Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterned random matrices and method of moments

We present a unified approach to limiting spectral distribution (LSD) of patterned matrices via the moment method. We demonstrate relatively short proofs for the LSD of common matrices and provide insight into the nature of different LSD and their interrelations. The method is flexible enough to be applicable to matrices with appropriate dependent entries, banded matrices, and matrices of the form Ap = 1 XX 0 where X is a p × n matrix with real entries and p ! 1 with n = n(p) ! 1 and p/n ! y with 0 � y < 1. This approach raises interesting questions about the class of patterns for which LSD exists and the nature of the possible limits. In many cases the LSD are not known in any explicit forms and so deriving probabilistic properties of the limit are also interesting issues.
Analysing and Evaluating Complementarity of Multi-Modal Data Fusion in AD Diagnosis

The clinical progression of Alzheimer's disease( AD ) can't be accurately evaluated by single modality data alone. Multi-modal data have a good effect on the diagnosis of AD. Clarifying the complementarity between modalities is crucial for the assessment of each stage of AD. Few studies have specifically explored the complementarity between different modalities due to the lack of completely aligned and paired multi-modal data and the limitation of sample size. However, collecting the full set of aligned and paired data is expensive or even impractical. In addition, the limited number of samples poses a great challenge to the robustness of the model. In this paper, different machine learning( ML ) methods were used to explore data complementarity between T1-weighted magnetic resonance imaging ( MRI ), cerebrospinal fluid ( CSF ), and fluorodeoxyglucose-positron emission tomography ( FDG-PET ) modalities. The different modal data of Alzheimer's Neuroimaging Initiative ( ADNI ) and the self-extracted neuroimaging data were experimentally explored. Experiments show that there is obvious complementarity between MRI and CSF. By fusing MRI and CSF data, three binary classification tasks using multi-modal fusion data have achieved varying degrees of improvement. At the same time, we also explored the important features of multi-modal fusion data through SHapley Additive exPlanations ( SHAP ), and found that most important features are supported by relevant literature.
Capacity Bounds and Achievable rates of Visible Light Communications for Future Smart City

The Shannon expression derived for a power-limited linear time-invariant (LTI) channel with additive white Gaussian noise (AWGN) is often quoted as a capacity for visible light communications (VLC). However, the use of the Shannon expression, i.e., bandwidth times the logarithm of unity plus the signal to noise ratio (SNR), is challenged in intensity modulation with direct detection (IM/DD) VLC channel. In this paper, we overview some literature on capacity bounds and achievable rates of VLC. We classify the Shannon capacity as the upper bound for VLC with optical or electrical power constraints. A tight closed-form lower bound can be also derived with the peak optical power, average optical power and electrical power constraints. Moreover, with a specific modulation, the achievable rate can be derived by inverting the bit error rate (BER) error function to obtain the SNR needed to carry a certain constellation with for instance one-dimension pulse amplitude modulation (PAM) or two-dimension quadrature amplitude modulation (QAM) at an adequate BER or symbol error rate (SER). These yield Shannon-like expressions for the achievable rates, but with a correction factor for the SNR which is a function of the required uncoded BER. Since QAM is commonly utilized in each subcarrier of multi-carrier orthogonal frequency division multiplexing (OFDM), this suggests the achievable rates of QAM can be extended for the throughput calculation of DC-biased optical OFDM (DCO-OFDM). Numerical results indicate a gap between the lower bound and the achievable rates by the PAM and DCO-OFDM signaling, which means that neither PAM nor DCO-OFDM is the optimal modulation scheme for VLC channels and other capacity-achieving modulations can be further explored.
A Review on 3D Convolutional Neural Network

The convolutional neural network (CNN) is a potent and popular neural network types and has been crucial to deep learning in recent years. A standard CNN which is known as 2-dimensions CNN was first proposed to solve image classification problems at first. With advancements in science and technology, as well as the growth of the internet, tasks involving video analysis have received increased attention in recent years. As a result, it was suggested that a 3-dimensions convolutional neural network (3D CNN) be applied to tackle 3D image processing or video analysis. 3D CNN is primarily used in the field of human activity recognition, video classification, medical imaging detection, and so on. This paper offers a succinct introduction to the general architecture and representative models of 3D CNN, contrasts the variations between 2D CNN and 3D CNN, explores the widely used models derived from 3D CNN, and displays the application outcomes of 3D CNN.
Dynamic Modeling, Operations, and Steady-State Analysis of DFIG based Nordex S77 Wind Turbine

In the last few decades, particularly in the transmission network, the proportion of wind-generated electricity has grown dramatically. For the better performance of the power system, detailed and accurate mathematical modeling of wind turbine systems is essential. In this paper, control, modeling, and steady-state analysis of a 5th-order machine model of a Doubly Fed Induction Generator (DFIG) are presented. To enhance the performance as well as dynamics of a variable wind speed wind turbine having a doubly fed induction generator a control strategy for active power regulation is presented. A vector control strategy is used to independently regulate both the reactive and actual power created by the Doubly Fed Induction Generator. The modeling and simulation are performed in MATLAB/SIMULINK software. This study uses an industrial model of wind turbines Nordex S77 parameters as a base wind turbine model. The model turbine results are compared with the turbine wind-speed characteristics. Furthermore, detailed simulation results are presented with the variation in the wind speed at different modes of DFIG; the sub-synchronous, synchronous, and super-synchronous mode operations are analyzed.
A Monocular SLAM System Based on the ORB Features

With the rise of the AR/VR industry and the development of intelligent mobile robots and driverless vehicles, the industry has seen a spurt in demand for SLAM technology. On the other hand, the development of sensor technology, the abundance of computing resources, and the advancement of algorithms make SLAM technology transition from algorithm to product. The visual SLAM technology relies on the small size, low power consumption, and information-rich visual sensors to provide a map of the environment. Its positioning results in the map for robots in unknown environments, which is of great significance for robot automation and intelligent applications. Among the many vision SLAM technologies, ORB-SLAM stands out for its outstanding performance as an open-source project and a complete SLAM system that incorporates various current mainstream approaches. This paper presents the main components and methods of Monocular ORB-SLAM and tests and evaluates them using current popular datasets. Monocular ORB-SLAM can perform well in slow-movement, textured scenes but still needs to overcome the issue of tracking lost due to camera shake and fewer textures.
Satellite Design for CANSAT with Autorotatig payloads

This research work aims to design a miniature satellite while keeping within the guidelines provided for CANSAT Competition 2021. This model exhibits some of the basic functioning of an actual satellite including the real time telemetry, receiving commands, performing some basic tasks and collect information from its surroundings. The effectiveness of this satellite relies on economical, physical and technical factors. It demonstrates complete working of a man-made satellite including carrying an objective oriented payload and it’s separate functioning. Since it is a model, to ensure its proper functioning and robustness, it will be launched to a height of 725m with the help of a rocket.
Towards Socially Acceptable Food Type Recognition

Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user's food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of them has been widely used in our daily lives and, at the same time, socially acceptable enough for continuous wear. In this paper, we propose a food type recognition method that takes advantage of Airpods Pro, a pair of widely used wireless in-ear headphones designed by Apple, to recognize 20 different types of food. As far as we know, we are the first to use this socially acceptable commercial product to recognize food types. Audio and motion sensor data are collected from Airpods Pro. Then 135 representative features are extracted and selected to construct the recognition model using the lightGBM algorithm. A real-world data collection is conducted to comprehensively evaluate the performance of the proposed method for seven human subjects. The results show that the average f1-score reaches 94.4% for the ten-fold cross-validation test and 96.0% for the self-evaluation test.
Compositional, structural, and electrical characterization of plasma oxidized thin aluminum layers for spin-tunnel junctions

In this paper we present results on how the plasma oxidation of a thin (1.5 nm) Al layer proceeds. Transmission electron microscopy of a Co/Al–oxide multilayer was used to determine the thickness of the oxides and Rutherford backscattering spectrometry and elastic recoil detection were utilized in order to determine the oxygen content. The oxide was also characterized via ac impedance measurements. These measurements indicated that the oxidation of Al on Co occurs in three discrete steps.
Mitigating DDoS Attack using Machine Learning Approach in SDN

In the past few years, the phishing attacks have been increased exponentially which have not left any sector of the society unharmed. The most popular attack among phishing is DDoS attack in which a server is made unavailable by sending a lot of requests to the server at the same time by attacker. The reason behind these types of attacks can be anything such as demand for ransom money, revenge, competition or something else. In this research paper, we’ve discussed about DDoS attack and its detection techniques. A machine learning model is proposed for the detection of DDOS attack in the software defined network. The proposed model compares the accuracy, precision, recall and F1-score of various machine learning algorithms such as Logistic regression, SVM, XGBoost, Decision tree, KNN etc. The accuracy of various algorithms is predicted and on the basis of this accuracy, a detailed comparative analysis report is prepared.
Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.
Robust Perceptual Hashing for Fingerprint Template Protection

Perceptual hashing has proven its robustness in securing biometric templates, where several methods have been proposed in order to have a robust and discriminating hash code against acceptable attacks. This paper proposes a robust perceptual hashing scheme for biometric template protection that based on SIFT and Harris functions for the feature ex-traction/selection phase and on DWT and SVD functions for the hash code generation phase. The experimental results in this paper discussed the effect of distance value and image blocks size parameters on hash performances (robustness and discrimination), which are used in the feature selection phase and the hash generation phase of our method respectively. The results show that we have could to choose the optimal parameters values in the generation of our hash code to give a best tradeoff between discrimination and robustness against acceptable image manipulations.
Feature Selection and Parameter Optimization of Optimized Extreme Learning Machine for Motor Fault Detection

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and non-linear fault categories and the accuracy is relatively low. , we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model. , SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method. , it is applied to the real motor fault detection example. , and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.
Quantifying Throughput of Basic Blocks on ARM Microarchitectures by Static Code Analyzers: A Case Study on Kunpeng 920

The performance of high-performance computing (HPC) and other real-world applications is becoming unpre-dictable as the micro-architecture of the modern central processing unit (CPU) turns to be more and more complex. As a consequence, predicting the execution time of a code snippet is notoriously difficult. Basic block throughput predictor, also known as static code analyzer, provides a ubiquitous way to predict the execution time of a basic block, which includes assembly code segments without jump-related instructions. In this article, we build a workflow to faithfully run, collect and analyze basic blocks from real-world applications. Several static code analyzers are introduced, compared and optimized to show which one performs better on accuracy and other metrics on Kunpeng 920 processor. Under extensive experiments, we achieve state-of-the-art 86.7% accuracy in predicting the throughput of all basic blocks.
Game and Dynamic Communication Path-Based Pricing Strategies for Microgrids Under Communication Interruption

Nowadays, the microgrid cluster is an important application scenario for energy trading. In trading, one of the most important research directions is the issue of pricing. To determine reasonable pricing for the microgrid cluster, data communication is used to create the cyber-physical system (CPS), which can improve the observability of microgrids. Then, the following works are carried out in the CPS. In the physical layer: 1) Regarding trading between microgrids and the load, based on the generalized game theory, an optimal pricing strategy is proposed, which takes into account the interactive relationships among microgrids and transforms the pricing problem into a Nikaido-Isoda function to obtain the optimal prices conveniently; 2) Regarding peer-to-peer trading between two microgrids, based on evolutionary game theory and the penalty mechanism, the optimal sale price of the seller is selected with boundary rationality. In the cyber layer, regarding the communication interruption issue existing in pricing (i.e., the game process), based on the principle of matching the performance of the path with the importance degree of the data, a dynamic regulating method of paths is proposed, i.e., adopting a new path to re-transmit the interrupted data to the destination. Finally, the effect of the proposed strategies is verified by case studies.
Music Recommendation System Using Machine Learning

The Internet plays a crucial role in society, and many apps use suggestion structure. It has created a wide range of applications, a global local area, and built for different types of data. Today's proposal framework has altered how we currently view the things that are to our advantage. Additionally, it is possible due to the data sifting method that is used to predict the customer's propensity. The most well-known applications of the recommender system are in the areas of books, news, articles, music, records, movies, and other media. The outline of the machine learning calculations and tactics developed for the proposal framework are covered in this study.We have suggested a music suggestion framework dataset in this article. Three categories make up the proposal framework: collaborative filtering, content-based, and approach based on half a breed. Recommender frameworks are widely used these days, especially in informal groups and electronic industry, as communitarian sifting methods become more mature. This paper organises the community-based, i-based separating process that uses the information provided by clients, analyses it, and then offers the songs that are most suitable for the client at that particular time.The paper describes this methodology, its steps, and its limitations. The recommended music list is arranged using the K-means algorithm and is based on the ratings that previous listeners have given the music. Additionally, this helps customers find the music of their choice based on the cinema insights of other customers efficiently and effectively without wasting much time in meaningless reading.The newly introduced recommender framework generates recommendations using a variety of information, including information about customers, the items that are available, and previous transactions stored in modified data sets. The customer could then efficiently browse the choices and find the music of their choice.
Graphed-based K-Means and Shortest Distance Tree for the Construction of Elderly Safe Corridor Accident and Prevention Platform

As medicine, science, and technology improve, the average age of the population on earth is also increasing. The World Health Organization (WHO) reported that the average age of the countries that had joined and surveyed was 68 years old, and the highest country had over 80 years. The care of the elderly population requires more attention and careful planning. There are a variety of social support systems available for the elderly today. Apart from encouraging them to participate in various social affairs and activities, it is also necessary to consider the safety and accident prevention needs of the elderly when they move. This is one of the critical factors for the elderly to obtain various opportunities and resources. Those areas or locations where older adults are densely concentrated require a more effective police presence to maintain the safety of moving routes. This research primarily deals with arranging police forces based on a simulation of elderly individuals' action trajectories. The level of care that the elderly require is weighted. Furthermore, the weighted K-means Clustering method is used to find the clusters of the elderly and make the cluster center close to the elderly who need high care to deploy the police force to provide higher priority services. Then the shortest paths between the elderly settlements (cluster center) and the activity venue are found based on the consideration that the movement patterns of the elderly are mainly walking, and they are most likely to arrive at the activity venue within the shortest distance. The activity venues of the shortest paths are taken as the root to construct the shortest path tree. During tree construction, the number of elderly in each settlement is summed in each intersection node as the number of elderly passing through. Then the breadth-first search method is applied to find the multi-path convergence points that the most elderly pass through as the reference for recommending police force arrangement. According to the proposed method, the police can plan safety corridors for the elderly so that they may receive the best safety care while away from home.
IoT Based Health Supervision of Energy Storage System

Batteries are being used for storage of electricity. Electricity storage are fueling the use of batteries over wide range. Production of batteries will increase by nearly 500% in future to meet the growing demand for clean energy technologies. There are many applications in which they are used very rapidly such as Electric Vehicles, Home Energy Management System and different machines which are used in industry. The design of the battery and chemical nature is very important because all the factors for improving the system depend on this. The efficiency of the charging, reliability and lifetime depend on this. Approximately 50% of premature battery failures is caused by deep discharge (DD), heat, fast charging, and overcharging all accelerate the "aging" process. Moreover, Batteries are very susceptible to extreme temperature, charging at a low voltage or charging at high voltage and over charging. Keeping in view these problems, to deal with these exponentially increasing issues along with other mentioned challenges. One of the advanced and innovative methodologies is developing a battery management system. This is a more efficient method where the health of battery is sustained. This results in decreasing the aging process and enhance the battery life. In this project, state-of-charge, (SOC) state-of-health, (SOH) for monitoring the state of battery different sensors and microcontrollers will be used to visualize data on IoT based Dashboard. In this paper we developed a optimized model for the management of the battery. This model is developed by controlling the different important factors such as voltage (V), Current (I), state of charge (SOC) and temperature. In the battery management system (BMS) the dependability and safety of the battery is utmost important. Moreover, the improvement in the system can be easily achieved with the help of battery management system visual interface.