Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Roughness Estimation for Turning Operation Based on Different Regression Models Using Vibration Signals

On machined parts, major indication of surface quality is surface roughness and also surface quality is one of the most specified customer requirements. In the turning process, the importance of machining parameter choice is enhancing, as it controls the required surface quality. To obtain the better surface quality, the most essential control parameters are tool overhang and tool geometry in turning operations. The goal of this study was to develop an empirical multiple regression models for prediction of surface roughness (Ra) from the input variables in finishing turning of 42CrMo4 steel. The main input parameters of this model are tool overhang and tool geometry such as tool nose radius, approaching angle, and rake angle in negative direction. Regression analysis with linear, quadratic and exponential data transformation is applied so as to find the best suitable model. The best results according to comparison of models considering determination coefficient (R 2 ) are achieved with quadratic regression model. In addition, tool nose radius was determined as the most effective parameter on turning by variance analysis (ANOVA). Cutting experiments and statistical analysis demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in all three models construction and verification .
Study of Factors Affecting the Performance of CIGS Based Thin Film Solar Cells

This research aims to improve the CIGS (copper, indium-gallium, diselenide) thin-film photovoltaic cell using Silvaco-Atlas device simulator. Four important performance parameters extracted from the simulated data were its power conversion efficiency, open circuit voltage, fill factor and short circuit current density. In order to compare performance and validate the results with respect to the parameters acquired from the simulation, a CIGS-based solar cell that was experimentally constructed was utilised as a reference. The simulated cell was further optimised by tweaking the absorber material thickness, band gap and concentration of doping. The optimised CIGS cell achieved improved power conversion efficiency from 22.9% to 25%, fill factor from 81.47% to 83.65%, open circuit voltage from 702 mV to 840 mV and short circuit current density from 37.4 to 33.3 mA/ cm 2• The drop in current density is due to the rise in band gap from 1.1 to 1.2 eV or the change in ratio of Ga/(Ga+In) from 0.31 to 0.45. The overall performance boost in PCE, FF and Voc outweigh the drop seen in Jsc, The enhanced results of our optimised CIGS cell can be used as guideline for developing highly efficient device and the performance of the device can further be enhanced by using a hole and electron transport layers, by replacing the window layer, buffer layer, back contact layer and anti reflective coating with better performing materials or by using graded CIGS absorber layer with different band gaps.
False discovery proportion estimation by permutations: confidence for significance analysis of microarrays

Significance analysis of microarrays (SAM) is a highly popular permutation‐based multiple‐testing method that estimates the false discovery proportion (FDP): the fraction of false positive results among all rejected hypotheses. Perhaps surprisingly, until now this method had no known properties. This paper extends SAM by providing 1−α upper confidence bounds for the FDP, so that exact confidence statements can be made. As a special case, an estimate of the FDP is obtained that underestimates the FDP with probability at most 0.5. Moreover, using a closed testing procedure, this paper decreases the upper bounds and estimates in such a way that the confidence level is maintained. We base our methods on a general result on exact testing with random permutations.
Principal Component Analysis in Image Classification: A review

Principal component analysis (PCA) is considered as an important technique for dimension reduction of the data in various artificial intelligence/machine learning applications. One of the most important application is computer vision or image classification. Owing to the benefits and importance of PCA in image classification it is used not only for reducing dimensions, but also used to find important/dominant features hidden inside the data set having high dimensions. That makes PCA as one of the best techniques that helps in image classification yielding highly accurate results. This paper reviews some of the recent studies of application using PCA in image classification. The article covers different datasets having different properties and information of images. Moreover, the paper contributed in listing details of evaluation matrices, datasets, objectives, and possible improvements to increase the accuracy with reduced computational time of included articles.
Offline and Time-variant EVD-based Closed-loop Digital Predistortion Design for Sub-THz Power Amplifier Array in Basedband Transmitter

In this paper, we propose an eigenvalue decomposition (EVD) based digital predistortion (DPD) design for nonlinear memory PAs in active antenna array structure with offline and time-variant scenario. The simulation environment incorporates various non-ideal effects based on IEEE Std 802.15. 3d, the transmitter error vector magnitude (TX EVM) performance can be improved from -9.07dB to -20.64 dB in the presence of the proposed DPD. Furthermore, since the characteristics of the sub-THz power amplifiers are sensitive to temperature changes, we simulate the impact when the temperature rises from 27 °C to 125°C. This leads to inaccurate offline estimation, while with the proposed online tracking mechanism, the TX EVM can be improved from -13.34 dB to -20.05 dB. For the hardware implementation, we use 16-nm FinFET CMOS process with eight times parallelism architecture to achieve a data transmission with a bandwidth of 7.04GHz. The core area and power are 0.106 mm2 and 464.8 mW for the TX DPD, and 0.0576 mm2 and 252.0 mW for the receiver optimizer, respectively.
282-to-607 TOPS/W, 7T-SRAM Based CiM with Reconfigurable Column SAR ADC for Neural Network Processing

Compute in memory ($C$iM) is a promising solution for solving the bottleneck of frequent data interface between memory and processor in Von-Neumann architecture. In this work, a hybrid current/charge domain 7T-SRAM based CiM architecture is proposed to mitigate the PVT-induced RBL variation during computation and thus offer a better linearity without significant impact on the operating frequency and area efficiency. Additionally, a column-referenced 1b to 5b reconfigurable SAR ADC is proposed to support multi-bit output. The proposed design is verified by the Monte-Carlo simulations using 40nm CMOS technology. The 5b mode ADC transferred MAC curve's DNL (LSB) ranges from −0.025 to 0.02 and INL (LSB) ranges from −0.13 to 0.25. The largest RBL variation $(\sigma)$ from MAC value −64 to MAC value +64 is 2.08 mV, resulting in a MNIST classification accuracy of 97.5%, which is only 0.1% degradation and Google Speech Command classification accuracy of 80.5%, which is only 0.5% degradation compared to the software baseline, respectively. The whole architecture offers energy efficiency of 282-to-607 TOPS/W for 1-5b output in the MAC operation, which is competitive when compared to other state-of-art $C$iM architectures.
Cryo-CMOS Mixed-Signal Circuits for Scalable Quantum Computing: Challenges and Future Steps

A systematic research on the development of cryogenic complementary metal-oxide semiconductor (cryo-CMOS) circuits, for implementing the required control electronics to manipulate the quantum bit (qubit) state, is performed over the last few years. Scalability constitutes a key term regarding the evolution of quantum computing from theory to practical application and CMOS technology has been proven to be a promising candidate for implementing the coveted scalable next-generation quantum computers (QCs). Mixed-signal blocks, used for uniting the analog and digital domains, play a key role in the efficient functionality of the qubit control/readout system, thus there is an ever-increasing interest in their high-performance circuit realization. The critical challenge in this venture is to achieve efficient cryogenic operation at low temperatures, i.e., close to the qubit around 4 K, simultaneously keeping power requirements at low values. An overview and comparison of the cryo-CMOS Digital-to-Analog converter (DAC) and Analog-to-Digital converter (ADC) circuit implementations for quantum computing applications that heretofore have been proposed in the literature is presented in this work. A discussion on the challenges and future strategic steps that are henceforth required to proceed toward the development of a functional scalable quantum computer is also conducted.
Long-term aspects of interdisciplinary learning: The case of electronic circuits

An innovative teaching approach that combines analog electronics and digital electronics was recently implemented in the introductory course "Electronic Circuits" offered at the Technion – Israel Institute of Technology. This interdisciplinary approach replaced the disciplinary way, prevalent in higher education, that addresses the two branches of electronics separately. The present study characterized long-term aspects related to interdisciplinary learning of electronic circuits. Semi-structured interviews were conducted with nine students who took advanced courses in electronic circuits (after completing the basic interdisciplinary course), four instructors of these courses and nine industry representatives. The participants were mainly asked about their positions on interdisciplinary learning of electronic circuits and its relevance. According to the findings, both students and instructors argue that interdisciplinary learning of electronic circuits promotes transfer of knowledge and skills. Moreover, the industry representatives claim that interdisciplinary learning is essential in engineering programs.
Nonequilibrium quantum electrodynamics: Entropy production during equilibration

We study nonequilibrium processes of Quantum Electrodynamics (QED) with relativistic charged Bose fields. The aim is to describe thermal equilibration, based on the Klein–Gordon equation for background coherent fields and the Kadanoff–Baym (KB) equation including the leading-order (LO) self-energy of the coupling expansion (the Hartree–Fock approximation). We introduce a gauge invariant relativistic kinetic entropy current at the first-order in the gradient expansion of the KB equation and we show the proof of the H-theorem in d + 1 dimensions (d = 1, 2, 3) in the presence of nonzero background coherent fields. Finally, we present numerical simulation in 1 + 1 dimensions and aim to investigate whether decoherence of the system occurs or not. We find that equilibrium states are realized with remaining background coherent charged Bose and photon fields by preparing distributions of incoherent charged particles asymmetrically in frequency mode as initial conditions in the KB equation even if LO self-energy is present.
Selection of environmental criteria for multi-criteria evaluation of energy supply alternatives in Non-Interconnected Zones of Colombia

Access to energy resources is deeply related to community development. The availability of electrical energy allows the development of social, economic, health, and educational activities, and access to reliable energy resources can support energy supply. In this sense, the need to invest in projects focused on using alternative sources of energy is a major issue. Nevertheless, these types of projects require the inclusion of economic, administrative, social, cultural, technical, and environmental criteria in their selection to be successfully executed, and ensure a solution with a sustainable nature, aligned with any context of implementation. The objective of this research is to set the environmental criteria for a multicriteria selection methodology for Colombia's energy supply systems. According to this purpose, and for the Colombian context, the concept of environmental criteria and the environmental sub-criterions that contribute to the correct development of an energy supply system were identified. This definition was constructed from a review of the known environmental impacts of five technologies: solar photovoltaic, wind, biomass, hydraulic and conventional (diesel). The sub-criteria were classified into the categories of intervened area, understood as the space occupied by the project and its impact on other activities; climate change, focusing on susceptibility and effects; the impact of biodiversity and its attributes concerning specific species and the generation of greenhouse gases (GHG) and other gases. At the same time, the methodological analysis was carried out to define the representativeness of each sub-criterion against the environmental criterion, being the sub-criterion area intervened, as the most representative within the conditions exposed by our context. This work represents an approach in Colombia for a multicriteria tool used for energy supply systems. The considerations adopted here could be subject to changes depending on the criteria that are integrated into the evaluation of an energy project.
Small-Signal Circuit Model for Synchronous Buck DC/DC Converter featuring ZVS at Low-Side

In this paper we provide an improved small-signal equivalent circuit model of a synchronous Buck converter which operates in Continuous Conduction Mode (CCM) and includes an alternative Zero Voltage Switching (ZVS) mechanism for the low-side power MOSFET that rely on the MOSFETs output capacitance. The addressed analysis improves the state of the art in DC/DC small-signal modeling as it is capable to predict unexpected effects on the dynamical system response such as the dependency on input voltage introduced by parasitics. Therefore, a complete design tool which permits to evaluate the impact of the MOSFETs output capacitance and the ZVS network on the converter dynamics is proposed. The derived equivalent circuit model which includes an additional feedforward path and a feedback loop is analyzed and the main open-loop transfer functions (control-to-output, line-to-output, output impedance) are analytically assessed. A verification has been carried out through SIMPLIS circuital simulations, corroborating the validity of the whole evaluation process.
Mental Stress Assessment Using Low in Cost Single Channel EEG System

Mental stress can cause physical diseases and behavioral, emotional, and monetary complications on an individual level. Thus, research is set on finding ways to detect mental stress to reduce the risk of damage to an individual's well-being. Assessment and stress detection can be performed using many physiological signals, including Electroencephalogram (EEG). This paper presents the study of a non-invasive system that measures mental stress using EEG signals that are acquired from one channel. The process involves EEG data acquisition, feature extraction, and stress level classification through machine learning. Moreover, this system was tested on human subjects. In addition, a questionnaire developed by psychologists that covered a wide range of symptoms associated with stress was utilized. The questionnaire is referred to as the Perceived Stress Scale (PSS). Thus, the EEG signals of twenty participants were recorded through single-channel EEG for five minutes. Feature extraction based on the frequency bands is used to train the machine learning algorithm to classify two stress levels: stressed and not stressed. Lastly, the increased beta activity of the subjects with high stress was observed with subjects without stress. This could be a critical factor in the classification of stress with single-channel EEG.
Adversarial Defense using Memristors and Input Preprocessing *

This paper shows that input preprocessing using compression and subsequent rescale and rearrange operations implemented using Memristor Crossbar Arrays (MCAs) provides a robust defense against adversarial attacks. The rescale and rearrange operations are implemented using a fully connected layer followed by three convolution layers. The experimental results show up to a 5.18% improvement in adversarial robustness compared to similar input preprocessing techniques on MCAs.
Kinematics and Microphysics of Convection in the Outer Rainband of Typhoon Nida (2016) Revealed by Polarimetric Radar

This study analyzes the microphysics of convective cells in an outer rainband of Typhoon Nida (2016) using data collected by a newly upgraded operational polarimetric radar in China. The life cycle of these convective cells is divided into three stages: developing, mature, and decaying according to the intensity of the corresponding updraft. Composite analysis shows that deep columns of ZDR and KDP collocate well with the enhanced updraft as the cells develop to their mature stage. A layered microphysical structure is observed in the ice region with riming near the −5°C level within the updraft, aggregation around the −15°C level, and deposition anywhere above the 0°C level. These ice-phase microphysical processes are important pathways of particle growth in the outer rainbands. In particular, riming contributes significantly to surface heavy rainfall. These contrast to previously documented inner rainbands, where warm-rain processes are the predominant pathway of particle growth.
RIS Energy Efficiency Optimization with Practical Power Models

Reconfigurable intelligent surface (RIS) has been envisioned as a promising research direction for future wireless communications. As an important indicator of RIS-assisted communication systems in green wireless communications, energy efficiency (EE) receives intensive research interest as an optimization target. Recently, an increasing number of works have been devoted to EE maximization in RIS-assisted systems, in which the power consumptions of the base station and RIS are jointly optimized. However, most existing works ignored the different power consumption between the ON and OFF states of the RIS elements, which mismatches the characteristics of the realworld physical components. To construct a more correct power dissipation model for EE optimization in RIS-aided systems, in this paper, we establish a novel ON-OFF aware power dissipation model for the RIS elements. Based on this new model, the EE optimization problem is re-formulated, and corresponding lowcomplexity algorithms are proposed by leveraging the mathematical structures of the problem. Simulation results verify the effectiveness of the proposed algorithms across a wide range of scenarios.
Power Allocation in Device-to-Device Communications based-Stackelberg Game Theory

In underlaid Device-to-Device (D2D) cellular networks, severe radio interference can be typical for cellular and D2D users, which causes each D2D user’s quality-of-experience (QoE) to degrade significantly. To remedy these problems, a dynamic Stackelberg game is proposed in this paper, where a single-leader (base station) and multiple-followers (cellular and D2D pairs) users are considered. Based on selection criteria jointly required by the base station, the cellular users and D2D users, a 2-stage Stackelberg game is investigated in this paper. In fact, the amount of power is first performed following a Nash Equilibrium at the first stage (base station). The derived amount power is then distributed among the other players (cellular and D2D users) relative to the proposed game, while respecting their heterogeneous criteria. Finally, power allocation and adjustment algorithms relative to the mentioned dynamic Stackelberg game will be investigated and compared with classical ones.
Classification of Mental Stress using Dry EEG Electrodes and Machine Learning

Stress is a major health issue that affects people worldwide and results in several diseases and negative psychological consequences. Therefore, early detection of stress has become crucial for maintaining a healthy society. In this study, five different machine learning classifiers were studied for their accuracy in assessing mental stress among university students. Mental stress was obtained from EEG signals using a dry Electroencephalography (EEG) electrode. To induce stress and calm mental states, a Stroop Color Word Task (SCWT) was utilized. EEG data were analyzed by extracting the mean power of 4 frequency bands using the Fast Fourier Transform (FFT). The different machine learning classifiers: K-Nearest Neighbors (KNN), Discriminant Analysis (DA), Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM), were then compared for accuracy, sensitivity, and specificity. The behavioral result showed that the accuracy of detecting the SCWT under stress was reduced by 50%. The SVM outperformed other classifiers, achieving the highest classification performance in subject dependent with 99.98 ± 0.09, 99.96 ± 0.122, 99.85 ± 0.30, and 99.27 ± 0.57 accuracies in alpha, beta, theta, and delta band, respectively. In conclusion, alpha and beta bands showed a slightly higher accuracy than other frequency bands. Meanwhile, SVM outperformed other classifiers, achieving the highest classification accuracy of 99.98 % with the mean power of the alpha band.
Numerical investigation on flow mechanisms of a squirrel cage fan

Squirrel cage fan has found wide applications in heating, ventilation and air-conditioning as well as other cooling systems. Although many studies have been conducted to investigate the performance of the squirrel cage fan, the complex flows and the associated flow mechanisms have not been fully elucidated. In this paper, computational fluid dynamics simulations are performed for a comprehensive analysis of the flow throughout a whole squirrel cage fan. A novel representation technique is proposed for a vivid and quantitative description of the flow velocity distributions at the impeller inlet and outlet to aid a heuristic analysis of the complex flows in squirrel cage fan. The pressure gradient distributions, together with the pressure itself, along the volute wall are also extracted and analyzed to explore the local flow conditions of the volute profile with four segmental arcs. The results show that the proposed representation technique is capable of effectively measuring the circumferentially nonuniform flow conditions at both the impeller inlet and outlet. The strong interaction between the impeller and the volute tongue induces reversed flows at both the impeller inlet and outlet, which further aggravates the circumferential nonuniformity of inactive zone and the upstream impeller flow field. Despite the first-order continuity of the volute profile itself, there are strong variations of tangential pressure gradients and sudden diffusion-expansion effects at the junctions of adjacent arcs. These findings benefit a deep understanding of the complex flow mechanisms within the squirrel cage fan.
A finite strain solution for the elastoplastic ground response curve in tunnelling: rocks with non‐linear failure envelopes

This paper generalizes the finite strain Coulomb solution of Vrakas and Anagnostou (Int J Numer Anal Meth Geomech 2014; 38(11): 1131–1148) for the classic tunnel mechanics problem of the ground response curve to elastoplastic grounds satisfying a non‐linear Mohr's failure criterion. A linear (Coulomb‐type) plastic potential function is used, leading to a non‐associated flow law, and edge plastic flow is considered in the plastic zone. The solution for a general non‐linear Mohr's failure criterion is semi‐analytical in that it requires the evaluation of definite integrals. In the special case of the Hoek–Brown criterion, however, these integrals are calculated analytically, resulting in a rigorous closed‐form series solution. The applicability of the derived solution is illustrated through the example of the Yacambú‐Quibor tunnel, where very large deformations were observed when crossing of weak graphitic phyllites. Copyright © 2016 John Wiley & Sons, Ltd.