Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Machine Learning Algorithms for classification of Alzheimer’s disease

With significant increase in interest in dementia and Alzheimer’s disease, a form of dementia that deteriorates the brain as well as the memory along with other important mental functions, in which brain neuron connections begin to disintegrate and die. Memory loss and confusion being the main symptoms, physicians and scientists are yet to find a concrete cure for the same. However, different strategies and medications have been found to be helpful, especially if early detection is possible. The report shows the results and analysis of detecting Alzheimer’s using machine learning models and compared them. The OASIS dataset has been applied to different machine learning models like SVM, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, and Ensemble Learning. It has been run through these models before and after fine tuning. The best accuracy was found with Support Vector Machines after fine tuning. Another observation is that the inclusion of feature of CDR’ i.e., Clinical Dementia Rating showed a spike in the evaluation metrics with the best accuracy being 97% achieved by Support Vector Model.
Optimization of Multi-Criteria Decision-Making Problems in Searching and Elimination of Vehicle Faults

The article presents the general decision-making process from a problem situation to an optimal solution, gives a mathematical description of the decision being made in the form of an efficiency criterion, the optimal value of which characterizes the decision being made. Various approaches and methods for finding the optimal values of the criteria are proposed. In this regard, the methods of linear and non-linear programming with their main features and scope are considered in order to be able to choose the appropriate method for determining the best value for all performance criteria for further providing this information to the decision maker. The optimization results are provided to the decision maker for analysis and assessment of the situation. The possibility of correcting the obtained values is also considered, considering the preferences of the decision maker. Thus, one of the possible approaches to the choice of mathematical support for the procedure for constructing a decision rule is considered.
Permanent Magnet Synchronous Motor Speed Control Method with Fractional Order Disturbance Observer

In this paper, a permanent magnet synchronous motor (PMSM) fractional order (FO) speed control method with FO disturbance observer is proposed. On the basis of PMSM vector control, a fractional order PI (FOPI) speed controller is designed. The FOPI parameters are tuned through speed open loop gain crossover frequency specification, phase margin specification and ITSE optimization to load torque step. To suppress the load torque disturbance further, a fractional order disturbance observer (FO-DO) is established. Through ITAE optimization, higher indiscrimination degree is achieved than integer order type-II system. Lastly, the effectiveness of the proposed method is verified on a MATLAB based PMSM control simulation platform. Higher tracking precision and better disturbance rejection can be achieved through the FOPI plus FO-DO control method.
An 8-b 8-GS/s Time-Interleaved SAR ADC With Foreground Offset Calibration in 28nm CMOS

This paper presents a high speed and low power TI-SAR ADC with offset calibration. Based on robust non-binary dynamic resistive DAC, comparator offset mismatch in the 2b/cycle TI-SAR ADC is well mitigated by the proposed offset calibration with small power consumption and speed degradation. A custom segmented and split CDAC is developed to reduce critical path delay and input capacitive load. With optimized switching strategy, VCM shift is minimized to ensure ADC accuracy and only two reference sources are needed. An 8-b 8GS/s prototype is designed in 28nm CMOS process, the simulated ADC ENOB and SFDR are 7.7bits and 63.4dB respectively with Nyquist input frequency. The ADC power consumption is 14.3mW under 1-V supply, corresponding to an excellent Walden FoM of 8.5 fJ/conv. -step.
Cloud Data Formats Transformation Algorithms

In cloud infocommunication systems built using open source components, there is a mismatch in various formats or data structures, which is revealed when maintaning such systems. The article assumes such data formats as YAML, JSON and XML and here is put a hypothesis about the possibility of converting various formats to a single format. Relying on the analysis results of the real data samples from cloud components, three possible options of the structure are proposed. The second hypothesis was to verify the possibility of combining data from several sources. After writing code fragments on Python language and taking experiments an algorithm for transforming the original data was proposed. The proposed algorithm is tested on real data samples and its convergence on a large sample met suitable conditions for the maintenance automation purpose.
A Brief Overview on Fuel Cell Electric Vehicles

The growing pollution had reach to an alarming state where the healthy state of environment is at stake since few decades. One of the major reason for this condition is petrol or diesel run vehicles. Not only pollution with increasing number of vehicles the amount of petrol and diesel is also depleting rapidly. Fuel Cell Electric Vehicles (FCEV) are a solution to this problem as they run basically on hydrogen which can be reused again and also it is environment friendly as it releases less smoke. This paper presents a brief overview of FCEV, their working principle, various topologies of FCEV, their energy management schemes and technical issues faced by them. These topologies depends on generation sources and storage elements. This article also discuss various electric motors used in FCEV. An analytical discussion regarding the converters and inverters used in FCEVs at different states had also been presented along with a tabular comparison.
Lidar-Based Cooperative SLAM with Different Parameters

This paper presents a feature-based map merging approach through detecting, describing, and matching geometric features between the maps. The key contribution of this work is to identify the effective method for merging maps that are developed by varying the grid resolution and scan rate parameters. The map data sets are created by Lidar mounted on the QCar mobile robot platform. The comparison of feature detection methods to register map images at different scale is presented. Finally, the effectiveness of the proposed approach is validated based on the map fusion assumptions using real-world data. Also, SLAM (robot motion) is carried out on the merged global map (developed by proposed map fusion method).
Arabic English Speech Emotion Recognition System

The Speech Emotion Recognition (SER) system is an approach to identify individuals' emotions. This is important for human-machine interface applications and for the emerging Metaverse. This work presents a bilingual Arabic-English speech emotion recognition system based on EYASE and RAVDESS datasets. A novel feature set was composed by using spectral and prosodic parameters to obtain high performance at a low computational cost. Different classification models were applied. These machine learning classifiers are Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Ensemble learning. The proposed feature set performance was compared to the "Interspeech 2009" challenge feature set, which is considered a benchmark in the field. Promising results were obtained using the proposed feature sets. SVM resulted in the best emotion recognition rate and execution performance. The best accuracies achieved were 85% on RADVESS, and 64% on EYASE. Ensemble learning detected the valence emotion with 90% on RADVESS, and 87.6% on EYASE.
HTTP web server implementation in air parameters monitoring system

Embedded systems are widely used in different spheres of everyday life. Implementation of web server into these systems enable remote access to processed data. Web server implementation should be suitable for limited resources of these systems. In this paper, web server implementation in system for air parameter monitoring will be presented. This implementation is done using LwIP stack and enables remote access to measurement results within local network. Operation principle of web server and whole system will be discussed.
A 32dBm OOB-IIP3 BW-Extended 5G-NR Receiver with 4th-Order Gain-Boosted N-Path LNA

This paper reports a self-interference-resilient receiver (RX) for 5G-NR-FDD covering 0.5 to 2GHz. It incorporates a $4^{\text{th}}$-order gain-boosted N-path low-noise amplifier (LNA) and a $2^{\text{nd}}$-order baseband (BB) TIA to widen the −3dB RF-BW and also enhance the out-of-band (OOB) roll-off slope. Furthermore, a positive-feedback loop is created for the input-impedance matching purpose thanks to the impedance-translation property of the N-path network. Implemented in 65nm CMOS process, the simulation results show that with >54MHz RF-BW the RX achieves >24dB OOB rejection at 80MHz offset. When the offset frequency $(\Delta f)$ is twice the −3dB RF-BW, the RX achieves 32dBm OOB-IIP3, while consuming a reasonable power of 27 to 67mW. The noise figure (NF) ranges from 3.2 to 5.5dB and active area is $0.38\text{mm}^{2}$.
Solar and Grid Power Integration for Water Pump Application

Water pumping system plays an very important role in irrigation sector However its mostly depend on conventional electricity supply or diesel generator. Dependency on such supply create a burden on non renewable sources. Hence in order to reduce its burden on such energy an effort has been taken in this article. This article consist of solar water pump, which is not only fed by grid but also its supported by non renewable energy i.e. solar photovoltaic. Results in reduction of burden on conventional energy. Proposed system consist of single phase induction motor, DC Boost converter, Inverter and PV array. The system is simulated with MATLAB/SIMULINK software and its results show good response.
Rainfall Forecasting for Silchar City using Stacked- LSTM

Accurate forecasting of rainfall is a very difficult task in meteorology. The complexity, execution time and high computing power required by the Numerical Weather Prediction (NWP) models have always motivated researchers to develop alternate rainfall prediction models. As weather data is a time series by nature this paper proposes a Stacked Long Short-Term Memory (Stacked-LSTM) based Recurrent Neural Network for rainfall prediction of Silchar city in north-east India. To further increase the efficiency of the model Boruta feature selection is used. The proposed model has been compared with LSTM, XGBoost, Random Forest and Multiple Linear Regression. The performance measures used in this study are Root Mean Squared Error (RMSE) and R-Squared values. The proposed model widely outperforms all other models with an RMSE value of 0.98 and R-Squared value of 97.03%.
A 40.6 ppm/°C 368 nW 10 kHz Relaxation Oscillator with Temperature-Sensor-based Piece-Wise Compensation Technique

This paper proposes an on-chip 10kHz relaxation oscillator (RxO) for Internet-of- Things (IoT) application. To reduce the frequency temperature coefficient (TC) of the RxO, a novel temperature-sensor-based piece-wise compensation scheme is introduced, which employs different hybrid resistor network combination for the proposed RxO according to the ambient temperature variation. Furthermore, a capacitance calibration method is employed to compensate the frequency drift caused by the process variation. The circuit is designed and simulated in a 0.18 $\boldsymbol{\mu}\mathbf{m}$ CMOS process. The proposed RxO acquires a frequency temperature coefficient of 40.6 ppm/°C from −40°C to 125 °C with the frequency drift of −0.41% to 0.26%. The frequency variation is 0.459% at the supply from 0.95 V to 2 V, and the typical power consumption is 368 nW under a supply voltage of 1 V.
Identification of the Nature of Threats to the Economic and Functional Security of Intelligent Onboard Systems and the Selection of Protection by Machine Learning

The problem of ensuring economic security in the field of development and production of intelligent on-board systems is considered by choosing the optimal set of operations and measures of an organizational and technological nature, identifying and analyzing the cause-and-effect nature of threats and choosing adequate measures to counter them according to the criterion of optimal expected costs or maximum security with restrictions in the form of given allowable costs for them. Its solution is proposed by machine learning methods and evolutionary optimization algorithms.
Low-Complexity Dynamic Single-Minimum Min-Sum Algorithm and Hardware Implementation for LDPC Codes

As a type of low-complexity decoding algorithm for low-density parity-check (LDPC) codes, the single-minimum min-sum (smMS) algorithm avoids finding the second minimum, while estimates it by adding a fixed value to the minimum instead. However, the inaccurate estimation of the sub-minimum results in obvious performance degradation. In this work, we propose an improved smMS algorithm, which adds a dynamic value to the minimum based on a special variable that can be easily computed and largely represents the convergence degree of iterative decoding. This new algorithm is thus called dynamic smMS (dsmMS) algorithm. In comparison to the standard normalized min-sum (NMS) algorithm, the performance gap for LDPC code (672,588) is narrowed from 0.55 dB of the smMS to 0.12 dB of the dsmMS. We also present a partially parallel decoding architecture for the dsmMS algorithm, and implement it under 55nm CMOS technology with an area of 0.21 mm2, Furthermore, compared with the traditional NMS decoder, the proposed design can reduce the area of the total decoder by 22%.
Research on Collaborative Interaction Strategy for Massive Distributed Renewable Energy in Clusters

In order to meet the needs of the transformation from traditional power grid to smart grid, the research status of distributed collaborative interaction strategy in renewable energy systems at home and abroad is summarized. Firstly, the advantages and disadvantages of traditional centralized control and distributed control are compared and analyzed; The research status of distributed collaborative interaction strategies, including event triggered consistency algorithm, is summarized; Then the typical applications of distributed cooperative interaction strategy in the fields of virtual power plants, economic dispatching and micro grid frequency control are analyzed; Finally, the development trend of distributed collaborative interaction in renewable energy system is prospected, and the problems that need to be solved urgently and the future development direction are pointed out.
Proposal of a Low-Cost System Architecture for Prototyping and Learning SLAM

Most SLAM enabled robotics system require expensive hardware, which makes it difficult f or students to experiment with them. This work proposes a low cost mobile robotic system for implementing and testing SLAM, allowing students to experiment with its various implementations in the real world and better understand each one. We present experimental results that show sensor data acquirement, such as odometry and images, and communication of those data to a central computer, where the SLAM algorithm can be implemented using whatever technique and programming language the student chooses.
Analysis of DDoS Attacks in Software Defined Networking using Machine Learning

The most practical configurable network is Software Defined Networking (SDN). SDN delineates the network's control layer from the data layer, while operationally integrating them. With global visibility into network status, the's logical centralized control improves network management. However, centralized management introduces new security risks. One of the most attractive victims of distributed denial of service (DDoS) attacks is the SDN control platform. Due to machine learning (ML) performance and unavoidable advantages against fingerprint vulnerabilities, this article proposes and considers a ML solution for protecting against DDoS attacks in SDN. ML techniques are evaluated in both, a theoretical format and a practical setup such that SDN controllers are disclosed to DDoS-attacks in order to reach significant deductions about ML-based cybersecurity of future networking protocols.
A Public-key Cryptography Using Integral Quaternions

The RSA public key oryptography is widely applied to internet security technology.Its core principal in mathmatics is to find a finite group with specific properties and conclude the methods of encryption and decryption according to the arithmetic rules between elements.At present,it is usually the congruences classes group of rational integer that is used in the internet security technology.In this paper,based on the analysis of quaternions,ring of integral quaternions and congruences classes group of integral quaternions,we draw the following conclusion:the congruences classes group of integral quaternions just has the speeific properties qualified for the RSA public key cryptography.
Computationally Optimized Approximate Computing for Signal Processing Applications

Computational optimization through approximation approach proves to be an effective way for Digital Signal Processing (DSP) at anemometric scales. Here approximation methodology is discussed to design arithmetic blocks of DSP. Implementation on Xilinx ISE 13.2 on Spartan 6 is done for approximate arithmetic block analysis like adder and multiplier which are capable to do area, power and delay optimization if some error is tolerable. Approximate Computational Complexity (ACC) method based 4:2 compressor is designed for image compression gives area, delay and power reduction as 18%, 7% and 49% respectively. Area and power optimized 8*8 image compression obtained at the cost of 20% increase in error rate which is tolerable with reference to adequate PSNR.
Performance Comparison of hardware RAID5 and RAID5+0 configurations under Linux environment

This paper presents a comparative performance analysis of RAID5 configurations and RAID5+0 configurations compared to the performance of a single disk. The measurements were performed on a Dell PowerEdge R515 server featuring an integrated PERC H7000 hardware controller. Since this server does not have the option of connecting disks except in the RAID configuration, only one disk is set for single drive in RAID0 mode, which essentially represents the performance of single drive. Virtual disk 1 was set in RAID0 mode with two 450 GB disks and it did not change during testing, while virtual disk 2 was changed depending on the type of tested RAID level. The operating system installed on VD1 is Linux Ubuntu 20.04. 3 LTS 64 bit. The test software is Filebench 1.5-alpha 3. The expected and assumed test results were confirmed by the Filebench measurement results.
A Hybrid Method for Signal Probability Estimation with Combinational Circuits

Signal probability is one of the most important factors in evaluating the performance of digital circuits. However, estimating signal probability for large circuits could be difficult due to signal delay and/or complex signal correlations caused by reconvergent fanouts. In this paper, we propose a hybrid approach to estimate signal probability by categorizing the strength of signal correlations in order to propagate signal probabilities through logic gates. Probabilities for signals with strong correlation are calculated directly and accurately, while those with relatively-weak correlations are evaluated using local bit-stream simulations. Simulation results on benchmark circuits show that the proposed method is both efficient and effective with a high level of accuracy.
Primary and secondary paediatric hypertension

High blood pressure (BP) or hypertension is a well known risk factor for developing heart attack, stroke, atrial fibrillation and renal failure. Although in the past hypertension was supposed to develop at middle age, it is now widely recognized that it begins early during childhood. As such, approximately 5–10% of children and adolescents are hypertensive. Unlike that previously reported, it is now widely accepted that primary hypertension is the most diffuse form of high BP encountered even in paediatric age, while secondary hypertension accounts just for a minority of the cases. There are significant differences between that outlined by the European Society of Hypertension (ESH), the European Society of Cardiology (ESC), and the last statement by the American Academy of Pediatrics (AAP) concerning the BP cut-offs to identify young hypertensive individuals. Not only that, but the AAP have also excluded obese children in the new normative data. This is undoubtedly a matter of concern. Conversely, both the AAP and ESH/ESC agree that medical therapy should be reserved just for nonresponders to measures like weight loss/salt intake reduction/increase in aerobic exercise. Secondary hypertension often occurs in aortic coarctation or chronic renal disease patients. The former can develop hypertension despite early effective repair. This is associated with significant morbidity and is arguably the most important adverse outcome in about 30% of these subjects. Also, syndromic patients, for example those with Williams syndrome, may suffer from a generalized aortopathy, which triggers increased arterial stiffness and hypertension. This review summarizes the state-of-the-art situation regarding primary and secondary paediatric hypertension.
Review on High-Throughput Micro-Combinatorial Characterization of Binary and Ternary Layers towards Databases

The novel, single-sample concept combinatorial method, the so-called micro-combinatory technique, has been shown to be suitable for the high-throughput and complex characterization of multicomponent thin films over an entire composition range. This review focuses on recent results regarding the characteristics of different binary and ternary films prepared by direct current (DC) and radiofrequency (RF) sputtering using the micro-combinatorial technique. In addition to the 3 mm diameter TEM grid used for microstructural analysis, by scaling up the substrate size to 10 × 25 mm, this novel approach has allowed for a comprehensive study of the properties of the materials as a function of their composition, which has been determined via transmission electron microscopy (TEM), scanning electron microscopy (SEM), Rutherford backscattering spectrometry (RBS), X-ray diffraction analysis (XRD), atomic force microscopy (AFM), spectroscopic ellipsometry, and nanoindentation studies. Thanks to the micro-combinatory technique, the characterization of multicomponent layers can be studied in greater detail and efficiency than before, which is beneficial for both research and practical applications. In addition to new scientific advances, we will briefly explore the potential for innovation with respect to this new high-throughput concept, including the creation of two- and three-component thin film databases.