Miracle-dz/UniFAR-FTUs huggingface dataset:
Number-plate recognition and examination of requirements for public Romanian roads usage

The increasing number of cars brings upon itself the need of rapidly check the lawfulness of the drivers. When travelling, there are several legal obligations a driver have to fulfill according to the law. In Romania, the authorities enforce the presence of legal documents such vignette, RCA (civil liability) and PTI/ITP (periodical technical inspection) when being subject to a routine control. This article aims to present a solution to ease the process of verification through a software solution which identifies the number-plate and then verifies in a unified database the availability of the above-mentioned legal documents. This solution is implemented in Matlab, using correlation factor between the identified number-plate and a set of characters (the alphabet) to determine the letters from the plate number. The program is connected to a database in which are saved the details (vignette, RCA, PTI) of the identified number, this being used by the authorities to verify the validity of the drivers' documents.
An analysis of voice quality of Chinese patients with depression

In the present study, we empirically explore how the voice quality of depression patients (as experimental group) differs from that of healthy people (as control group), in terms of jitter, shimmer, HNR and pitch. Our analysis results reveal that the shimmer, maximum HNR and minimum HNR of patients are significantly different from those of the control group. Specifically, the patients tend to have a higher shimmer and lower maximum and mean HNR. To figure out to what extent the emotion has influenced the results, we further investigate whether there are significant differences in voice quality among different variations of emotion (positive, neutral, and negative) embedded in text reading. It turns out that no significant differences in voice hoarseness are found, showing that the voice quality is immune to emotion. Therefore, we can conclude that in general the voice of depression patients is hoarser than non-depressed people.
Readiness, Safety, and Privacy on Adopting Autonomous Vehicle Technology: UAE Case Study

Different perspectives discuss autonomous vehicle (AV) as predicted future transportation that includes design, engineering, economics, computer science, social, economic, legislation, and ethical aspects. On the other hand, AV technology offers new problems such as new engineering aspects, social and ethical problems, and presents in the form of an idealized decision-making problem. There are many benefits to AV vehicles, which are at the forefront of technology development. However, the level of cybersecurity provided by this technology is poorly understood. Cyber-attacks against AV can have severe implications for safety, privacy, manufacturers' reputation, and overall perception. In this research, we examine public opinion regarding the implications and challenges that might face AV technology in the United Arab Emirates (UAE). We claim that the successful adoption of AV in future transport systems will be achieved by reasonable risk management approaches that deal with different potential threats of cybersecurity. This survey produced usable responses from 204 participants. The significant result was that the respondents had not previously heard of vehicle-related technology; however, most had a positive first opinion on technology. Around 85% of participants felt that the expected benefits were likely to occur. They expressed a high level of concern regarding security and performance issues. Respondents stated that safety is the most crucial aspect of driverless vehicles. Mostly, it stated that personal communication devices should integrate with the AV's internet connection. The primary outcomes are that the population feels positive about AV and have optimistic expectations regarding its benefits. While maintaining some concerns, most of the people are willing to drive the AV.
Adaptive Disturbance Elimination Control of ESTATCOM for Unbalance Voltage Mitigation

This paper takes into account the mitigating of unbalance voltage in power transmission and power common coupling (PCC) points in the built benchmark which consist of Institute of Electrical and Electronics Engineers 30 bus (IEEE30bus) using simulation in MATLAB/SIMULINK. One of the shunt compensation FACTS method utilized in this work is Energy Storage Static synchronous compensator (ESTATCOM) including modern control action as Adaptive Disturbance Elimination Control (ADEC) performed. The control method based on an Extended State Observer (ESO) in order to estimate the internal and external of voltage control in which provide compensation for disturbances especially negative sequence occurrence. Definition of voltage unbalance in this paper based on IEEE definition of the percentage Voltage Unbalance Factor (%VUF), that is defined as the negative sequence voltage component divided on the positive sequence voltage component. Results show that when ESTATCOM has been used impact on both active and reactive power.
Stacked auto-encoder based Time- frequency features of Speech signal for Parkinson disease prediction

Proper classification between normal and Parkinson affected people is an important topic in recent years. From the last two decades, the number of methods has been proposed for the classification of Parkinson's affected and healthy people. Most of them based on a shallow structured network classifier. In this study stacked auto-encoder deep neural network framework is introduced to classify Parkinson affected and healthy people voice signals. The present study uses a spectrogram and scalogram of speech signals as input to the stacked autoencoder deep network. The extracted features are tested with a support vector classifier (SVM) and a Softmax classifier. Highest classification accuracy of up to 87 % with a spectrogram and 83 % with scalogram are obtained using Softmax classifier. The softmax classifier performed better than SVM. The proposed deep neural network may be a new window for further research.
A Method for Improving the Accuracy of Direction of Arrival Measurement based on Sparse Vector Estimation

This paper presents a method to determine the direction of arrival (DOA) of electromagnetic waves to a linear array of antennas. It is based on Discrete Fourier Transform and uses a sparse vector estimation technique to improve the accuracy. Unlike the other existent methods, this approach allows DOA estimation in a “single shot” data acquisition.
Machine Learning and IoT based Disease Predictor and Alert Generator System

Rapidly expanding market of health and increase in the common health issues such as cardiovascular, neurological and pulmonary, sleep, hydration and breath rate disorders motivates researchers to contribute in developing as well as designing systems that accurately observes the body parameter analyses, where the values of monitored parameters and generates alerts if the values show deviation from the standard pre-set values. The monitoring helps in early detection of chronic diseases such as diabetes, heart, respiratory disorders. Devices described in literature faces the challenges such as low reliability, low accuracy and less energy efficient. In this manuscript, authors present a review of existing literature available at national as well as international level. The review focuses on wearable devices such as smart eyewear, wrist band, smart jewelry, sleep track, smart garments and head mounted etc. The challenges identified by the review, motivates the authors to focus on designing a health monitoring frame work. In this manuscript, the authors propose a Machine Learning and IoT based Disease Predictor and Alert Generator System. The ease to use and high reliability proves the usefulness of system in the real time scenario.
A Research on SRAM-based FPGA Operating Frequency Margin Test Method

With the wide application of SRAM-based FPGA, the programmable resources of SRAM-based FPGA and its internal IP core are applied to the maximum extent. Owing to the negative correlation between the FPGA resource utilization and frequency, the FPGA frequency margin becomes a bottleneck that restricts reliable application of FPGA under extreme application conditions. The deviation of the manufacturing process leads to certain discreteness in the frequency after the FPGA chip is assembled and packaged, thus the devices with extremely low frequency shall be removed. In this regard, it is crucial to determine the frequency margin range of SRAM-based FPGA in order for the application reliability. In this paper, simulation and testing of the frequency limits of FPGA are conducted via LUT ring oscillator, carry chain ring oscillator and multiplier core ring oscillator, to analyze the simulation value and measured value, and the difference of the device under test from the products manufactured by Xilinx. The recommended standard for screening of the frequency margin of SRAM-based FPGA is also provided.
A new approach for Interference Mitigation in Multiple WBAN Using EMR-Rules

Wireless Body Area Network (WBAN) has turned into an important field of research that could give the solution for healthcare monitoring of human body. Clients of WBANs are expanding exponentially as more individuals are grasping wearable checking gadgets for various social insurance causes. Interference is one of the real issues in WBANs, which emerges principally because of closeness of different WBANs, irregular human portability, and disseminated nature of individuals conveying WBANs. In this paper, we proposed an agent node selection scheme based upon EMR-Rules (efficient mitigating rules) enhance its Quality of Service (QoS) parameters. EMR gives great arrangements if there is any vulnerability or incomplete truth in input factors. When the latency or cost exceeds the desired value's either degrades, then the link has interference then we select any agent node available in the network to the source node. On this set of rules, link with the highest hyperlink value is preferred and in this manner, agent node is chosen and it will hold transmitting the records to the sink node. Till the damaged node is reset. This technique offers improvements in strength efficiency and packet delivery ratio.
Suitability of IoT to Blockchain Network based on Consensus Algorithm

The Internet of Things (IoT) and Blockchain are increasingly growing focus on research. Blockchain is like the Internet when we first knew of it. This new technology could revolutionize how we do everything. Seriously, everything. Think how the Internet changed our daily lives today. And just like that, we don’t really have to understand the technology behind it and be knowledge of its importance. But what makes this innovation revolution a bigger deal than Bitcoin and possibly even the Internet itself, are the exponential opportunities the concept provides. However, the potential to integrated Blockchain to Internet of Things (IoT) has constraint due to the required high computational power. IoT comprises of numerous platforms which have limited performance. Usually, these platforms cannot handle intensive procedures and often has scalability issues. In this paper, we give an overview of consensus algorithms and the necessity of this method to blockchain technology. Moreover, we focused on three of the utmost common consensus algorithms used in blockchain technology and explore their potential adaptation in an IoT framework with respect to its requirements.
Investigating Big Data Analytics Readiness in Higher Education Using the Technology-Organisation-Environment (TOE) Framework

This paper aims to conceptualise the big data analytics (BDA) readiness among staff in higher education institutions in Malaysia. BDA refers to the process of collecting, managing, processing, analysing and visualising huge amount of data. BDA readiness is important because skills and competencies in using big data for data-driven decision making is vital particularly in the era of 4IR. Based on a single case study approach, this paper investigates the BDA readiness of a national university in Malaysia. The study merges readiness theories and the Technology-Organisation-Environment (TOE) framework constructs towards proposing a conceptual framework of how BDA readiness can be investigated in a higher education setting. Data from in-depth interviews with staff members of an institution of higher education is used to provide empirical evidence supporting the proposed conceptual framework.
Entrepreneurial Orientation in Organizational Culture of Small Enterprise

The concept of entrepreneurial orientation, as a concept at the enterprise level, can be located in the organizational culture, creating a specific standard of this culture. If this standard is perceived as having high quality (intensity), one can speak about an entrepreneurial culture. The main aim of the article is the diagnosis of the relations between entrepreneurial orientation and its implementation in the organizational culture. The hypothesis was empirically tested with questionnaire survey data obtained in 2018 from 260 small enterprises in Poland. As a main conclusion, the entrepreneurial culture of small enterprises is based on entrepreneurial orientation dimensions to a different degree, which confirms the assumption of the multidimensionality of the construct. Small enterprises base their entrepreneurial culture to the greatest extent on proactivity and innovation, which are also the best aware elements of the entrepreneurial culture of the surveyed enterprises.
Adopting Stochastic Network Calculus as Mathematical Theory for Performance Analysis of Underwater Wireless Communication Networks

Underwater Wireless Communication Network (UWCN) is highly emerging in recent times due to the broad variety of underwater applications ranging from disaster prediction, environmental resource monitoring, military security surveillance and assisted navigation. Since the kind of accuracy these applications demands from the dynamic underwater environment is really high, so there is a need for effective way of study underwater communication networks. Usually underwater networks can be studied with the help of actual underwater testbed or with the model of the underwater network. Studying the underwater system with the actual underwater testbed is costly. The effective way of analysis can be done by creating a mathematical model of underwater systems. Queuing theory is one of the most popular mathematical theories used for conventional circuit switched networks whereas it can’t be applied for modeling modern packet switched networks which has high variability compared to that of circuit switched networks. So this paper presents Stochastic Network Calculus (SNC) as the mathematical theory for modeling underwater communication networks. Underlying principles and basic models provided by SNC for analyzing the performance graduates of UWCN is discussed in detail for the benefit of researchers looking for the effective mathematical theory for modeling the system in the domain of underwater communication.
A Review on Acute Lymphoblastic Leukemia Classification Based on Hybrid Low Level Features

leukemia region unit ordered likewise whichever myelogenous (also called myeloid) or white platelet contingent upon that sorts for the influenced white platelets region unit. Leukemia happens when that bone marrow produces adolescent white cells, Furthermore leukemia happen when the marrow produces full grown phones. Intense lymphocytic leukemia (ALL) might additionally make a structure of cancellous around that those bone marrow makes excessively awful huge numbers adolescent lymphocytes (a sensibly white blood cell). Threatening Growth ailment might potentially might want an impact looking into RBC, WBC, and platelets. Every last bit is the greater part commonplace clinched alongside childhood, with a top frequency at 2–5 a considerable length of time outdated and in turn top over adulthood. The arranged approach is assessed around 3public picture databases for totally completely different aspects. The further execution measures: accuracy, specificity, and affectability. Division will furthermore order about intense lymphocytic leukemia that is frequently completed by utilizing “manage taking in” methodology.
Real Time Condition Monitoring on Brakes using Machine Learning Techniques

The condition monitoring is a technique used to predict the machine or an equipment’s status; it can be used to predict the health of the system. This is possible only by gathering some parameters which continuously vary depending on the usage of the machinery or a particular subjective device. In this experimentation a real time hydraulic brake system is considered for the fault diagnosis study by continuously monitoring of the brake system of a car. From which fail-safe condition of the brakes using vibration analysis by implementing some machine learning techniques can be obtained. Various brake faults are considered for extracting the features using histogram.
Inverted Polymer Solar Cells Using V2O5/NiO as anode selective contact: Degradation Study

In this work, we analyze the stability and degradation over time of inverted organic solar cells (iOSCs) using a stack layer of vanadium oxide and nickel oxide as hole transport layers (HTL). The parameter used to compare with other cells is the power conversion efficiency (PCE). Vanadium oxide layer (V2O5) used as HTL is the reference in this work which is monitored the stability and degradation. All devices are fabricated under nitrogen environment. The stack layer thickness used as HTL is fabricated with NiO on V2O5layer, which has been varied. The photovoltaic devices are encapsulated to be studied their degradation over time following the ISOS-D1 protocols during 10200 h. The active layer for devices is a mix of PTB7-Th and PC70BM being the donor and acceptor material, respectively. The performance parameters under light during the degradation process are extracted from the current density - voltage (J-V) characteristics. The PCE of iOSCs manufactured with the stack V2O5/ NiO (0.5 nm), V2O5/NiO(1 nm) and V2O5/NiO(0 nm) reached the 80% (TS80) after 1600 h, 190 h and 84 h, respectively.
XBAROPT - Enabling Ultra-Pipelined, Novel STT MRAM Based Processing-in-Memory DNN Accelerator

An explosion in big data driven machine learning (ML) applications in conjunction with a severe slowdown of Moore’s Law are prompting the search for alternative application-specific hardware fabrics. With its focus on bringing the compute inside memory bitcells, processing-in-memory (PIM) has been proposed to accelerate ML inference applications. In this paper, we present a modular, end-to-end simulation framework that is required to find a power-performance optimized solution for PIM based architectures for a given application. Our simulation framework encompasses multiple levels of hierarchies including device bitcell, array, memory hierarchy, dataflow, data re-use and algorithm-to-system mapping. Novel concepts at two levels of the hierarchy are introduced and evaluated: 1. Logic embeddable, high Ion/Ioff Magnetic Tunnel Junction (MTJ) bitcell and 2. Cycle accurate inter and intra layer pipelined operation for high performance and low power operations. Results are compared to pure digital custom ASIC implementation showing orders of magnitude improvements in power-performance on widely accepted MLPerf benchmarks.
Joint Radio Resource Management of Channel-Assignment and User-Association for Load Balancing in Dense WLAN Environment

IEEE 802.11-based wireless local area network (WLAN) is currently the most popular communication system and a lot of access points (APs) have been densely deployed over heavily populated areas. In such dense WLAN environment, the WLAN users can suffer from performance degradation due to high co-channel and adjacent-channel interference among APs. To tackling such user performance degradation problem, we propose a two-phase radio resource management (RRM) framework, of which the first phase is channel assignment (CA) and the second phase is user association for channel load balancing (UA-ChLB). In designing the RRM framework, we take account of typical WLAN environment where dual bands are supported and two types of APs coexist: controlled-APs, which are managed by a centralized controller, and stand-alone APs, which independently operate for themselves. The proposed CA method utilizes a channel bonding technique, which provides a high data rate by integrating multiple basic channels while reducing the interference among neighboring APs. The proposed hybrid UA-ChLB scheme, which is composed of distributed/centralized UA-ChLB, efficiently coordinates the channel load among neighboring APs and between two bands of 2.4 and 5 GHz, under consideration of the wireless channel quality, interference, and traffic conditions. We implement a prototype system in practice using the proposed RRM scheme. The experimental results show that the proposed RRM scheme greatly improves both throughput and fairness in dense WLAN environment, as compared with some existing schemes.
A 6-Transistor Ultra-Low Power CMOS Voltage Reference with 0.02%/V Line Sensitivity

This work presents a technique for design of ultralow power (ULP) CMOS voltage references achieving extremely low line sensitivity while maintaining state-of-the-art temperature insensitivity through the use of a 6-transistor (6 $T$) structure. The proposed technique demonstrates good performance in sub-100 nm CMOS technologies. The 65-nm CMOS implementation occupies only 840 µ m2 of area and consumes 28.6 pA from a 0.5 V supply. Measurements from 6 samples from the same wafer show an average line sensitivity of 0.02 %/V, a 10X improvement over previous 65 nm implementations, and an average temperature coefficient of 99.2 ppm/°C.
Efficient Low-Cost Ship Detection for SAR Imagery Based on Simplified U-Net

Due to the rapid development of chip technology and deep learning revolution, many ship detection frameworks for synthetic aperture radar (SAR) imagery based on convolutional neural networks (CNNs) have been proposed and achieved great success. However, there are problems hampering their development: 1) For the SAR ship detection task, it is uneconomic to apply heavy backbone network to extract features because it results in heavy computing load and prolongs the inference time cost; 2) The anchor-based methods usually have massive hyper-parameters, which typically need to be tuned carefully and easily lead to weak detection performance. To alleviate the problems, an efficient low-cost ship detection network for SAR imagery is proposed in this paper. Firstly, a simplified U-Net as the backbone to extract features is proposed. It only contains ~0.47 million learnable weights, which is 2.37%, 0.76%, 0.34%, 1.01%, 0.55% and 1.07% of DarkNet-19, DarkNet-53, VGG-16, ResNet-50, ResNet-101 and ResNext-101, respectively. Secondly, an anchor-free SAR ship detection framework consisting of a bounding boxes regression sub-net and a score map regression sub-net based on simplified U-Net is proposed. To evaluate the effectiveness of our method, extensive experiments have been conducted and a more comprehensive set of evaluation metrics have been applied. Results demonstrate that the proposed network achieves 68.1% average precision and 67.6% average recall on the SAR ship detection dataset (SSDD), respectively. Compared with the state-of-the-art works, our proposed network achieves very competitive detection performance and extreme lightweight (~0.93 million learnable weights in total).