated the existing learning models and have proposed an efficient IoT-DDoS defense solution. Finally, we have concluded the research with prospective extensions.
Caching Resource Allocation and Mobility Aware in Information-Centric Networking with Mobile Edge Computing

The rapid rise in communication delay and the speed of downloading Wireless Communication devices with Fifth Generation Wireless Networks (5-G) wireless networks, mobile edge servers may use the high-speed local link to reduce the demand of their core networks by caching or prefetching content. The Information-Centric Networking (ICN) with Mobile Edge Computing (MEC) aims to upgrade its system to overcome the lack of network capacity. The aim is to provide a network service that is more suitable today (particularly resource allocation and mobility) and resilient to interruptions and failures. Cache resource allocation and mobility is one of the problematic and realistically major issues of Information-Centric-Network and in Mobile Edge Computing. Though several studies have been optimized. A minimum consideration has been devoted to minimizing user energy consumption to improve caching efficiency and service quality. To address this deficiency, the focus of this study is optimized cache resource allocation for closer users and connected networks with minimum time delay and energy consumption considering mobility. For this reason, the problem of caching resource allocation (CRA) is divided into two subproblems: the problem of Base Station caching ability (BSCC) and the challenge of Request MAtching (RMAT). Then we can one by one solve BSCC and RMAT and merge their CRA solutions.
An Intriguing Analysis of E-Seller Challenges for Multi-Platform eCommerce

e-Commerce today is a huge domain which has flourished at an unparalleled pace throughout the world. People have been turning towards online businesses for buying as well as selling their merchandise. The times of COVID-19 pandemic have further fueled this shift. Considerable research has been done for the betterment of the marketplace as well as customer experience. However, the e-Seller's point of view has never been given its due in this scenario. Most of the e-Sellers have now shifted to multi-platform e-Commerce solutions with surge in competition. This manuscript focuses on the plight of the e-Seller and elaborates the Process Flow of an e-Commerce business and details the various challenges encountered during each phase for a multi-platform goods trade. It discusses the set procedure that every e-Commerce business follows in order to achieve satisfactory results and reviews the needs of an e-Seller and the tools available for easing out some of the e-Seller tasks. The findings are based on the first-hand information and experience provided by the e-sellers and analysis of their data. With practically no study and appropriate tools to aid the e-Seller, the survival chances of even average-sized businesses are low. Despite the advancement of technology, through the research done in this paper, it has been found out that there are many domains in the e-Commerce Process Flow of the e-Seller which have still not been studied and fail to come up with a solution to help the trader. As a first of its kind, these novel dimensions in this domain have been highlighted, and the possibility for a plausible solution has been conveyed through this manuscript.
A New Adaptive Sparse Pseudospectral Approximation Method and its Application for Stochastic Power Flow

With the increasing integration of various renewable energy resources into electric power systems, the stochastic power flow calculation, which falls into the uncertainty propagation (UP) problems, has attracted renewed interest recently. The adaptive sparse pseudospectral approximation method (A-SPAM) is highly efficient to solve UP problems. However, there still exist two major drawbacks: 1) its global error estimate is not accurate enough to evaluate the actual approximation error; and 2) it is intended for single-output systems and there is no efficient method for its application in multi-output systems. In order to overcome these two defects, in this study a new adaptive sparse pseudospectral approximation method (NA-SPAM) is proposed based on two improvements on A-SPAM. First, a new global error estimate is presented which features much higher estimation accuracy. Second, a revised version of error estimates is given based on unification of the index set, which apparently improves the calculation efficiency for multi-output problems. At last, the effectiveness of the two improvements and NA-SPAM is validated by mathematical and practical cases.
Effectively Incremental Structural Graph Clustering for Dynamic Parameter

As an useful and important graph clustering algorithm for discovering meaningful clusters, SCAN has been used in a lot of different graph analysis applications, such as mining communities in social networks and detecting functional clusters of genes in computational biology. SCAN generates clusters in light of two parameters ∊ and µ. Due to the users lack the necessary professional knowledge, however, the parameter ∊ needs to be changed multiple times to obtain the desired clustering results. Every time the parameter ∊ changes, the new clustering result R’ can be obtained by executing the SCAN algorithm once, which takes a lot of time. To address this problem, in this paper, based on the previously clustering result R, we explore an effective incremental clustering strategy when the parameter ∊ changes dynamically to ∊‘. Although the SCAN results are affected by the parameters ∊ and µ, some vertices in $R$ are unaffected when the clustering parameter ∊ increases or decreases. Moreover, some useful information for clustering is constant, such as the structural neighborhood of any vertex, and the degree of any vertex. In this case, these information can be stored for use in the process of obtaining the new clustering result R’ with regard to the changed parameter ∊’ and the original parameter µ. Therefore, we explore two effective incremental clustering algorithms for the dynamically changing parameter ∊’, which avoids re-executing the SCAN algorithm based on the new parameter ∊’ and µ. Finally, we conduct comprehensive experimental studies, which illustrates that the incremental clustering model can effectively obtain the new clustering results when the clustering parameter ∊ changes dynamically.
MapReduce based Heart Disease Prediction System

Decision tree is the traditional method to process health-related data. However, there are challenges in developing the distributed decision tree algorithms for massive health data using Hadoop. First, as the datasets are massive, the process of building a decision tree is time-consuming. The second problem is splitting. To solve these issues, we propose MapReduce-based Heart Disease Prediction System (MRHDP) for constructing a decision tree to predict heart disease with the Map Reduce programming model using the Hadoop platform. We develop distributed algorithms using the MapReduce framework, and also we conducted a large-scale experiment on massive data sets. Finally, the results indicate that the MRHDP algorithm exhibits better prediction results for heart disease, indicates better processing with less time productivity and versatility.
Estimation of Background Medium’s Properties in Microwave Holographic Imaging

In this paper, an approach is proposed to enhance the images produced by holographic microwave imaging technique. The approach estimates the electrical properties of the background medium. For that, first, we assumed that the background properties are within a pre-known range. Then, wideband holographic imaging technique is applied to two sets of frequencies to reconstruct two sets of images for assumed property values within the pre-determined range. An error is computed to record the differences between the two sets of images. The error is expected to be minimum at the true values of the background medium’s properties. Simulation and experimental results demonstrate the validity of the proposed technique.
A Multi-Step Incremental Analog-to-Digital Converter With a Single Opamp and Two- Capacitor SAR Extended Counting

This work describes a hybrid incremental ADC (IADC) with two-capacitor (2-C) successive-approximation registers (SAR) extended counting in two-step operation to achieve high resolution data conversion. The circuits in the first step is acting as a first-order incremental analog-to-digital converter (IADC). Finite impulse response (FIR) DAC is incorporated in the loop filter to reduce the transient voltage step. It is reconfigured as a 2-C SAR to perform extended counting technique in the second step. Only one opamp is re-used in both steps. The hardware is prototyped in <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology, and the hybrid ADC accomplishes a measured DR / SNR / SNDR of 100.2 / 97.1 / 96.6 dB and an input signal bandwidth of 1.2 kHz. Operated at 1.5-V, it consumes <inline-formula> <tex-math notation="LaTeX">$33.2~\mu \text{W}$ </tex-math></inline-formula>, and this achieves a Walden figure-of-merit (FoM) of 0.25 pJ/conversion-step and Schreier FoM of 175.8 dB.