Miracle-dz/UniFAR-FTUs huggingface dataset:
Antenna Array Design for Coherent MIMO Radar Networks

High angular resolution provides improved environmental perception and increases the detection quality of extended targets. It is therefore a key requirement towards future radar systems for autonomous driving. The angular resolution of a radar system fundamentally depends on its antenna array aperture size. It is technically difficult and economically challenging to realize a large aperture radar system as a single sensor. Radar networks, consisting of multiple individual radar sensors, mitigate the challenges caused by creating a large aperture radar system. This paper presents a radar network consisting of two individual MIMO radar sensors equipped with L-shaped physical antenna arrays. L-shaped arrays for the individual sensors are chosen to achieve a rectangular equally spaced radar network vir-tual aperture. Furthermore, the paper discusses the performance of the resulting virtual aperture in the context of DoA estimation. Measurements of a bicycle, conducted with a coherently coupled radar network consisting of 768 virtual channels, demonstrate the performance of a high angular resolution radar system.
Apply Distributed CNN on Genomics to accelerate Transcription-Factor TAL1 Motif Prediction

Big Data works perfectly along with Deep learning to extract knowledge from a huge amount of data. However, this processing could take a lot of training time. Genomics is a Big Data science with high dimensionality. It relies on deep learning to solve complicated problems in certain diseases like cancer by using different DNA information such as the transcription factor. TAL1 is a transcription factor that is essential for the development of hematopoiesis and of the vascular system. In this paper, we highlight the potential of deep learning in the field of genomics and its challenges such as the training time that takes hours, weeks, and in some cases months. Therefore, we propose to apply a distributed deep learning implementation based on Convolutional Neural Networks (CNN) that showed good results in decreasing the training time and enhancing the accuracy performance with 95% by using multiple GPU and TPU as accelerators. We proved the efficiency of using a distributed strategy based on data-parallelism in predicting the transcription-factor TAL1 motif faster.
The SN 1986 G in Centaurus A

Photometric and spectroscopic observations of SN 1986G, spanning up to one year after the explosion, are presented. The overall photometric behavior of this SN Ia results rather normal. The maximum at B = 12.45 + 0.05 was reached on May 11, 1986, then the decline to the inflection point was relatively fast, but similar to other SNe. It is shown that the fast, early decline rate is not due, as suspected before, to the combined effect of the strong differential absorption and peculiar spectral evolution of this SN. Also, the late time decline rate resembles that of other SN Ia. The particular position of the SN in the two-color diagram may be due to some undetected spectral peculiarity in the UV-visible gap of the available observations
Detection and Recognition of Animals Using Yolo Algorithm

Animal recognition and tracking systems have the potential to provide accurate and cost-effective monitoring of animal populations and their behaviour in the wild, which can be crucial for conservation efforts and ecological research. This study explores the utilization of the YOLO version 3 object detection and recognition algorithm, in conjunction with Firebase technology, to trigger an application that sends real-time data to nearby safari vehicles for animal tracking purposes. However, it is important to consider the ethical and privacy concerns of both the animals and the people involved when developing and using animal recognition and tracking systems and to ensure that adequate training and support are provided for users, particularly in cases where it involves interaction with wild animals.
Survey: An IOT Based Approach for Hybrid Security Management in Health Care for Secure Cloud Storage

In some Emergency Situations, Patients must be monitored continuously and treated for. However, going to the hospital to perform such work is challenging due to time restraints. A virtual care monitoring system can help with this. With the rising use of cloud computing, the suggested system presents an efficient data science technique for IoT enabled healthcare monitoring systems that improve the productivity of data processing and the use of data in the cloud. IoT sensors in plenty are used to gather health care data. For the purposes of science processing, these data are stored in the cloud. An altered data science technique is initially introduced in the Healthcare Monitoring - Data Science Technique (HM-DST). The Upgraded Pigeon Optimization (IPO) technique is used to arrange the data that is kept in the cloud, which helps to increase prediction accuracy. The best method for selecting features is then illustrated for the removal and features selection. In order to analyze human healthcare, a backtracking search-based deep neural network (BS-DNN) is used. The performance of our system is then assessed using various real-time healthcare datasets, and changes are observed using the current smart healthcare monitoring systems. To assist the medical community in early detection of a patient’s primary organs’ health status, allowing for proper treatment, as well as to securely store patient health data in the cloud. The goal of the project is to demonstrate how the sensory system can be used to track a patient’s movement invisibly.
Towards Prediction of Bus Arrival Time using Multi-layer Perceptron (MLP) and MLP Regressor

Intelligent transport systems have been in research and development in recent decades. However, not all countries can afford to deploy such systems for the public usage. Conventional public transport systems such as public buses are still the main mode of public transportation system in many developing countries. Due to the issue of public transportation's inaccurate bus arrival timing, the general public still prefers private transportation. The goal of this study is to investigate the use of machine learning to improve the prediction accuracy of bus arrival timing. Two machine learning models, a multi-layer perceptron (MLP) and a MLP regressor, were compared in terms of their performance on small datasets. The experiment data was collected from Kulai-Johor Bahru Sentral bus route in Malaysia and cleaned to negate errors that influenced the accuracy of the models. The performances of the models were analysed and discussed and we observed that the MLP outperforms the MLP regressor. A limitation of this study is the small dataset that only comprises bus location data collected on a single bus route.
OFDM based WiFi Passive Sensing: a reference-free non-coherent approach

WiFi based passive sensing is attracting considerable interest in the scientific community for both research and commercial purposes. In this work, we aim at taking a step forward in an endeavor to achieve good sensing capabilities employing compact, low-cost, and stand-alone WiFi sensors. To this end, we resort to a reference-free non-coherent signal processing scheme, where the presence of a moving target echo is sought by detecting the amplitude modulation that it produces on the direct signal transmitted from the WiFi access point. We first validate the proposed strategy against simulated data, identifying advantages and limitations. Then, we apply the conceived solution on experimental data collected in a small outdoor area with the purpose of detecting a small cooperative drone.
Comparative Analysis of DCVSL and MDCVSL Techniques in 18nm FinFET Technology

In the VLSI design circuit, average power dissipation is a very common source of concern. This paper aims to reduce the average power dissipation and other parameters that are performed in this paper notably propagation delay, EDP (energy delay product), and PDP (Power delay product). These two technologies have been compared based on 2:1 MUX using Differential Cascode Voltage Switch Logic (DCVSL) and Modified Differential Cascode Voltage Switch Logic (MDCVSL) has been presented with FinFET 18nm technology. The results show values of Power Delay Product (PDP) and Energy Delay Product (PDP) for DCVSL based on 2:1 MUX using FinFET approach at different voltage levels notably at 1.2V is 1612.41× 10-15 (J), at 1V is 566.744× 10-15 (J) and at 0.8V is 198.177× 10-15(J). EDP of DCVSL at 1.2V is 4075.68×10-22 (Js), at 1V is 1431.36× 10-22 (Js) and 0.8V is 5047.9× 10-22 (Js) respectively. MDCVSL using 2:1 MUX has shown efficiency in terms of both EDP and PDP over DCVSL using 2:1 MUX PDP of MDCVSL at 1.2V is 1230.164× 10-15 (J), at 1V is 400.542× 10-15 (J) and at 0.8V is 137.716 × 10-15(J). EDP of MDCVSL at 1.2V is 3107.39× 10-22 (Js), at 1V is 1010.92× 10-22 (Js) and 0.8V is 3471.6× 10-22 (Js). In relation to the DCVSL approach, MDCVSL has evidenced an improved transient response and lower energy usage. Consequently, the MDCVSL is used in both industrial and biological uses, and it can be used for digital and cascading circuits.
Application of Grammatical Evolution to the Determination of Constitutive Equations

Grammatical evolution (GE), which is a kind of evolutionary algorithm, can find an executable program or program fragment that will achieve a good fitness value for the given objective function to be minimized. The search algorithm of GE is very similar to the genetic algorithm (GA) except for the translation rules from genotype (bit-string) to phenotype (function or program). In this study, an original GE and three improved algorithms are applied for determining the constitutive relationship in the plane strain state. The numerical results show the convergence speed of the improved algorithm is faster than the original GE.
A Comparative Study on Clickbait Detection using Machine Learning Based Methods

Clickbait is a type of providing false content, intended to attract a variety of users and get engagement and monetary benefits. It makes users curious to click the link and follow the content in various formats like audio, video, text, and images. Clickbait detection is a critical and difficult task. Many researchers have proposed various techniques using deep learning techniques and machine learning techniques like Logistic Regres- sion, Linear Support Vector Machine, Adaboost, Multilayer Per- ceptron, Random Forest, Convolution Neural Networks(CNN), and Recurrent Convolutional Neural Networks (RCNN). To give a clear overview of the efficient algorithms, we went through some existing studies from 2016–2022, which proposed various clickbait detection methods. This review gives an exhaustive study of existing methods and also suggests some recommendations for further enhancements to be done by combining the various deep learning techniques and machine learning techniques.
AutoLoc: Toward Ubiquitous AoA-Based Indoor Localization Using Commodity WiFi

The past decade's research in RF-based indoor localization has shown the possibility of decimeter-level accuracy based on the Angle of Arrival (AoA) of WiFi signals. However, existing systems require either recurring manual calibrations or the assistance of extra sensors to calibrate uncertain initial phases for accurate AoA estimation, hindering their large-scale deployment. In this paper, we propose AutoLoc, a ubiquitous localization system that overcomes the above limitation and can be seamlessly applied to the widely deployed WiFi Access Points. The key insight underlining AutoLoc is that the uncertain initial phase is constant once the RF oscillators are frequency locked. To this end, we propose three main innovations. First, we eliminate the uncertain initial phase while preserving the geometric information by combining Channel State Information (CSI) from different locations. Second, we introduce a cooperative confidence-aware localization algorithm that accurately localizes the target by quantifying the confidence of all possible AoA candidates. Third, we design a two-phase localization framework to fortify AutoLoc in multipath-rich scenarios. We implement AutoLoc on Commercial Off-The-Shelf WiFi devices. The experiment results in large-scale environments reveal that AutoLoc can achieve comparable median accuracy and 54$\%$ better 90$\%$ percentile accuracy compared with the state-of-the-art localization approach with manual calibration.
E-commerce Application using PHP and Web Development: A Review

The world of books has seen a significant shift in recent years, with more and more people turning to online shopping for their literary needs. An e-commerce bookshop is a perfect solution for those who prefer the convenience of browsing and purchasing books from the comfort of their own homes. The purpose of this project is to create an e- commerce bookshop that meets the needs of book lovers around the world. The conventional offline bookstores on one hand require space and management while the online method has made it hassle-free. The e- commerce bookshop will be designed to provide customers with a wide selection of books across various genres, including fiction, non-fiction, biographies, self-help, and more. This e-commerce bookshop will provide customers with a convenient and enjoyable way to purchase books online. The site will be designed to meet the needs of book lovers, featuring a wide selection of books, user- friendly features, and a secure payment and delivery system.
Investigation of the Compass Biped Walker's Passive Dynamics and Trajectory Tracking Method for Chaos Control

The aim of this research is to manage the precise and rhythmic movements of a bipedal robot with a compass configuration as it undergoes a passive dynamic walk. To approximate the walking pattern of a biped robot, we implement an impulsive hybrid nonlinear system. To start with, we showcase the fact that a passive walk by a bipedal compass robot can exhibit unexpected behaviors like bifurcations and chaos with the aid of bifurcation diagrams. In order to suppress such complex nonlinear tendencies, we resort to two methods for regulating the bipedal compass-type robot and achieving a one-periodic gait. The control approach used in this study is based on the passive dynamic walking of the biped robot, and the objective is to follow the trajectory of the period-1 passive movement during the swing phase.
Joint consideration of Production, Quality and Maintenance: a Bibliometric Analysis

Dealing with the complexity of smart industries, and increasing their productivity has always been the research focus of researchers aiming at meeting the requirements of Production, Quality and Maintenance, as three fundamental pillars of industries. Given the direct link of this triplet, studies acting on these three functions are, generally, divided into two main types of optimization: Partial integration which considers jointly Production-Quality or Production-Maintenance, or Maintenance-Quality for optimizing productivity, and Full integration that consider them all. To understand the publications trends using the joint consideration of Production, Quality and Maintenance of the existing studies and to provide a holistic view, this paper performs a Systematic Literature Network Analysis SLNA that undertakes a bibliometric analysis from 1977 by the mean of: Performance Analysis and Science mapping using VosViewer. The main contribution of this paper is the classification of the investigated papers into thematics and the study of the relationships among the most frequent terms in theses papers. Analyses of 2306 original publications demonstrated that the joint consideration of Production and Quality is the oldest and the Joint consideration of Production-Maintenance is the most widely used, whereas the full consideration of Production, Quality and Maintenance is recent and related to advanced smart tools.
3D Hand Tracking with Induced Magnetic Field

Sensor-based 3D hand tracking is still challenging despite the massive exploration of different sensing modalities in the past decades. This work describes the design, implementation, and evaluation of a novel induced magnetic field-based 3D hand tracking system, aiming to address the shortcomings of existing approaches and supply an alternative solution. The system is composed of transmitters for magnetic field generation, a wrist-worn receiver for field strength sensing, and Zigbee units for synchronization. In more detail, the transmitters generate the oscillating magnetic fields with a registered sequence, the receiver coil senses the strength of the induced magnetic field by a customized three axes orthogonal coil, which is configured as the LC oscillator with the same oscillating frequency so that an induced current shows up when the receiver is located in the field of the generated magnetic field. Five scenarios are explored to evaluate the performance of the proposed system in hand tracking regarding the transmitters' deployment: “in front of a whiteboard”, “above a table”, “in front of and in a shelf”, “in front of the waist and chest”, and “around the waist”. The true-range multilateration method is used to compute the coordinates of the hand in 3D space. Compared with the ground truth collected by a commercial ultrasound positioning system, the presented magnetic field-based system shows a robust accuracy of around ten centimeters with the transmitters deployed both off-body and on-body(in front of the waist and chest), which indicates the feasibility of the proposed sensing modality in 3D hand tracking.
HiFiPot: a High-Fidelity Emulation Framework for Internet of Things Honeypots

Internet of Things (IoT) devices are easy targets for attackers. Preventing and counter-fighting this threat impose to capture and analyze attackers’ behaviors. Several IoT-oriented honeypots have been proposed recently while, in parallel, emulation techniques for IoT devices have been improved to allow firmware analysis of this type of devices.In this paper, our objective is to consolidate these two facets in a single framework called HiFiPot. It is capable of creating a high-interaction honeypot on-the-fly with a high fidelity, i.e. from a firmware image. Our technique improves the most recent stateof-the-art solution to emulate an IoT device without scarifying the furtiveness. It is based on an iterative learning procedure to automatically correct emulation errors and to ensure Internet connectivity while maintaining these corrections invisible to the attackers. Out of the 1,000 firmware images tested, 443 (44,3%) can be deployed as honeypot. More than 500 instances of HiFiPot were deployed in the wild, and received about 1,900 HTTP traversal attacks, and downloaded 31 distinct malware binaries (out of 909) among which eight were unknown.
Multiple Grapes Leaf Disease Identification Using an Optimal Deep Learning Model: Xception

One of the popular fruit yields in India is the grape. The spread of numerous diseases on grapes’ fruit, stem, and leaves causes a decline in production. Bacteria, fungi, viruses, etc. are the principal culprits behind leaf diseases. Diseases are a significant influence in restricting the yield of fruit, and they are frequently challenging to control. Correct control measures cannot be implemented at the right time for a disease without an accurate illness identification. One of the most popular methods for identifying and categorizing plant leaf infections is image processing. This study uses the Xception classification approach to help identify and categorize grape leaf diseases and datasets taken from an online source like Kaggle. There are a total of 8500 images of grape leaves to use in this research. Furthermore, two alternative methods KNN and SVM were evaluated in terms of their effectiveness in detecting illnesses in grape plants. The suggested Research model has an accuracy of 99% for detecting and classifying the tested grape leaf disease which has higher accuracy as compared to alternative models.
Darting-out Target Detection with NLOS Signals for Vehicle MIMO mmWave Radar

Detection and tracking of crossing objects are cru-cial in automated driving to ensure the safety of pedestrians and drivers. However, in urban scenarios, occlusions not only make it difficult to detect the crossing target in non line of sight (NLOS) area, but also make the crossing target in line of sight (LOS) area susceptible to multipath signals. In this paper, a method is proposed to effectively detect the darting-out target occluded by parked vehicle during the entire movement for millimeter wave (mmWave) radar. Two typical situations of electromagnetic (EM) propagation are studied when the target darts out from NLOS area to LOS area. The ground reflection EM signal is used to detection and tracking when the darting-out target is located in NLOS area. The reflecting plane of parked vehicle is estimated to assist in eliminating interference caused by NLOS signals when the darting-out target is located in LOS area. Experimental results ultimately validate the effectiveness of the proposed method.
Challenges for Miniaturized Neurostimulators: Design of wireless Bio/CMOS interfaces for brain stimulation

Neurostimulation addresses neurological and mental disorders such as Parkinson’s, blindness, deafness, and depression. Miniaturized, wireless, and free-floating neurostimulators improve spatial resolution, increase the surface of coverage, and reduce chronic trauma. Several techniques have been proposed over the last few years. This article focuses on some selected approaches that have been shown to be effective and promising.
Adoption of Digital Marketing Toward Digital Transformation in Indonesian Micro- and Small-Sized Enterprises

Digital transformation in various industries has led to considerable changes in organizational strategic behavior. Micro, small, and medium enterprises (MSMEs) in the global sense, have been under pressure to adopt information and communication technology to increase their performance. Digital marketing has started to be recognized as one potential enabler of digital transformation. Managerial practices and academic literature provide limited guidance on how digital marketing can be used as an enabler of digital transformation across enterprises. This is especially in dearth among the study of MSMEs, particularly in emerging countries such as Indonesia where MSMEs have significant major roles in economic growth and workforce absorption. Besides, only a small portion of Indonesian MSMEs has embraced digital technology. Using the case study method, this paper finds that digital marketing adoption of the MSMEs may serve as the first-level enabler of digital transformation by allowing the MSMEs to enhance its dynamic capabilities. This paper contributes to the area of digital transformation through a marketing perspective, something that is novel in the context of MSMEs. For managers, the results of the study provide actionable guidelines for the MSMEs on how to drive digital transformation with digital marketing as the entry-level of adoption.