Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Scalability of Nanowire Junctionless Accumulation Mode FETs using an Intrinsic Pocket

Nanowire (NW) junctionless accumulation Mode (JAM) FETs were proposed to improve the ON-state current as compared to the conventional junctionless FETs. However, NWJAMFETs suffer from an enhanced detrimental lateral-band-to-band tunneling (L-BTBT) which restricts their scaling to sub-10 nm regime. Therefore, we investigate the scalability of NW JAMFETs using an intrinsic pocket in the extension regions. Using calibrated 3-D simulations, we demonstrate that the incorporation of an intrinsic pocket substantially reduces the L-BTBT induced OFF-state current by around 2 orders and 4 orders of magnitude for a gate length of 20 nm and 7 nm, respectively, leading to a significant ON-state to OFF-state current ratio (ION/IOFF) of 106 and 105, respectively and a small DIBL of 12mV/V and subthreshold swing (SS) of 62.2mV/dec for 20 nm gate length device. The NW JAMFET with intrinsic pocket (Pi-NW JAMFET) exhibits superior short channel characteristics due to the reduced electrostatic source/channel-to-drain coupling and a diminished parasitic BJT action in the sub-10 nm regime extending the roadmap for the device scaling. The proposed Pi-NW JAMFETs show superior performance even at a scaled contacted poly pitch (CPP) of 52 nm with IOFF~28pA/um for a gate length of 7 nm. The excellent OFF-state characteristics of Pi-NW JAMFET makes them a lucrative FET architecture for realizing sub-10 nm channel lengths for low power and low leakage applications.
Subcentimeter Precision Ranging System for Moving Targets With a Doppler-Effect- Compensated Ultrasonic Direct Sequence Spread Spectrum

In this article, we present a Doppler-effect-compensated ranging system that can be applied to subcentimeter-precision ultrasonic distance measurement for moving targets using a direct sequence spread spectrum (DSSS). First, the theory of the Doppler compensation technique is explained, and then, the evaluation results obtained using maximum-length-sequence coded ultrasonic signals are discussed. In this study, we employ a 128-b DSSS code with three wavelengths per bit. The experimental results demonstrate that the proposed system can measure a target accelerating at 9.8 m/s2 with a speed of up to 2 m/s in the range of 0.1–1.6 m, with a standard deviation of less than 5 mm. We also confirmed by simulation that the system can track a target accelerating at 30 m/s2 with a speed of up to 5.5 m/s in the range of 4–6 m.
Automated Facial Expression based Pain Assessment Using Deep Convolutional Neural Network

Pain detection is one of the crucial factors for a patient’s recovery as it gives cues about the health stability of the patient. Currently, pain is detected by expert observation and self-report of the patient. But it is a challenging task to assess the pain by observation as it is highly subjective and requires experience. To detect pain various techniques are utilized in the recent past by researchers. However, these methods require handcraft features for model training which is a manual process, thus time-consuming and requires domain expertise as well. Additionally, a deep convolutional neural network has been utilized by the state of art methods for pain recognition. But these methods are computationally expensive in terms of network architecture and utilized preprocessing techniques before feeding the data to the neural network. Research work employs a deep convolutional neural network without any pre-processing steps to achieve competitive results. In view of that, a fully automated method that utilized Deep Convolutional Neural Network (DCNN) is proposed. Moreover, the proposed network has been trained end-to-end manner and does not require any complicated pre and post-processing steps. Therefore it is computationally efficient and can be utilized for another classification task as well. To evaluate the proposed model publically available pain dataset namely, UNBC-McMaster shoulder pain data is used. The experimental results show that proposed method has AUC of 0.97 which demonstrates that proposed approach outperforms conventional feature engineering based techniques and provide competitive results than current state of art deep neural network based methods which utilize complex architecture with preprocessing techniques.
Unmaned Aerial Vehicles Threats and Defence Solutions

Unmanned aerial vehicles likely know as drones have become the most optimistic solution with a bundle of applications in the field of monitoring environment, transportation, media live streaming, and military operations. The autopilot UAV system is normally used in exigent operations to acquire critical information. The basic UAV system consists of three major components which include aerial vehicles contain some sensors and actuators, ground control stations, and communication channels. UAV systems are vulnerable to different security threats due to their deployment in serval crucial domains. There exist numerous attack techniques includes, GPS spoofing, de-authentication, denial of services, injecting false information, damaging UAV sensors, Key loggers, which can be unfavorable for our security objectives. Over the last decade, their innumerable defense mechanisms are proposed to mitigate security risks from these kinds of attacks. In this review, we will discuss major components of UAV, prime vulnerabilities for cyber-attacks, and their defense solutions.
Cl2 reactive ion etching for gate recessing of AlGaN/GaN field-effect transistors

An effective gate recess etch process has been applied to AlGaN/GaN modulation-doped field-effect transistors (MODFETs), utilizing low power Cl2 reactive ion etching. In comparison to GaAs-based materials, GaN shows a greater robustness to ion damage under ion bombardment at very low ion energies (<70 V). It suggests that a viable gate recess etch process is possible. Recessed gate AlGaN/GaN MODFETs with gate to drain breakdown higher than −80 V have been demonstrated with an optimized lower power Cl2 reactive ion etching.
A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech

Detection of voice changes in Parkinson’s Disease (PD) patients would make it possible for early detection and intervention before the onset of disabling physical symptoms. This study explores static and dynamic speech features relating to PD detection. A comparative analysis of the articulation transition characteristics shows that the number of articulation transitions and the trend of the fundamental frequency curve are significantly different between HC speakers and PD patients. Motivated by this observation, we propose to apply Bidirectional long-short term memory (LSTM) model to capture time-series dynamic features of a speech signal for detecting PD. The dynamic speech features are measured based on computing the energy content in the transition from unvoiced to voiced segments (onset), and in the transition from voiced to unvoiced segments (offset). Under the two evaluation methods of 10-fold cross validation (CV) and splitting the dataset without samples overlap of one individual, the experimental results show that the proposed method remarkably improves the accuracy of PD detection over traditional machine learning models using static features.
32nm FinFET Based PLL

The demand for low power based circuits increases as the size of channel length decreases. But as channel length decreases it is difficult to use CMOS based circuits due to problems in its fundamental material, short channel effect and high leakage. Thus FinFET technology can be used as an alternative to classical MOSFET, to achieve low power application. The effectiveness of various Double Gate in Mixed Signal(MS) circuit level design is less explored. Phase Locked Loop (PLL) is one of the important MS circuit and are integral part of many electronic system. Extracting advantages of FinFET over above mentioned issues in PLL, this paper proposes two design of PLL using novel device DGMOS FinFET at 32nm technology. The first design of FinFET based PLL uses FinFET in shorted gate (SG) mode & the second design uses FinFET in independent gate (IG) mode for the design of Phase Frequency Detector (PFD) block. The designed circuits are simulated using HSPICE by Synopsis with 32nm FinFET technology with power supply voltage of 0.8V. Performance of designed PLL are analyzed at operating frequency of 500MHz. The comparative analysis of all the designs of PLL shows that Modified FinFET based PLL at 32nm shows better performance in terms of faster locking period at 38.34ns, high frequency operation from 500MHz to 11GHz with smaller area requiring lesser no. of transistors & having power dissipation of 86.36 µW which is comparatively low.
Ensemble learning models for short-term electricity demand forecasting

Forecasting energy consumption is critical in decision-making for efficient energy saving, improve stability of the power grid, and prevent supply-demand discrepancy. To predict day-ahead load forecasting for the demand of city of Kirkuk two scenarios were presented. First, benchmarked three individual machine learning algorithms e.g. generalized linear model (GLM), artificial neural network (ANN), and random forest (RF). Second, compared the predictive capabilities for individual models with the ensemble models. The results indicate that the predictive models maybe can be improved using simple ensemble learning strategies such as averaging the predicted results. This study is also present future research directions to improve the model prediction capabilities.
Linear Prediction Approach to the Robust Parameter Estimation for the Damped Sinusoids

In this paper, we focus on the problem of parameter estimation for the damped sinusoids, which are corrupted by impulsive noise. To provide a robust initial guess for the current parameter estimators, the robust weighted linear prediction (RWLP) estimator is developed, where the parameter estimates are obtained by minimizing the weighted ℓp-norm of the linear prediction (LP) error vector. The Markov optimum weighting matrix is derived, and an iteratively reweighted least-squares (IRLS) procedure is devised to calculate the LP coefficient estimates. The simulation results demonstrate the robustness of the RWLP estimator by comparing with the ℓ2-norm based counterpart, and the computational efficiency by comparing with the ℓp-MUSIC algorithm.
Design of Circularly Polarized Patch Antenna using Genetic Algorithm for Energy Harvesting

This paper presents a design of single-fed circularly polarized patch antenna at 2.4 GHz-band for RF energy harvesting application. The irregular shaped patch is obtained by genetic algorithm optimization technique. The fitness function for circular polarization and return loss has reliably conflated in the genetic algorithm. The optimized irregular shaped single-fed microstrip patch antenna is designed using Matlab and HFSS software and the simulated result shows a return loss of -15.498 dB and the axial ratio of 2.09 dB at 2.41 GHz resonating frequency.
ICC T20 Cricket World Cup 2020 Winner Prediction Using Machine Learning Techniques

Predicting future sounds like magic, whether it be detecting, in advance, the intent of a potential customer to purchase company's products or figuring out where the price of a stock is heading towards. If we can reliably predict the future of something, then we own a massive advantage. Machine learning has served to amplify this magic and uncover the mystery. It has also served in the fields of sports. Billions of people around the world are big fans of Cricket and wait for the results eagerly. In this paper, we discuss the 7th edition of T20 World Cup 2020 which will be held in Australia. We have compared popular machine learning techniques for the prediction of the T20 cricket world cup winner. Among the developed models, Random Forest proved to be the best machine learning algorithm using a custom accuracy metric. It obtained a custom accuracy of 80.86%. In this analysis, Australia emerged as the winner of the T20 world cup 2020. For this purpose, the ESPN Cricinfo dataset has been used.
Analysis of Guiding Quality Evaluation Model Based on Regional Ecological Safety Performance Evaluation and Information Mining

Analysis of guiding quality evaluation model based on regional ecological safety performance evaluation and general information mining is well studied in this article. Knowledge utilization is a key link in the knowledge management. It refers to process of realizing transformation from knowledge theory to practice and the extension of the practice level by means of exchange and sharing on the basis of creation and accumulation. Inspired by this, the article has too many aspects of novelties. At first, reduces the clustering to a constrained nonlinear programming problem, and solves the problem through various optimization methods to obtain the fuzzy partition. Secondly, the structure rule of a new classification is designed for an approximate space of different perspective makes use of information entropy. Finally, the proposed model is applied for the performance evaluation. The experimental results have shown the performance.
Adaptive Mask for Region-based Facial Micro-Expression Recognition

Facial micro-expression can be characterized by its short duration and subtle movements. In facial micro-expression recognition, these subtle movements require more specific feature descriptors due to only a few parts of the face produce information that helps us to recognize micro-expressions. Over the past decade, researchers designed different Region of Interests (ROIs) to study specific face regions in micro-expressions recognition. To further study this aspect, we proposed a region-based method with an adaptive mask for facial micro-expression recognition. Based on the most frequent Action Units on the two publicly available datasets, i.e. CASME II and SAMM, 14 ROIs are defined where the adaptive mask is created by calculating the optical flow after Gaussian smoothing. Further, LBP-TOP features are extracted from each ROIs and Sequential Minimal Optimization is used to classify the micro-expressions. When evaluating our proposed method on CASME II and SAMM, we achieved the accuracy of 68.2% and 56.1%. In terms of F1-Score, our proposed method achieved 0.57 on CASMEII and the best performance of 0.50 on SAMM.
Machine Learning-Based Cocoa E-Health System

Cocoa diseases and pests such as Mirids, Black pod, and the Cocoa Swollen Shoot Virus Disease (CSSVD) has been a major contributor to the decline in cocoa productivity in Ghana. The study aims at using machine learning and software engineering architecture to develop a Smart Cocoa Health System to detect, diagnose, and predict, the presence of cocoa diseases. The software is in three parts: The data management, analytics application, and the control application (user experience). The analytics application involves the use of machine learning techniques for predictive data analytics to foretell the possible occurrences of the diseases before they actually occur. The users will use the control application to interact with of the software system in accordance with the information displayed by the analytics application.
Evaluation of a Josephson Arbitrary Waveform Synthesizer at Low Voltages for the Calibration of Lock-In Amplifiers

We have evaluated a Josephson arbitrary waveform synthesizer (JAWS) in the voltage range from <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{V}$ </tex-math></inline-formula> to 1 mV at frequencies from 60 to 1000 Hz for the use in the calibration of lock-in amplifiers. The uncertainty contribution from the JAWS system is 45 nV for 1 mV at 1000 Hz and <inline-formula> <tex-math notation="LaTeX">$k=2.0$ </tex-math></inline-formula>. We anticipate that the JAWS will help extend the lower voltage and frequency range of ac voltage metrology and improve the uncertainties by one order of magnitude compared to conventional techniques.
A Complete, Accurate and Efficient Solution for the Perspective-N-Line Problem

This paper presents a complete, accurate, and efficient solution for the Perspective-n-Line (PnL) problem. Generally, the camera pose can be determined from <inline-formula><tex-math notation="LaTeX">$N \geq 3$</tex-math></inline-formula> 2D-3D line correspondences. The minimal problem (<inline-formula><tex-math notation="LaTeX">$N= 3$</tex-math></inline-formula>) and the least-squares problem (<inline-formula><tex-math notation="LaTeX">$N > 3$</tex-math></inline-formula>) are generally solved in different ways. This paper shows that a least-squares PnL problem can be transformed into a quadratic equation system that has the same form as the minimal problem. This leads to a unified solution for the minimal and least-squares PnL problems. We adopt the Gram-Schmidt process and a novel hidden variable polynomial solver to increase the numerical stability of our algorithm. Experimental results show that our algorithm is more accurate and robust than the state-of-the-art least-squares algorithms <xref ref-type="bibr" rid="ref1">[1]</xref>–<xref ref-type="bibr" rid="ref2"/><xref ref-type="bibr" rid="ref3"/><xref ref-type="bibr" rid="ref4">[4]</xref> and is significantly faster. Moreover, our algorithm is more stable than previous minimal solutions <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref5">[5]</xref>, <xref ref-type="bibr" rid="ref6">[6]</xref> with comparable runtime.
Flip-flops fanout splitting in scan designs

Test point insertion is a key method of circuit modification for significant reduction of test patterns during an automatic test pattern generation in scan compression designs. This paper presents a new method of flip-flops replication in order to reduce test data volume and test application time by splitting the fanout of scan cells. This proposed approach employs fanout reduction and reconvergent fanout elimination in large nanometer designs. Experimental results on several IP-designs with embedded compression of test patterns show that the proposed method reduces test application time up to nearly 2 times with hardware costs not exceeding 2.5% of design area.
Automated Software Testing on Mobile Applications: A Review with Special Focus on Android Platform

Development of mobile applications and hence testing them with automated tools are rapidly achieving grounds on the technology market with the explosive evolution, demand and use of mobile technology. Although automated software testing has been a topic of research for many years, automation testing on mobile application is being a key topic in the software engineering industry and hence the development and introduction of new automated tools have gained attention in the recent years. This research aims at identifying, analyzing, and synthesizing the current state-of-the-art of automated testing on mobile application with special focus on to the popular mobile application platform "Android". A systematic literature review was conducted on studies retrieved from electronic search space. Various literature reviewed and referred there in, as a conclusion, its obvious that none of the techniques used in automated testing is alone inadequate and a combination of them works well as an effective testing strategy. Also, the challenges thus faced by the practitioners of automated testing could be still mitigated with an efficient selection of testing tool.
Urban sprawl evolution and soil artificialization assessment by using satellite data from 1985 to 2019: case of El Jadida metropolitan in Morocco

Urban planning, a technical and political process related to land use and the design of the urban environment, emerged to meet the economic and social demands of the population. Nevertheless, in order to achieve sustainable development of the territory, urban planning must be metered by taking into account the environmental balance. Controlling urban sprawl inevitably involves assessing its spatio-temporal evolution. The objective of this study is to explore geospatial data to trace, characterize and monitor the extension of the urban fabric of greater El Jadida and also identify the different stages of urbanization and the changes induced over thirty-four years (1985 to 2019). To this end, three algorithms such as Minimum Distance of Mean (MDM), Maximum Likelihood (MLC) and Support Vector Machine (SVM) were applied to Landsat multispectral satellite images (TM , ETM+ and OLI). The spatial similarity and study area statistics were evaluated to determine the accuracy of the three processes and to choose the most suitable classification algorithm in our case. This methodology allowed the temporal comparison of the different tele-analytical maps of land occupation and land use highlighting the artificialization of the territory and the areas of urban and peri-urban sprawl extension between 1985 and 2019.
A Bacterial Foraging Optimization Technique and Predictive Control Approach for Power Management in a Standalone Microgrid

In this paper, the Bacterial Foraging Optimization (BFO) algorithm is proposed for real-time calculation of active and reactive power references estimation. This method emulates the behavior of E.coli bacteria to find the global minimum of a cost function. Using this technique, one estimates the power references of the system to use as input parameters for the developed predictive control strategy for the three-phase inverter. The comportment of the BFO and the predictive control, are tested using Matlab/Simulink under load variation. In addition, a detailed comparison between the BFO method and Particle Swarm Optimization (PSO) is provided.
Real-time Monitoring and Comprehensive Analysis Framework for Rainwater and Soil Pollution based on Big Data Feature Extraction and IoT

Real-time monitoring and comprehensive analysis framework for rainwater and soil pollution based on big data feature extraction and Internet of Things (IoT) is discussed in this article. The procedural operation technology of the dispatch automation system is proposed, and introduces the overall structure and implementation plan of the dispatch automation system. The novelty is summarized into the following core aspects. (1) By representing the reconstructed training samples with low rank, a robust discriminant feature space constraint term was designed to enable the learning of feature subspace to improve the data analytic effectiveness. (2) For the forward propagation and backward propagation, the direction of the traversal and the operations that must be performed are different. Based on this, the novel real-time analysis model is designed. The proposed model is simulated on the hardware devices. The results have shown that the proposed method is efficient.