Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Matching Based on Phase Congruency Corner Detection

Feature point matching is most commonly adopted among all kinds of stereo image matching.The result of feature point matching is affected by many factors such as object occlusions,lighting conditions and noises,therefore it is important to find a robust algorithm of feature point detection.In this paper described a new corner detector developed from the phase congruency model of feature detection.The new operator uses the principal moments of the phase congruency information to determine the corners and results in reliable feature detection under varying illumination conditions with fixed thresholds.A new feature point matching algorithm is then proposed.It employs the condition that the depth of the scene is locally continuous as extra constraint,and uses the method for extended assignment problem for global optimization.Experiments showed that the results of the algorithm are satisfactory.
Influence of reduced dietary protein level on quality of pork carcasses in Windsnyer pigs

To promote the sustainable production of local pigs, their dietary protein requirements need to be determined. Meat production from these pigs when fed on appropriate diets, coupled with their adaptability to climatic extremes and disease and parasite challenges, could be of huge benefit to the pork industry. The objective of the study was to determine the carcass traits, primary pork cuts, and internal organ weights of pigs fed decreasing dietary protein levels. Thirty, slow-growing, Windsnyer male pigs were randomly allocated to six dietary treatments in a complete randomized design. There were five replications for each of the six treatments. Dietary crude protein levels in the six experimental diets were 193, 174, 154, 135, 116, and 97 g/kg, respectively. The diets were formulated to contain similar net energy levels of ~9.5 MJ/kg. Lysine, methionine, threonine, and tryptophan levels were the same for all diets. A two week adaptation was followed by an 8w feeding phase. At slaughter, pigs had an average weight of ~39.13±0.85 kg. Pigs were humanely slaughtered at the end of feeding period to determine carcass characteristics, primary pork cuts, and internal organ size. A negative linear relationship was observed between protein levels and cooler shrink. There was a positive linear relationship between protein level and dressing percentage, cooler shrink, and shoulder fat. There was a quadratic relationship between dietary protein level and shoulder fat, ham diameter, P2(3) backfat depth, and kidney weight. The thickness of dorsal fat at the last rib, the thickness of back fat, and the width of back fat at P2(2) increased linearly as protein level decreased. The reduction in dietary protein level had an influence on carcass traits, primal pork cuts, and internal organs in slow-growing Windsnyer pigs. A reduction in dietary protein level below 116 g/kg compromised ham diameter, P2(3) width of back fat thickness, shoulder fat, and kidney weight.
Detecting COVID using CNN from Chest X-Beams

Since inception of Corona Virus, 47.6 Cr. individuals got infected and 61L deaths occurred. Still it’s going on and spreading across the world. Many health workers, researchers, experts, scientists are making efforts to slow down its pace & putting efforts in evaluating the techniques to detect it. For this, it is highly required to understand the virus & its versions. It is a part of SARS – Severe acute respiratory syndrome. To detect COVID, there are numerous ways but using Chest X-beams we are able to reduce the detection time and cost. To evaluate the Chest X-beams we need radiologists. So here, we develop a model to identify COVID X-beam in comparison to Normal X-beam. These days DL algo’s are producing best results in classification. A pre-trained CNN models using large datasets is to preferred for image classification. Firstly our models need to be trained and then tested to recognize the images of X-beams of one of the either case. Logically we have to locate the best CNN model for diagnosis.
Utilization Of Reduced Switch Components With Different Topologies In Multi-Level Inverter For Renewable Energy Applications-A Detailed Review

Multilevel inverters become more popular and attracted many in research and industrial applications. The MLI's were widely chosen for their higher and medium power/ voltage applications which were very helpful for interfacing non-conventional energy sources. Also for the past decade the research under development of reduced switch MLI topologies rapidly increased by making appropriate combination of switch connections. It will provide multi-level outputs with less harmonic distortions. Due to this, reduced switch MLI is most popularly used for power converter topologies. The various traditional multi-level inverter topologies and hybrid MLI schemes which are applied for non-conventional energy sources are discussed in this research paper and also it includes distinct modulation schemes to increase the performance of MLI.
Performance Enhancement of Conventional Design of 4-Bit Carry Look-Ahead Adder

This research aims to increase performance parameters of conventional CMOS based 4-bit carry look-ahead (CLA) adder. CLA adder in conventional design fully utilize static CMOS (S-CMOS) logic for which its transistor count is quite high. Moreover, due to high input impedance, its delay is also high. To enhance performance and to reduce transistor count, (1) full swing gate diffusion input (GDI) based AND gates and (2) GDI based XOR gates have been utilized rather than using S-CMOS based AND and XOR gates. According to simulation conducted using Cadence tools, the proposed CLA adder design achieved noteworthy improvements in performance compared to the fully S-CMOS based existing design.
Low-latency remote-offloading system for accelerator offloading

Specific workloads are increasingly offloaded to accelerators such as graphic processing unit (GPU) and field-programmable gate array (FPGA) for real-time processing and computing efficiency. Accelerators are expensive components and consume much power, so it is desirable to increase the efficiency of accelerator utilization by sharing accelerators among multiple servers over a network. However, task offloading over a network has the problem of latency due to network processing overhead in remote offloading. This paper proposes a low-latency system for accelerator offloading over a network. To reduce overheads of remote offloading, we propose a system composed of (1) fast recombination processing of chunked data with a simple protocol to reduce number of memory copy, (2) polling-based receiving data checking to reduce overhead due to interrupts in interaction with a network interface card, and (3) a run-to-completion model in network processing and accelerator offloading to reduce overhead with context switching. We show that the system can improve performance by 53.99% compared with a simple implementation and confirmed the performance with a virtual radio access network use-case as a low-latency application.
Power Allocation in a Cell-Free MIMO System using Reinforcement Learning-Based Approach

In this paper, we revisit the max-product signal-to-interference-plus-noise ratio (SINR) power allocation policy in a cell-free multiple-input multiple-output (MIMO) system that was previously approached using the model and data-driven based methods. We develop a reinforcement learning (RL) algorithm to be employed by all access points to compute their best power allocation profile in real-time. Model-free temporal difference RL algorithms are designed for the max-product SINR power optimization strategy inspired by state-action-reward-state-action (SARSA), Q -learning, and expected SARSA. The proposed algorithms find suboptimal scalable power allocation solutions for cell-free systems with less dependency on the underlying system model and less computational complexity.
The Degradation Mechanism and Similar Characterization for IGBT

Insulated-gate bipolar transistors (IGBT) are widely used in power electronic systems. The reliability of their operation is very important for social life. The detection of key Degradation characteristic parameters is an important technical basis to realize the fault pre-diagnosis of power electronic systems, which is of great significance to improve the stability of power electronic systems. In this paper, various degradation types of power electronic devices are firstly introduced, and the mechanisms of degradation are discussed in detail. Secondly, the feasibility of degradation characterization based on key characteristic parameters (voltage rise time) is studied. It is shown that the evolution of each degradation can be represented by a key characteristic parameter (voltage rise time), and ultimately by a similar junction temperature shift. Finally, the development prospects of multiple degradation pre-diagnosis based on key parameters are prospected.
No-Reference 3D Point Cloud Quality Assessment Using Multi-View Projection and Deep Convolutional Neural Network

Digital representation of 3D content in the form of 3D point clouds (PC) has gained increasing interest and has emerged in various computer vision applications. However, various degradation may appear on the PC during acquisition, transmission, or treatment steps in the 3D processing pipeline. Therefore, several Full-Reference, Reduced-Reference, and No-Reference metrics have been proposed to estimate the visual quality of PC. However, Full-Reference and Reduced-Reference metrics require reference information, which is not accessible in real-world applications, and No-Reference metrics still lack precision in evaluating the PC quality. In this context, we propose a novel deep learning-based method for No-Reference Point Cloud Quality Assessment (NR-PCQA) that aims to automatically predict the perceived visual quality of the PC without using the reference content. More specifically, in order to imitate the human visual system during the PC quality evaluation that captures the geometric and color degradation, we render the PC into different 2D views using a perspective projection. Then, the projected 2D views are divided into patches that are fed to a Convolutional Neural Network (CNN) to learn sophisticated and discriminative visual quality features for evaluating the local quality of each patch. Finally, the overall quality score of the PC is obtained by pooling the quality score patches. We conduct extensive experiments on three benchmark databases: ICIP2020, SJTU, and WPC, and we compare the proposed model to the existing Full-Reference, Reduced-Reference, and No-Reference state-of-the-art methods. Based on the experimental results, our proposed model achieves high correlations with the subjective quality scores and outperforms the state-of-the-art methods.
Decentralized incentive-based DDoS mitigation

DDoS attacks continue to pose a major threat to the Internet’s stability. Current solutions are costly and require deployment of dedicated expansive scrubbing centers. We present DDP, an incentive based Decentralized DDoS Protection mechanism that enables operators to apply near attack-source DDoS mitigation, using network devices which are already deployed in their network. DDP is based on blockchain and smart contracts that make all accounting transparent to all participant operators, allowing them to get accurate compensation according to the effort they invest. This is a unique advantage of DDP as it enables operators to charge for protection, providing an incentive for them to participate in DDP. We provide a demo tool that enables us to measure the percentage of filtered attack traffic per the operators’ participation percentage. We show that even at participation rate of 50% we get filtration rate of 89.2% and 0% false positive.
Impact of Machine and Deep Learning in Drug Repurposing

Deep learning has recently received massive attention on many areas of science and technology such as computer games, speech recognition, computer vision, natural language processing, self-driving cars, among others. Conventional Methods of drug discovery is time consuming and costlier. Drug repurposing is considered to be a promising method to identify the new drug target interactions for the existing drugs. To reduce the development costs and time, various approaches have suggested for repurposing of drug. In this paper, we are going to discuss about several machine and deep learning approaches used for drug repurposing.
Prediction of Criminal Behavior Based on Genetic Data - A Review

Modern day research in genetics related to behavior of a human being suggests a prudent linking between criminal behavior, some gene variants and childhood experiences. Though the analysis linking this relationship is still in the early stages, and the criminal proceedings are already incorporating genetic information. Polymorphism has been linked to phenotypes of the criminal justice system, according to previous studies. The majority contrast the effects of high MAOA alleles (3.5-repeat, 4-repeat, and occasionally 5-repeat alleles) with those of low MAOA activity alleles (2-repeat and 3repeat alleles), finding that the latter impart an increased risk of antisocial characteristics. This brief paper summarizes the different studies and different aspects which can be used for the Prediction of Criminal behavior based on genes and other factors. Machine learning can be used for criminal behavior detection also, but there are a lot of factors that are considered but the most important one is the MAOA Gene.
Texture Analysis on Digital Microscopic Leather Images For Species Identification

This paper describes the relevance of texture analysis on leather images. The aim is to improve the prediction accuracy by quantifying the morphological and statistical behavior of the leather images. Hence, the present work proposed to combine the multi-resolution discrete wavelet transform (DWT) and local binary pattern (LBP) texture operators. The hybrid texture features (DWT + LBP) offer better species-specific feature discrimination. This work adopts a multi-layer perceptron (MLP) model to evaluate the discriminatory behavior of the texture features. The proposed work extract, analyze and learn the species' distinct texture features of the novel digital microscopic leather image data. The experimental results noted a significant improvement in species prediction with 99.58% accuracy. Therefore, texture analysis elevates the ability to interpret the leather images per species. It is thus a necessary key to learn the permissible leather species' behavior so as to prevent the trade of non-permissible leather and its products.
Determination of the Optimal Air Gap of an HFCT

When using high-frequency current transformers (HFCT) to measure partial discharges (PD) on power cables, saturation of the sensor's ferromagnetic core is a known problem. Saturation is usually not caused by the PD signals, but by the 50 Hz (or 60 Hz) operating current of the power cable. Saturation falsifies the measurement results, reduces the HFCT sensitivity and should therefore be avoided as much as possible. A typical method to avoid saturation is to insert air gaps in the ferrite core, i.e., to use a split-core HFCT. However, finding the optimal air gap length is a complicated task as it depends on the core material and degree of saturation. If the air gap is too small, the saturation will not be reduced enough; too large an air gap reduces HFCT sensitivity. In order to find the optimal air gap of an exemplary split-core HFCT, we performed many measurements. This paper presents all the results and derives a function to calculate the optimal air gap for our HFCT. To achieve the highest possible sensitivity, the air gap must be adjusted depending on the 50 Hz operating current. Based on this result, we plan to develop a split-core HFCT with air gap control in the future.
A Unified Metric for Fast Frequency Response in Low-Inertia Power Systems

Future power systems with more inverter-based resources (IBRs), will be vulnerable to frequency decline contingencies. Fast frequency response (FFR) provided by IBRs is a good candidate to arrest frequency excursions. Diverse types of FFR have been proposed, and some have been deployed in our power systems. Without a unified quantification of FFR, it is hard for the grid operators to compare and fully leverage the FFR capabilities of IBRs. This work introduces a potential unified metric that quantifies two key characteristics of FFR and describes its application to three prevailing FFR types. We then use metric-to-frequency mapping to validate the accuracy of the metric in predicting the impact of a given FFR on the trajectory of a frequency event. The results show that the proposed metric is simple yet accurately captures the ability of diverse forms of FFR to improve system frequency dynamics.
Open Radio Access Network challenges for Next Generation Mobile Network

Due to the tightly-coupled hardware and software architecture of existing RAN systems and their non-flexibility, disaggregation of software and hardware can bring many unprecedented opportunities regarding enabling the entrance of multiple small-scale infrastructure providers to enter the RAN market, which creates more competitive and innovative RAN ecosystem. Moreover, mobile network operators (MNOs) will also have the advantage of selecting the services according to their network requirements. Open Radio Access Network (O-RAN) builds a multi-vendor RAN ecosystem and utilizes openness and intelligence to address the complexity of network functionalities, increase development agility, and provide more cost-effective platforms due to softwarization and the avoidance of dedicated hardware. However, O-RAN faces many challenges, such as interoperability, convergence, and AI/ML management which still need to be addressed before its wide deployment. This paper surveys the existing issues in the Open RAN ecosystem and explores existing solutions.
Evaluating the performance of the Quantum Approximate Optimisation Algorithm to solve the Quadratic Assignment Problem

The performance of the Quantum Approximate Optimisation Algorithm (QAOA) in solving the Quadratic Assignment Problem (QAP) is evaluated, with the Variational Quantum Eigensolver (VQE) as a benchmark. The QAP is directly revelant to numerous industry scenarios. The QAP, a Combinatorial Optimisation Problem (COP), is classified as $\mathcal{NP}$ -Hard. This classification means CPU time increases exponentially as the problem size scales when solving the QAP using deterministic optimisation techniques. Therefore, this work investigates the QAOA in search of a non-deterministic optimisation technique to efficiently obtain solutions to the QAP. This research compares two warm start techniques to solve QAP instances of sizes 3 to 7. The metrics of comparison - that measure efficiency and solution quality - were introduced in previous work on this topic. For the QAOA, the impact of the p-value, a determination of circuit depth, is investigated. Of the two quantum hybrid heuristics, the VQE retrieves solutions in a shorter computational time with a smaller circuit size, which allows for solving instances with a larger problem size. Compared to the VQE, the QAOA performs better in terms of feasibility as the problem size scales. The quantum warm start method results implies that the QAOA may not maintain higher solution quality for instances larger than size 4. Still, further investigation should be conducted once quantum devices with more qubits and higher quantum volumes are available.
An Intensive review on implementation of Big Data in different applications and its associated issues and Challenges

Combining analytical modeling with Big Information Analytics is a trend lately in characteristic extraction. This entails collecting expertise from reliable data sources, swiftly evaluating data, and making future projections. Big data consists of enormous volumes of information that are difficult to examine using conventional data & computing techniques. Big dataset applications have a technical challenge when transferring data between different places, which would be highly costly and requires a large main memory for data storage before treatment. In a cloud setting, big data is the exchange and engagement of information whose scale and complexity exceed the customary technical prowess of data collection, organization, and analysis. This article offers a thorough analysis of several big analytics techniques. Additionally, it offers a thorough analysis of numerous Big Data issues and difficulties.
An optimal planning approach for EV charging station installation in Distribution Network

The increasing adoption of electric vehicles (EVs) over conventional internal combustion engine based vehicles has led to the need for building charging infrastructure for sustaining the popularity of EVs. Installation of charging facility means an additional load demand increase for the distribution system. The location, size and type of charging station must be optimally planned so as to minimize the effect of EV charging station load and ensure the safe operation of the network. In this work, various case studies are performed to investigate the impact of location, number and type of charging station and subsequently optimal planning is done taking into the account the charging need of EV users. To validate the effectiveness, the results of optimal planning are compared to random planning. Further, it was observed that voltage limits at some buses are violated which is corrected by introducing a DG unit of optimal size at optimal location. The entire study has been performed on the IEEE 33 bus distribution system.
Guava Bruise Area Calculation Using Color And Grayscale Image Segmentation

A bruise area calculation method on guava fruit surface was presented that relied on image segmentation by identifying boundaries or edges appearing in the guava image. Boundary detection was used to calculate the entire area of the guava and this was then compared with the bruise area. Image data presented as color combination of R, G and B was difficult to apply and performed poorly, while simple foreground and edge segmentation methods using grayscale images provided good results. The bruise region had maximum image intensity and the thresholding image segmentation method was used to specify this area. Results were not acceptable because of sensitivity to light and shadow. A color image segmentation method based on thresholding was used to overcome this issue. Two color spaces, HSV and L*a*b, clearly distinguished between the color of the guava and the color of the bruise. A specific portion of the covered color area could be specified in both color spaces and both produced good results. After specifying the desired region, the calculated area was compared between the grayscale and color models. Color image segmentation by the color thresholder produced good results that matched our data.
Differential‐phase reflectometry for edge profile measurements on Tokamak fusion test reactor

Edge electron density profile measurements, including the scrape‐off layer, have been made during ion cyclotron range of frequency (ICRF) heating with the two‐frequency differential‐phase reflectometer installed on an ICRF antenna on the Tokamak fusion test reactor (TFTR). This system probes the plasma using the extraordinary mode with two signals swept from 90 to 118 GHz, while maintaining a fixed‐difference frequency of 125 MHz. The extraordinary mode is used to obtain density profiles in the range of 1×1011–3×1013 cm−3 in high‐field (4.5–4.9 T) full‐size (R0=2.62 m, a=0.96 m) TFTR plasmas. The reflectometer launcher is located in an ICRF antenna and views the plasma through a small penetration in the center of the Faraday shield. A 26‐m‐long overmoded waveguide run connects the launcher to the reflectometer microwave electronics. Profile measurements made with this reflectometer system will be presented along with a discussion of the characteristics of this differential phase reflectometer and data ana.