Miracle-dz/UniFAR-FTUs huggingface dataset:
AI based Speech Language Therapy using Speech Quality Parameters for Aphasia Person: A Comprehensive Review

Aphasia is a communication disorder that hinders the ability to express and communicate with society. Aphasia individual can face difficulties in sentence formation, opinions and trouble with pronunciation. In this survey paper, various techniques have been reviewed based on previous researches including diagnosis and treatment. Automatic diagnosis can evaluate the severity level of aphasia disorder. The important feature of this disorder is that it will not affect their intelligence. This motivated to come up with a speech assessment system which will evaluate speech based on acoustic features. Further, speech will classify into Aphasia quotient score (0 - 100). This system will analyse aphasic speech and provides feedback to the patients. This will help them to improve their speaking and reading skills.
Machine learning based Pedantic Analysis of Predictive Algorithms in Crop Yield Management

Predictive analytics is a statistical technique used to forecast and investigate the development from past chronological data or to extract the information from data. With the help of rising technologies like predictive analytics in data mining, machine learning combining with Internet of Things [IoT], the major challenges in crop yield can be solved and pave way to earn profit. Machine learning means the process of making the system to learn from the previous experiences that help in prediction. In this paper, an conjectural evaluation on diverse prediction algorithms like support vector machines (SVM), recurrent neural networks (RNN), K nearest neighbour regression (KNN-R), Naive Bayes, BayesNet, support vector regression (SVR) etc., is done and its performance are described on the basis of error rates and accuracy level in crop yield. BayesNet shows the higher accuracy of about 97.53% and RNN has less percentage error rates that dominate other algorithms in harvest prediction.
Self-Amplifying Current-Mode Multiplier Design using a Multi-Memristor Crossbar Cell Structure

Multipliers play a major role in modern compute-intensive applications such as artificial intelligence (AI) and signal processing. However, the logic complexity of conventional multipliers is a significant challenge for energy and performance efficiency. This paper proposes a novel current-mode multiplier without additional carry propagation and amplification circuits. The basic functional block is a one-transistor-multi-memristor (1TxM) cell, corresponding to a partial product term. In this cell, the transistor enables or disables the term by switching it ON or OFF, and the memristor acts as the resistive memory unit that determines the state of the cell: either high (i.e. logic 0) or low (i.e. logic 1). The number of memristors in a single cell is suitably chosen to achieve the required amplification depending on the significance of the cell current paths. This sidesteps the need to have a separate current mirror circuit for each path. The parallel current paths are then analogously accumulated to a common path to define the output, avoiding the complex carry propagation steps in conventional multipliers. Using Cadence Virtuoso analogue design environment, we carried out extensive experiments to confirm the functional and parametric properties of the multiplier. The results shows that the proposed multiplier reduces 64% energy cost when compared with recently proposed transistor-memristor cell based approaches.
Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

This paper introduces an adaptable, human-computer interaction method to control multi-rotor aerial vehicles in unsupervised, multi-subject environments. A region-based convolutional neural network (R-CNN) first detects subjects in a frame and their faces’ regions of interest (RoIs), which are then fed to a facial recognition module to search for the main subject within the frame. The R-CNN model supplies the right-hand RoI of the main subject to a convolutional neural network (CNN) that classifies the right-hand gesture. A motion processing unit (MPU) and four flex sensors are embedded in the left hand’s smart glove to produce discrete and continuous signals. Those signals are generated based on the bending of left-hand fingers and the left hand’s roll angle and then fed to a support vector machine (SVM) to classify the left-hand gesture. Three validation layers have been implemented, including a human-based validation, classification validation, and the system validation. The comprehensive experimental results have validated the proposed method.
Liquid-liquid equilibrium of cyclohexane-n-hexane-methanol mixtures: effect of water content

Experimental liquid-liquid equilibrium data for the ternary system cyclohexane-n-hexane-methanol and for the binary systems n-hexane-methanol and cyclohexane-methanol are presented over a temperature range from 284 to 298{Kappa} at pressure of 0.1 MPa. Attention is given to the effect of the purity of methanol as far as the water content is concerned. The data are correlated by means of excess Gibbs energy models (NRTL and UNIQUAC), and the binary interaction parameters are reported.
Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming

Cloud computing offers an attractive solution for modern computer games. By moving the increasingly demanding graphical calculations (e.g., generation of real-time video streams) to the cloud, consumers can play games using small, cheap devices. While cloud gaming has many advantages and is increasingly deployed, not much work has been done to understand the underlying factors impacting players’ user experience when moving the processing to the cloud. In this paper, we study the impact of the quality of service (QoS) factors most affecting the players’ quality of experience (QoE) and in-game performance. In particular, these relationships are studied from multiple perspectives using complementing analysis methods applied on the data collected via instrumented user tests. During the tests, we manipulated the players’ network conditions and collected low-level QoS metrics and in-game performance, and after each game, the users answered questions capturing their QoE. New insights are provided using different correlation/auto-correlation/cross-correlation statistics, regression models, and a thorough breakdown of the QoS metric most strongly correlated with the users’ QoE. We find that the frame age is the most important QoS metric for predicting in-game performance and QoE, and that spikes in the frame age caused by large frame transfers can have extended negative impact as they can cause processing backlogs. The study emphasizes the need to carefully consider and optimize the parts making up the frame age, including dependencies between the processing steps. By lowering the frame age, more enjoyable gaming experiences can be provided.
Optimal Power Allocation of Cooperative NOMA with Finite Blocklength Codes

Compared with orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) has enormous advantages in enhancing fairness and spectral efficiency for downlink transmission. To further improve the performance of NOMA, cooperative NOMA (C-NOMA) has attracted attention while power allocation of C-NOMA is of great importance to ameliorate the system performance. To guarantee the requirements of ultra-reliable and low-latency communication (URLLC) nowadays, the code length is supposed to be short enough where finite blocklength (FBL) codes are adopted. This paper presents a two-user half-duplex downlink C-NOMA scheme with low latency constraint. To minimize the error probability, power allocation problems are formulated. For this purpose, the properties of power allocation to objective functions are investigated. First, the error probability is analysed in terms of signal-to-noise ratio. Afterwards, the convexity of the power allocation problems under certain conditions are shown and optimal algorithms that solve the problems are proposed. Numerical results demonstrate that the proposed C-NOMA with finite blocklength codes successfully achieve lower error probability with regard to the conventional NOMA.
Performance Evaluation of Supervised Machine Learning Algorithms in Prediction of Heart Disease

Big challenge in health care industry is to record and analyze the massive amount of information about patients. Innovations in technologies made revolution in the healthcare industries. In recent years the data analytics developed as promising tool for problem solving and decision making in healthcare professions. Data analytics process the data automatically to make healthcare system more dynamic and robust. It systematically uses and analyses the data of health care for better treatment with low costs. The chief applications of Machine learning in healthcare are the detection and diagnosis of diseases. The heart is the chief organ of human body. Heart disease increases the mortality rate in the world. Around 90% of heart diseases are preventable. Machine learning plays a remarkable role in the health care industry in prediction of heart disease. In this research paper, the presence of heart disease is predicted by employing Decision Tree, Naïve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and logistic Regression algorithms. The performance of the algorithms was analyzed using parameters such as Accuracy, Precision, AUC and F1-score. From the experimental result, it is found that the Random Forest is more accurate for predicting the heart disease with accuracy of 83.52% compared with other supervised machine learning algorithms. The F1- Score, AUC and precision score of Random forest classifiers are 84.21%, 88.24% and 88.89% respectively.
An Efficiency Comparative Workbench Study of eGaN and Silicon Discrete Transistor based Buck Converters

This paper presents a comparative efficiency experimental study of eGaN and conventional Si MOSFET power transistors within similar power ratings. The aim is to prove that the use of eGaN-FET power devices can be beneficial for 12V/1.2V POL buck converters required for microprocessors, FPGA, and other such power supply- demand architectures. Two evaluation boards (a Silicon MOSFET based one and an eGaN FET based one) are tested from an efficiency point of view on the work-bench; for a higher precision, the output current delivered in load was measured by mean of a current probe, avoiding in this way the adding extra resistance in series with the load which has very small values. Because the temperature highly impacts the efficiency performance and reliability of converters within power supplies, making the environment a critical factor in their selection, a thermal scanning of the power devices' package was taken in order to show how is the heat dissipated and giving a suggestive picture of converter's reliability.
An ab initio Method to Predict Phase Transitions in crystalline CO2

A crystal can exist in different phases and can change from one crystal symmetry to another when varying ranges of temperatures and pressures are applied. Solid Carbon dioxide (CO2) has been widely researched over decades mainly due its structural simplicity and importance in terrestrial chemistry, planetary chemistry, its abundance and its rich polymorphism. Various Density Functional Theory (DFT) methods have been devised and yet there exists challenges in computationally evaluating the interactions that exist in crystals using DFT. Here we consider an ab initio method which involves the calculation of Gibbs free energies and hence predicting the phase transition diagrams of solid CO2 and its phases I,II and III using Density Functional Perturbation theory (DFPT). The DFPT method is able to model accurately the crystal structure, at any given temperature and pressure which is based on the electronic interactions and forces acting on the lattice structures. We predict that for low values of temperatures, as pressure increases CO2 changes its crystalline phase from I to III and for higher values of temperature and pressure we observe a transition from phase III to I. The results obtained are in excellent agreement with the experimental results.
Simultaneous measurement of interfacial area and mass transfer coefficient in three-phase fluidized beds

The technique of simultaneous measurement of the mass transfer coefficient and the interfacial area (Robinson and Wilke, 1974) was used to measure the volumetric liquid-phase mass transfer coefficient , the interfacial area and the true liquid-phase mass transfer coefficient (kL) in three-phase fluidized beds. The eddy-cell model of Lamont and Scott (1970) was found to give a reasonable fit to the kL data, if the proportionality constant is modified. The and data were correlated by empirical equations. Some information on bubble diameter was deduced from the interfacial area and holdup measurements.
The Property of ITO Produced by Optical Thin Film Coating for Solar Cell

We studied the crystal quality and optical properties of ITO produced with optical thin film coatings to replace conventional metal electrodes as electrodes for III-V solar cells. ITO films were grown on glass, GaAs and silicon substrate. ITO is a polycrystalline with predominant (222) crystalline plane. A smooth surface with protruding nano-crystallite was observed. The light transmittance of ITO has a strong relationship with thickness and annealing temperature. The transmittance was obviously enhanced after being annealed at 500 °C. With the thickness increase, the transmittance decreases in total. The band gap of the ITO film was estimated 3.8 eV by extrapolating the linear part of the Tauc plot curves to intercept the energy axis. The refractive index n of ITO film is was measured. The reflectivity is greatly influenced by the thickness of ITO.
Characterization of a Digital AMR Magnetometer for Space Applications

In this article, we present research, development, calibration, and characterization of a novel concept of a digitally compensated, low-noise magnetometer based on anisotropic magnetoresistance sensors that is suitable for space applications. The main idea of the design was to reduce the number of precise analog components while using the digital signal processing power available in a modern microcontroller. Our most recent effort targeted lowering power consumption, enhancement of radiation hardness, and overall improvement of the parameters. The principle of operation is presented in detail, along with a detailed description of the instrumentation used to characterize the real instrument, including its noise, linearity, and temperature stability in the range of −20 °C to +70°C. The results of total ionizing dose (TID) testing at a gamma-ray irradiation facility are discussed at the complete magnetometer and part levels. This is an extended version of an article presented at I2MTC 2020 that contains the results of a second radiation test done with a slightly modified design. The instrument worked well throughout the entire irradiation session (TID of 1.05 kGy over 72 h), and the stability of main parameters was very good (50 pT/Gy offset and 1 ppm/Gy sensitivity stability).
ESTIMATION OF HYDROXYL AND HYDROPEROXYL RADICALS CONCENTRATIONS IN THE URBAN ATMOSPHERE OF SANTIAGO

Photochemical smog episodes are frequent in Santiago of Chile city. We have measured the concentration of the main free radical precursors (formaldehyde, ozone, nitrogen dioxide, peroxyacetyl nitrate and nitrous acid), nitrogen monoxide and the irradiance. Calculations of the rate of OH* production were performed for different periods of the year and at emission (Cerrillos and Estacion Central) and receptor sites (Las Condes). From these data and simple steady state considerations were derived the concentrations of hydroxyl and hydroperoxyl radicals. Daily profiles of both radicals show a noticeable accumulation during early afternoon, the time of most intense oxidants accumulation. During summer time, hydroperoxyl radical concentrations are higher in Las Condes than in Cerrillos. The concentrations calculated for both radicals are higher than those estimated in other cities, suggesting a particularly high photochemical activity in the urban atmosphere of Santiago of Chile
Natto characteristics as affected by steaming time, Bacillus strain, and fermentation time

Natto was made in a laboratory scale from soybean using 2 steaming times, 9 Bacillus natto strains, and 6 fermentation times. Natto characteristics including color, firmness, viscosity, ammonia content, and bacteria population were determined. The highest viscosity value of the final products was the natto inoculated with B. nattoâItobiki strainâ; the 2nd was natto with B. natto NRRL Bâ3383 strain. A higher steaming time reduced fermentation time and ammonia content in the final products. A combination of soaking at the room temperature for 20 h (weight increase ratio: 2.1 to 2.3), steaming at 121Â°C for 40 min, inoculated with âItobiki strainâ or NRRLâ3383 strain, and fermented at 40 to 42Â°C for 18 h was able to produce good natto products in a laboratory scale.
Spatial Partitioning Strategies for Memory-Efficient Ray Tracing of Particles

3D particle data is relevant for a wide range of scientific domains, from molecular dynamics to astrophysics. Simulations in these domains can produce datasets containing millions or billions of particles and rendering needs to be in high quality and interactive to support the scientists in exploring and understanding the structure of their data. One general baseline approach is to represent particles as spheres and employ ray tracing as a rendering technique. However, ray tracing requires the data to be organized in acceleration data structures like bounding volume hierarchies (BVH) to achieve interactive frame rates. Modern GPUs provide hardware acceleration for traversing such data structures but are more limited in memory than CPUs. In this paper, we evaluate different acceleration data structures for sphere-based datasets, including particle kD trees, with respect to their scalability regarding both memory size and speed, and we analyze how these data structures can benefit from hardware acceleration. We show that a bricking of data results in the most effective BVH, both fast to traverse utilizing hardware acceleration and with a reasonably small memory footprint. Additionally, we present a hybrid acceleration data structure that has negligible memory overhead and still ensures reasonable traversal speed. Based on our results, visualization tools and APIs for the ray tracing can provide overall better performance by adapting to the needs of particle-centric application scenarios.
A Frame-Level Constant Bit-Rate Control Using Recursive Bayesian Estimation for Versatile Video Coding

In this paper, we present a frame-level constant bit-rate (CBR) control method using recursive Bayesian estimation (RBE) for Versatile Video Coding (VVC). An <italic>R</italic>-<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> model for rate control (RC) has handled the total texture and non-texture bits at a time and has worked reasonably well in High Efficiency Video Coding (HEVC). Nevertheless, if the rate estimation is inaccurately performed, that is, the <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> values for a current frame cannot be linearly modeled with their respective values in the previous frames, the resulting RC performance is degraded. In our work, we adopt the RBE which alternates <italic>prediction</italic> and <italic>update</italic> steps not only to precisely estimate the rates, but also to allocate target bits based on the changes in the distortions of the previously coded frames, thus considering the rates and distortions simultaneously. Therefore, an elaborate RC can be performed especially at fluctuating frame complexities. Experimental results show that our RC method outperforms the RC of VVC Test Model (VTM-5.0) in terms of normalized root mean square error (NRMSE) with maximum (average) 34.95% (12.35%) improvement, and maintains higher visual quality consistency in terms of standard deviation of PSNR by 33.07% (22.34%) improvement for All Intra (AI), maximum (average) 44.82% (27.29%) and 22.54% (9.50%) for Low Delay (LD), and maximum (average) 47.35% (39.94%) and 30.35% (18.54%) for Random Access (RA), respectively, compared to the default RC method of the original VTM-5.0.
FullReuse: A Novel ReRAM-based CNN Accelerator Reusing Data in Multiple Levels

The processing of Convolutional Neural Network (CNN) involves a large amount of data movements and thus usually causes significant latency and energy consumption. Resistive Random Access Memory (ReRAM) based CNN accelerators with Processing-In-Memory (PIM) architecture are deemed as a promising solution to improve the energy efficiency. However, the weight mapping methods and the corresponding dataflow in state of the art accelerators are not yet well designed to fully explore the possible data reuse in the CNN inference. In this paper, we propose a new ReRAM based PIM architecture named FullReuse in which all types of data reuse are realized with novel simple hardware circuit. The latency and energy consumption in the buffer and interconnect for data movements are minimized. Experiments with the VGG-network on the NeuroSim platform shows that the FullReuse can achieve up to 1.6 times improvement in the processing speed when compare with state of the art accelerators with comparable power efficiency and 14% area overhead.
A Coin-Sized Detector for Room-Temperature X-Ray Photons and Electron Spectroscopy

The system design and characterization of a compact single-SDD (Silicon Drift Detector) module for X-ray and electron spectroscopy in a windowless, room-temperature, vacuum environment is presented. The measured SDD leakage current density at +22° C is only 31 pA/cm2. The module, characterized at room temperature with 55Fe X-rays, demonstrated state-of-the-art performance: an energy resolution of 136 eV FWHM at 5.9 keV has been obtained with 1 µs filter peaking time, and its count-rate capability has been tested up to 4.2 Mcps with a 32 ns filter peaking time while maintaining an energy resolution of less than 200 eV FWHM at 5.9 keV.
Design of Low Complexity High performance LUT based Feed-Forward FFT architecture

Rotator with Feed Forward FFT hardware architecture has been proven for many advance industrial application like bio-medical, Digital signal processing it's more suitable for OFDM aplication,wlan and other application.FFT have vast application but characteristic changes rapidly and then controlled by reducing the area, throughput, complexity of rotator using micro rotator In this method used for synthetization of multi delay path commutator architecture to meet requirement of many DSP applications. Characterization for determination of m-rotator at different angles are cascaded design using rotator. These study help for localization of FFT for different application. The design has the minimal hardware and computational complexity to meet IEEE standards. The coding for m-rotator based FFT architecture has been done using Verilog under XILINX ISE platform. The results are verified and found to be compatible with kintex-7 xc7k70t-3-fbg676.