Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Assessment of Three-Phase NPC-Based Grid Integrated Single-Stage Solar PV System With Reduced DC Bus Capacitor

This work proposes an effective reduction in the capacitor size of the dc bus of a conventional three-phase neutral point clamped (NPC) inverter-based grid integrated single-stage solar photovoltaic (PV) system. This has been done by implementing a metallized polypropylene film dc bus capacitor instead of an electrolytic capacitor. As an outcome, this enhances the system’s long-term reliability, reduces cost, and increases the system’s life expectancy. The unbalance in capacitor voltage due to a small dc bus capacitor is also identified. As a solution, a carrier-based modified modulation strategy is implemented to improve the dynamic response of neutral point voltage. A single-stage solar PV array is utilized in this article. An optimal power extraction scheme is adopted to extract solar PV power. The dc bus voltage overshoots issue that arises during the change in irradiation is resolved and minimal oscillations are observed. The implemented scheme allows the feeding of the extracted power to the three-phase grid via the NPC converter, which in turn diminishes the total harmonic distortion of the grid. The PI controller minimizes the voltage and current error. The system performance is observed using a MATLAB version simulation model, and authenticated through the experimental results obtained from the laboratory prototype.
Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning

Nowadays, the energy used in commercial, residential, and office buildings represents a significant amount of the total energy spent worldwide. In these contexts, energy can be dramatically reduced by understanding when there is a waste of such an important resource. This can allow both a meaningful saving on energy costs and a significant reduction in CO2 emissions. In this field, occupancy prediction can help limit energy waste by allowing clever use of appliances and systems according to the real presence of the final beneficiaries. The aim of the paper is twofold. On a side, it wants to propose an approach based on Federated Learning (FL) and Long Short-Term Memory neural networks for the occupancy prediction in several rooms of a building. On the other side, it wants to show how FL helps in the occupancy predictions for the spaces in which the training of a specific model was not already performed. Some simulation experiments will show the effectiveness of the proposed approach.
A Hardware-Friendly Low-Power Area-Efficient GMM-Based Analog Classifier For Skin Detection

Skin detection is the process of identifying skin pixels in a 2-D image or video. It is a vital part of today’s Computer Vision technology and widely spread among applications including medical diagnosis, cryptographic protection, localization tasks and human-machine interaction. To this end, this work proposes a novel, low power (31–35nW), low voltage (0.6V), area-efficient (2098µm2), GMM-Based analog classifier for skin detection. The architecture consists of bump circuits and a Lazzaro Winner-Take-All circuit. The presented classifier was designed and simulated in a TSMC 90nm CMOS process, using the Cadence IC Suite. To verify the proper operation of the proposed design, a real-world skin segmentation dataset is used. To confirm the circuit’s accuracy, post-layout simulation results were compared to a software-based implementation.
Electron energy-loss spectroscopy in the TEM

Electron energy-loss spectroscopy (EELS) is an analytical technique that measures the change in kinetic energy of electrons after they have interacted with a specimen. When carried out in a modern transmission electron microscope, EELS is capable of giving structural and chemical information about a solid, with a spatial resolution down to the atomic level in favourable cases. The energy resolution is typically 1 eV but can approach 0.1 eV if an electron-beam monochromator is used. This review provides an overview of EELS instrumentation and of the physics involved in the scattering of kilovolt electrons in solids. Features of the energy-loss spectrum are discussed, including plasmon peaks, inner-shell ionization edges and fine structure related to the electronic densities of states. Examples are given of the use of EELS for the measurement of local properties, including specimen thickness, mechanical and electronic properties (such as bandgap) and chemical composition. Factors that determine the spatial resolution of the analysis are outlined, including radiation damage to the specimen. Comparisons are made with related techniques, particularly x-ray absorption spectroscopy.
Vanishing Theorems for Conformally Compact Manifolds

Abstract We study L 2 harmonic p-forms on conformally compact manifolds with a rather weak boundary regularity assumption. We proved that if the lower bound of the curvature operator is great than or equal to −1 and the infimum of the L 2 spectrum of the Laplacian great than p(n − p) for some p ≤ n/2, then there is no nontrivial L 2 harmonic p-form.
Design and Analysis of Polarization and Pattern Reconfigurable Antenna for IRNSS S-Band Applications

In this paper, a polarization and pattern reconfigurable antenna is proposed for the 2.492 GHz S-band Indian Regional Navigation Satellite System (IRNSS) application. The proposed antenna consists of a square radiating element surrounded by an RF PIN diode-loaded octagonal parasitic ring to control the beamwidth of the radiation pattern in the presence of interference. The antenna is fed with four probes and integrated with a reconfigurable feeding network to control polarization reconfigurability. The proposed antenna can switch between left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). The -10 dB impedance bandwidth of the proposed antenna and feed network is well-matched. The total realized gain of the antenna is 5.6 dBi and 6.4 dBi for PIN diode ON and OFF conditions. The proposed geometry can be able to mitigate interference by reducing beamwidth by 5-6 dB near the horizon.
Demosaicing to Detect Demosaicing and Image Forgeries

The trustfulness of images is a critical concern. Digital photographs can no longer be assumed to be truthful; indeed, digital image editing tools can easily and convincingly alter the semantic content of an image. Being able to analyse an image to check for forgeries becomes of the utmost importance in many domains, from police investigations to fact-checking and journalism. We propose here to analyse traces left by the camera during demosaicing, one of the first steps of image formation. When an object is added or displaced on an image, the demosaicing traces can be disrupted, leaving forgery clues. In order to detect these inconsistencies, we explore the possibilities offered by double demosaicing. Computing the demosaicing residual of an image with different demosaicing algorithms and patterns enables one to find image regions with inconsistent demosaicing traces. We render the method fully automatic by a simple a contrario scheme computing forgery detection thresholds with statistical guarantees on the number of false alarms.
Simple criteria for the absence of the beam‐Weibel instability

Rigorously sufficient and approximately necessary conditions for the absence of the beam‐Weibel instability are derived. These conditions include previously known stability criteria and resolve the seeming contradiction that these modes can be stabilized by beam temperature when the plasma is cold, but they cannot be stabilized by beam temperature when the plasma has infinitesimally small temperature.
Impact Of Global Warming On Endangered Species Using Data Science

As environment conditions changes, species amassing and piece in biological networks might be rebuilt in unpredicted ways. Some territory types might become wiped out, while others might extend in size, making it more challenging to safeguard biological systems and species in their ongoing locales. As they are habitually natural surroundings specialists and somewhat uncommon, undetermined and endangered species are supposed to be excessively impacted by environmental change. Many species have been saved from being extinct due to the Endangered Species Act in the United States which was introduced in 1973. Critical habitat identification and conservation is an important technique for species preservation. It is difficult to designate and conserve habitats for protected species when future habitat circumstances are unknown. This problem can be addressed by using the concept of data science. Data science is all about data gathering, analysis and decision making. This helps us to plot graph from analyzed data and make future predictions. The Caspian Tiger went extinct in the year 1970 is an endangered species which are basically originated in Ukraine and southern Russia, Russia. According to a literature review, the primary determinants of climate specialty are hustle over extended periods of dry weather and yearly mean temperature. When existing species-present associations were compared to future scenarios, it was observed that rising emission rates resulted in a considerable loss of climatic region. In the proposed system we collect the speed of an animal as well as positional information using GPS of the animal and communicate it to the base station. Collected data is analyzed by exploratory data analysis. Later the movements of the animal are visualized graphically from this we can predict the behavior of the animal.
Infrared and visible image fusion method based on dual domain enhancement in low illumination environment

For the problem of low contrast and loss of details in the fused infrared and visible images under a low illumination environment, this paper proposes a fusion method based on dual-domain enhancement. Firstly, the infrared and visible images are preprocessed: the Retinex model is used to enhance the infrared image, then denoise, and the logarithmic image processing (LIP) model is used to enhance the visible image; then the edge-preserving filter is used to decompose the preprocessed image into base layer and detail layer; for the base layer, a fusion technology based on visual saliency mapping is used to control the image quality For the detail layer, a simple summation fusion strategy is used to retain more details; finally, the fused image is reconstructed by linear combination. Experimental results show that the proposed algorithm is superior to the contrast algorithm in subjective visual effect and objective index evaluation.
Verification of Feature Detection Through Thermal Imaging: An Extension of PiBase

PiBase is a low-cost, Internet-of-Things-capable security systems. It offers users an integrated system of hardware and software in the form of a basic smart camera made from a Raspberry Pi and off-the-shelf parts, an Android app, and a backend built for Google Firebase. Using Haar-feature cascade classifiers and Linear Binary Pattern Histograms, it attempts to provide comprehensive detection and recognition of potential security threats. Being only a prototype there are some vulnerabilities in the initial design. This new design addresses one security threat, namely the possibility of mimicking an authorized user’s appearance. This is achieved through the integration of thermal imaging alongside the original camera used in the system. Some challenges with this approach include maintaining low-cost and part accessibility, working within limitations of the hardware, and choosing an effective method of integration. The proposed solution addresses each of these, in addition to the original issue, by transforming the output of a low-resolution thermal sensor array into a kind of clipping-mask to filter out non-human objects from the input image before performing its other operations.
A Review of DDoS Attack Detection and Prevention Mechanisms in Clouds

Cloud provides access to shared pool of resources like storage, networking, and processing. Distributed denial of service attacks are dangerous for Cloud services because they mainly target the availability of resources. It is important to detect and prevent a DDoS attack for the continuity of Cloud services. In this review, we analyze the different mechanisms of detection and prevention of the DDoS attacks in Clouds. We identify the major DDoS attacks in Clouds and compare the frequently-used strategies to detect, prevent, and mitigate those attacks that will help the future researchers in this area.
SVM Based Brain Tumor Detection and Classification System

The brain is the most significant organ in the body since it controls and regulates all vital bodily activities, and a tumour is a bulk of tissue created by the growth of aberrant cells over time. A brain tumour is a tumour that has either grown or moved into the brain. To yet, no root cause has been found for the development of brain tumours. Malignant brain tumours have a relatively high death rate because they grow in the most important organ of the body, the brain, even though they are not particularly prevalent (1.8% of all recorded malignancies worldwide are brain tumours). Therefore, to reduce mortality, it is crucial to precisely identify brain tumours in their early stages. For the management of brain tumour diagnosis, we have presented a computer-assisted radiology system that will evaluate brain cancers from MRI scans. In this paper, we developed a model that accurately identifies tumours using SVM after segmenting pictures with the Watershed and PSO algorithms, DWT and PCA methods, and feature extraction.
Deep-Learning-Based Step Detection and Step Length Estimation With a Handheld IMU

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator.
Simulation of Facilities That Ensure Adaptive Functioning of Urban Street and Road Network

The key priority for the development of urban street and road network is the introduction of automated traffic control systems. One of the key elements of automated traffic control systems is the efficient management of traffic lights. Under conditions of maximum traffic load on the roadway, it is possible to implement such control by introducing an adaptive traffic control system. The authors analyzed the existing means of traffic flows simulation. Based on the possibilities of use of adaptive traffic control tools, it is suggested to use the Simulation of urban mobility (SUMO) software as a simulation tool. A feature of SUMO (Simulation of urban mobility) is the so-called “open source” and the possibility of its improvement, taking into account the tasks set by the researcher. By means of use of this software product, the simulation of the influence of adaptive traffic lights on the capacity of the road network of various types was carried out. Based on the results of the experiments, the main diagrams of traffic flows were built. A script was written in Python to interpret the simulation results and visualize the main traffic flow diagrams. The results show that the use of adaptive traffic control even at individual intersections gives an increase in capacity across the entire road network by 20-30%. The suggested results can be used in road network simulation of various types of networks using adaptive traffic lights.
Depth Perceived Trajectory Mapping using Precedent Obstacle Avoidance (POA) for Optimized AGV Locomotion

Machine Vision builds up a significant role in the act of achieving an instantial near perfect collision-free locomotion solution in AGV (Automated Guided Vehicle). For conservation of memory structured from earlier explored non-optimum path by the concerned mobile agent, VI (Visual Inference) constructs a technical precision in decision making to choose optimized path. Representation of transformed Geometric configurations of on-route obstacles in a specific indoor environment into spatial coordinates is achieved by Visual Perception. This paper basically focuses on collection of 3D depth scanned data of concerned indoor surrounding using RGB-D (Red, Green, Blue Depth) sensor for proper detection and avoidance of obstacles. The identification of obstacles is categorized using specially proposed technique of Sector Divided Precedence Obstacle Avoidance (POA). The work extends its findings with the incorporation of the results obtained from implementing the proposed 3D Depth SLAM (Simultaneous Localization And Mapping) for trajectory mapping. The subsequent sections portray structural working architecture, and experimental analysis with a brief numerical description of the constructed required algorithms, which would serve as an important memory for accomplishing optimum PTP (Point-To-Point) navigation by AGV in a specific GPS (Global Positioning System)-denied indoor architecture as well as outdoor environment. Visual and tabular presentation of experimental output confirms the reliability of the proposed research procedure to be carried out further in the domain of MRN (Mobile Robot Navigation).
Wireless Airborne IoT Network for Rural Water Quality Monitoring

Currently, the Chini Lake shores house around 500 indigenous people distributed across six villages with limited access to cellular tower communication coverage. This is mainly due to the challenging terrain profile and dense foliage. Therefore, it is important to establish a reliable line-of-sight (LoS) data transmission via a low-altitude platform (LAP). The high availability but low-cost wireless communication infrastructure such as LoRa is the perfect solution in this scenario. This will allow better coverage due to good propagation characteristics at lower frequency bands as well as the elevated platform. The solution shall also equip a wireless machine-to-machine (M2M) network, sensors technologies and a big data analytic enablement platform. In addition, the characterization of the wireless channel behaviour in Malaysia's tropical rural areas, where the propagated wireless signal suffers from several imperfections, such as attenuation, diffraction, scattering and absorption due to the presence of various surrounding elements is also being investigated. The outcome of this research is expected to offer a new understanding of the propagation behaviour of current and future wireless IoT technologies, thus helping the network engineer to perform accurate planning and deployment in a rural environment. With this solution, the indigenous Orang Asli community in Chini Lake, Pahang, Malaysia will have access to digital content, as well as water level alerts for mitigation of flooding and drought situations, and Internet access for the promotion of local products and services.
A Team Crowdsourcing Method Combining Competition and Collaboration

Crowdsourcing has become an effective measure to solve the problems that are hard for automated algorithms and require lots of human efforts to provide feedback. Recent studies proved that introducing competition mechanism could reduce the crowdsourcing cost compared with the "Pay-per-Task" scheme. However, in this scenario, those workers who do not get high scores will quickly lose interest and quit. Meanwhile, some researchers also found that assigning tasks to an artificially-formed team could create more productive value via the collaboration among workers. However, most previous studies randomly assign each worker to one of the teams, resulting in a great disparity in strength among teams. To address the issues, we propose a team crowdsourcing method combining competition and collaboration. Specifically, we explore a novel team formation approach, where each worker is assigned to the team according to their capability such that the strength of all team are balanced. In addition, we also design an incentive mechanism for the workers within a team can work together efficiently. Finally, the experiments on a real dataset demonstrate that our approach outperforms the enhanced baseline by around 10.7% in the overall value produced, the cost reduce about 9.3% for each task and ensure the accuracy of annotations.
Planar Endfire Multibeam Antenna Implementation for Millimeter Wave 5G Applications using a Passive Beam Switching Network

Multibeam antenna implementation is necessary for millimeter wave (mmW) 5G applications to overcome the attenuation losses effects at these high frequencies, and to improve the spatial coverage using spatial multiplexing. In this work, a single layered, planar, endfire, and linear antenna array of four elements has been designed, which is fed by a 4X4 Butler matrix based beam switching circuit to produce spatially distributed beams. This antenna system that is implemented using transmission lines has a total dimensions of 58.6 mm X 37.7 mm. Four beams produced using the proposed antenna system are spatially distributed with one beam each in 69°, 84°, 97.5° and 112° directions. Peak gain of 13 dBi, side lobe levels of -7.3 dB, and total efficiency of 95% have been achieved with the proposed design that has a wide impedance bandwidth ranging from 26.5 GHz to 30.5 GHz. These values of radiation parameters make this multibeam antenna ideal for mmW 5G implementation.
Prototype Design of Deep Learning-based Voice Control Model for Smart Home

The demand for smart home technology is increasing to help older people feel more comfortable at home. Smart home technology can support the elderly in independent daily activities. The Internet of Things (IoT) is currently one of the key platforms for data-driven smart homes. The automated process of recognizing or verifying an individual’s identification based on his speech is known as voice recognition or speaker recognition. The main challenge in adjusting to the evolution of conversations in society is that the systems generally refer to existing patterns in the database. Therefore, we propose a prototype design of a smart home’s deep learning-based voice control model. First, we develop the model based on the convolutional neural network (CNN) and deep neural network (DNN) to obtain the best accuracy. Then, we create a model-based CNN and DNN used to construct a voice recognition system independent of text and language. The simulation result shows that the proposed model could extract the voice sample. The result also indicates that the accuracy of using CNN is better than that of using DNN.