Miracle-dz/UniFAR-FTUs huggingface dataset:
An Error Resilient Design Platform for Aggressively Reducing Power, Area and Routing Congestion

In the traditional implementation methodology, a range of target voltage levels as defined in the Unified Power Format (UPF) together with the regular timing constraints are applied during the timing, area and power optimization stages of RTL-to-gate mapping. However, this approach usually requires stronger-driving-strength and bigger-size combinational and sequential standard cell mapping for maintaining the degraded performance caused by lower supply voltage. In this paper, an innovative power-saving design platform, using an analysis flow that effectively integrates the following methodologies, was proposed: (1) path retiming, slack redistribution, and modified razor insertions; (2) customized vector-free approaches and the automation procedures of generating corresponding and randomized stimulus for early-stage static and dynamic voltage-aware power analysis; and (3) precise prediction via Design Dependent Critical Path Monitor (DDCPM) for avoiding the happening of unexpected timing violations caused by the aggressive scaling of supply voltage during the fine-grained DVFS. Accordingly, not only dramatic reductions of power consumption and chip area but the serious routing congestion issues often happened in a design with high occupation of long-depth critical timing paths could also be effectively alleviated. One of our experimental results in TSMC 55nm process node shows the maximum power and area reduction is 62.7% and 29.1%, respectively.
Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron

An Alzheimer's individual cognitively goes through three stages: Cognitive Normal, Mild Cognitive Impairment (MCI), and complete cognitive impairment and finally succumb to death. It is very important to understand all these stages to diagnose the disease at different levels properly. Past studies mainly center on issues of binary classification such as Alzheimer's Disease (AD) vs. Normal Control (NC), MCI vs. AD, NC vs. MCI, or MCI converter (MCIc) vs. MCI non-converter (MCInc). There are less efficient works in Multi-level classification. Multiâlevel diagnosis enables one to use the same algorithm to identify the two classes of diseases, namely MCI and AD and normal, which is impossible with a binary level classification. This helps determine the severity levels of the disease and helps identify the disease at an earlier stage, i.e., at the MCI stage. The training of architecture with AD, MCI, and NC data also improves discriminating non-diseased from diseased ones. The transition from one stage to another is indicated by anatomical changes in the Structural MRI (SMRI). For CNN to learn discriminatory features underlying the disease and identify the brain affected regions that correspond to each level from MRI scans, the algorithm should have the three stages in a single algorithm. So we propose a multi-level classification of AD using cascaded 3D CNN features and Multilayer Perceptron classifier to achieve a ternary classification accuracy of 96.66%, which outperforms state of the art.
Don't Worry be Happy - Using virtual environments to induce emotional states measured by subjective scales and heart rate parameters

Advancing technology and higher availability of Virtual Reality (VR) devices sparked its application in various research fields. For instance, health-related research showed that simulated nature environments in VR could reduce arousal and increase valence levels. This study investigates how the amount of possible interactivity influences the presence in nature environments and consequences on arousal and valence. After inducing fear (high arousal and low valence) through a VR-horror game, it was tested how participants recovered if they played a VR-nature game with either no, limited, or extensive interaction. The horror game proved to be a valid stimulus for inducing high arousal and low valence with a successful manipulation check. Igroup presence questionnaire (IPQ) scores showed that more interaction with the virtual environment increases spatial presence. A beneficial effect of experiencing nature can also be concluded. Results from the Self-Assessment Manikin questionnaire (SAM) scores for valence indicate a significant increase in the conditions with extensive and limited interaction compared to the control group. The VR Nature experience did significantly decrease arousal and increase valence compared to the post-horror game ratings. The physiological responses support this finding. These results can increase the effectiveness of health-related VR-applications to elevate mood levels by either implementing plenty of interactions and consequently increasing spatial presence or doing the opposite and leaving out any interactions at all.
Child Cry Classification - An Analysis of Features and Models

This paper presents a study on the classification of child cries based on various features extracted through speech and auditory processing. Certain spectral and descriptive features vary significantly in a child's cry intended for a specific purpose. Firstly, the model was trained using individual features. Later, the best features were selected and the model was again trained by combining these features. Logistic regression, SVM, KNN and Random Forest models were used for classification. A total of 457 samples were used for training/testing the models from the dataset Donate-a-cry corpus.
Modeling of Hybrid Stepper Motor Finalized to the Optimization of the Holding Torque

The hybrid stepper motor is an electronically commutated brushless synchronous rotating electrical machine that defines incremental motion. Thanks to its remarkable precision and versatility, in recent years it has been the subject of increasing industrial applications. To obtain stepper motors with better performance, software modelling, finite element simulations and iterative optimizations by means of genetic algorithms were performed. The software used to carry out these simulations were Altair Flux 2D, Flux 3D and Hyperstudy. The computational complexity of the model obtained and the hardware tools available have imposed a magneto-static and non-transient analysis. Despite this simplification, it was necessary to proceed with a probabilistic "design of experiments" approach and a specific mathematical model before developing the optimization that otherwise would have been enormously expensive in terms of time and memory space.
A Comparison of Current Determination Strategies for the IPMSM Considering Magnetic Saturation, Core Losses and Resistance Variation

This paper deals with the current determination of Interior Permanent-Magnet Synchronous Motors (IPMSM) for wide-speed torque control operation for commonly chosen optimization criteria. An offline exhaustive algorithm for current determination using Maximum Torque Per Ampere (MTPA), Maximum Torque per Motor Loss (MTPML), and Maximum Torque Per System Loss (MTPSL) is presented. The magnetic saturation, stator resistive voltage drop, and inverter losses, are also included. Their impact on the system performance using each method are fully investigated. Simulink simulations are conducted considering an industrial application. The results from the three different optimisation criteria (MTPA, MTPML, MTPSL) are compared and discussed in terms of overall efficiency, DC power and, AC power, both in motoring and generating mode.
Call Frequency-Based Fault Localization

Spectrum-Based Fault Localization (SBFL), in its basic form, uses only local information about a program element’s (such as a method’s) coverage to predict its faultiness, and rarely is any additional (contextual) information leveraged about the element itself, nor the test cases. As such an additional context, in the presented approach, we rely on the frequency of the investigated method occurring in call stack instances during the course of executing the failing test cases. The basic intuition is that if a method is called in many different contexts during a failing test case, it will be more probable to be accountable for the fault compared to other methods. We empirically evaluated the fault localization capability of the approach compared to five traditional SBFL techniques using the bug benchmark Defects4J. We found that the new algorithms (i) find the location of bugs at higher rank positions more often, (ii) can achieve 38%–52% rank position improvement compared to the baseline algorithms with statistical significance, and (iii) place more items at the top-10 positions of the suspiciousness ranking.
Ferroresonance Analysis of Particular Iron Core Transformers

The paper quantitatively investigates the ferroresonance phenomenon for a specific class of single-phase and small power transformers. These low-voltage devices that usually exhibit an E+I iron-core shape geometry, are to be commonly found in actual electric and electronic installations. Their operation under no- or light load conditions in circuits that also include capacitive elements, favors the ferroresonance occurrence, which is usually initiated by a switching process. Thus, adopting a suitable magnetization description of the laminated transformer core, a time domain analysis is conducted by numerically solving the nonlinear and nonautonomous differential equations system that models the transients. Hence, the transformer current and voltage time variations are predicted and critically examined by modern investigation tools, specific to nonlinear dynamic systems such a ferroresonant circuit – harmonic analyses, 3D phase portrait and Poincaré map representations.
Discrete Flux Weakening Controller Design for Future Aircraft Electric Starter/Generator System Operating in High-Speed Range

This paper aims to develop an analytical design approach of discrete flux weakening (FW) controller that is implemented for high-speed starter/generator systems. The FW control plant has also been derived for small signal analysis while taking into account realistic signal delay effects. Accordingly, a proposed discrete FW controller has been presented. A generalized methodology to select the controller’s gains has been developed in order to guarantee the stability and dynamic performance throughout the whole operating conditions in starter and generator modes. Simulations have been carried out in order to validate the analytical findings and to validate the proposed design.
A Test Method for Large-size TSV Considering Resistive Open Fault and Leakage Fault Coexistence

Large-size TSV has larger parasitic capacitance, which leads to more resolution challenges. Meanwhile, due to the opposite effect of resistive open fault and leakage fault in the ring oscillator, the coexistence of two types of faults will cause serious test confusion. These problems lead to significant difficulty of TSV test. In this paper, a test method using Schmitt trigger as TSV receiver is presented to enhance test resolution for large-size TSVs and reduce test confusion when two kinds of faults exist simultaneously. Schmitt trigger that is characterized by two threshold voltages is used to improve the test resolution, and then reduce the supply voltage to check whether the ring oscillator oscillates or not so as to dealing with fault coexistence. HSPICE simulation results based on 45nm CMOS technology demonstrate that the proposed method offers better test resolution compared with the existing similar methods, and can distinguish the misdiagnosis when there are two kinds of faults existing simultaneously in a TSV.
Preparation and crystal structure of sorafenib sulfate monohydrate

Salt formation is the most commonly method for increasing the solubility of drug. Sorafenib (Sor), a multikinase inhibitor active in the treatment a board of human cancers, has a very low solubility. In this study, sorafenib sulfate monohydrate (Sor-S) has been prepared for improving its solubility. The simulated powder X-ray diffraction (PXRD) was different from experimental PXRD of free Sor. The crystal structure of Sor-S was characterized. The hydrogen bond system, topology, and the superposition of the molecular conformations of sorafenib in base form and protonated cation in salt form were analyzed. The Hirshfeld surfaces plotted with dnorm was conducted for investigating the hydrogen bonds. Shape index and curvedness maps presented two rings with π π interplanar stacking. The tabletability was predicted using an energy framework.
Study on extraction method of geological profile data based on BIM

Based on Revit BIM platform and geotechnical exploration borehole data, a method to obtain arbitrary profile data of geological body is proposed. The extraction of attribute data such as profile and lithology of 3D geological body and arbitrary angle cutting plane is realized in the form of plug-in, and the input function of slope stability analysis parameters is developed on the basis of profile model.
The Research Status and Development of Personal Cooling Systems

Personal cooling systems (PCSs) have been commonly used to relieve the risk of heat strain when exposed to hot environments. This review introduces PCSs on cooling principle, classification and test methods. Then, predicting the development trend of PCSs. According to different cooling medium, PCSs are classified into three types: air cooling systems, liquid cooling systems and phase change cooling systems, introducing the working principle, advantages and disadvantages respectively. In addition, the characteristics of three testing methods of PCSs (i.e., mathematical modeling, manikin testing and human trial) are summarized, and manikin testing includes three operation conditions: constant skin temperature mode, constant heat flux mode and thermoregulatory model control mode. Furthermore, the development trend of PCSs in features, standardize and accurate test as well as evaluation system are predicted.
Universal Non-Volatile Resistive Switching Behavior in 2D Metal Dichalcogenides Featuring Unique Conductive-Point Random Access Memory Effect

Two-dimensional materials have been discovered to exhibit non-volatile resistive switching (NVRS) phenomenon. In our work, we reported the universal NVRS behavior in a dozen metal dichalcogenides, featuring low switching voltage, large on/off ratio, fast switching speed and forming free characteristics. A unique conductive-point random access memory (CPRAM) effect is used to explain the switching mechanisms, supported by experimental results from current-sweep measurements.
A broadband, high even-order suppression distributed power amplifier for sub-6 GHz communication system

A high power level with high even-order suppression over the broadband power amplifier is presented. In this study, the multi-octave bandwidth is achieved with the use of a distributed architecture. Besides, with the presence of the balun, a high power level is achieved with even-order suppression properties. The design is fabricated using a 450 nm Gallium Nitride (GaN) process with a bare-die size of 4.5 mm x 3 mm. The EM simulated results in the 2–6 GHz band show that the PA exhibits a minimum linear gain of 10.8 dB, with the saturated output power of 44.6 dBm (28W). The 2nd harmonic rejection level is at least -33 dBc.
Implementation of HUB and Spoke Topology in VPN Using EIGRP

A Virtual Private Network (VPN) is a network that allows service provider to have point-to-point connections between the customer sites. This paper involves the implementation and configuration of Hub and Spoke Topology in VPN using Enhanced Interior Gateway Routing Protocol (EIGRP) for security and encrypted form of connectivity. EIGRP is the routing protocol that uses Diffusing Update Algorithm (DUAL) such that no boundary decisions are made in between any two routers on its own. Information is transferred using EIGRP in between customer-edge and provider-edge routers with minimal delay compared to other protocols in more secured form. The main application of this type of technology is employed in Banking and connection of separate Autonomous system like in Traffic engineering.
Torque Ripple Suppression for IPMSM using FEA- based Model Predictive Direct Torque Control

The interest in model predictive direct torque control (MP-DTC) for high-performance dynamic control of electric drives has been growing. Unlike the conventional direct torque control (DTC), MP-DTC can achieve optimal voltage selection by predicting the control variables evaluations using a machine model. However, the model-machine mismatch can degrade the control performance significantly, especially for the interior permanent magnet synchronous machine (IPMSM) because of its highly nonlinear characteristics. Therefore, this paper describes an investigation of the MP-DTC algorithm that exploits the advantages of a finite element analysis (FEA) based model in representing the behavior of the IPMSM precisely, including the magnetic saturation and spatial harmonics effects, to suppress the torque pulsations and eliminate the steady-state torque error. Moreover, this approach optimizes the duty ratio simultaneously with the voltage vector selection to guarantee further torque ripple reduction under steady-state operation, especially at low speeds. Simulation results of an 80-kW IPMSM drive are presented to validate the model and the proposed control method.
3D Mapping Based IMU Loosely Coupled Model For Autonomous Robot

In this paper, we focus on building a three-dimensional environment map on large scale scenes for robot navigation. The main contribution of this paper is that we build a loosely-coupled IMU-SLAM fusion model based on EKF and extend it to be a dense mapping system running on embedded devices. To this end, we use the inertial measurement unit to estimate an accurate pose for the direct SLAM system, which improves the efficiency of image matching in direct SLAM and reduces the probability of falling into a local minimum. That is the main reason why we can build dense map robustly and fast on a low-cost platform. We then use the pose of the camera calculated by SLAM to update the IMU bias for the next operating. Finally, the experiments show that our system algorithm can precisely estimate the pose of the camera and robustly build an environment map in large-scale scenes, which can be used in robot navigation.
Optimal Design of Magnetic Hysteresis Couplers

Hysteresis couplers are commonly used within industrial applications requiring the transmission of a constant torque from zero to synchronous speed. Respect to the other coupler types, they feature low torque ripple and simple mechanical structure. This paper presents a comparison between different hysteresis couplers optimized in terms of average torque and permanent magnets volume. Two coupler topologies are analyzed along with two magnetic materials. Special attention is paid to the different behaviour of isotropic and anisotropic materials. The different coupler options are optimally designed with the aid of an automatic procedure making use of a computational effective performance evaluation method. A single static finite element analysis is carried out to calculate the flux density distributions within the device which are used in post processing to evaluate the transmitted torque via a simplified hysteresis model. The comparative analysis of the optimal designs allows drawing general guidelines particularly useful in the preliminary design stage of this class of electromagnetic devices.
IV Tracer For Photovoltaic Panel

A device used for measuring the current-voltage characteristic of the photovoltaic panel has been developed. The circuit is built with the STM32F103C8, Arm based 32-bit microcontroller. The power MOSFET is used as the electronic load. The electronic load is controlled by varying the gate-source voltage between 0 to +3V. The voltage is detected with a voltage divider across the Panel’s output terminal. The current is detected by the high wattage current sensor, 0.1 Ohms 10 Watt. The control firmware sweeps the gate voltage and captures the current-voltage signal with 56 sampling points. The current-voltage curve is plotted on the 128x64 pixels OLED display. The results show the maximum power point (PMAX), open-circuit voltage (Voc), short-circuit current (Isc), and fill factor (FF).
Predicting Congestive Heart Failure Risk Factors in King Abdulaziz Medical City A Machine Learning Approach

Congestive heart failure (CHF) is one of the diseases with a high burden on the healthcare systems. Patients visits and follow-up at the out-patient clinics are associated with high direct and indirect costs and affect the patient treatment outcomes. In this study, we have tried to test and use machine learning models to predict the risk level and class of CHF patients to confidently extend the timing for the next out-patient cardiac clinic visit. The data for 700 patients’ records were statistically analyzed with Waikato Environment Knowledge Analysis version 3.9. 4 (Weka) using eight different machine learning models. Among the eight tested models, the Random Forest and Logistic regression models were found to be the best. Overall performance of the models was promising with these excellent measures (Precision, Recall, F-measure, and ROC) for the Random Forest and Logistic regression models with high accuracy around 0.89. Future work with more balanced datasets and records are needed to test such models which could save the healthcare systems a lot of direct and indirect costs and improve patients’ outcomes.
Convergence of Non-Convex Non-Concave GANs Using Sinkhorn Divergence

Sinkhorn divergence is a symmetric normalization of entropic regularized optimal transport. It is a smooth and continuous metrized weak-convergence with excellent geometric properties. We use it as an alternative for the minimax objective function in formulating generative adversarial networks. The optimization is defined with Sinkhorn divergence as the objective, under the non-convex and non-concave condition. This work focuses on the optimization’s convergence and stability. We propose a first order sequential stochastic gradient descent ascent (SeqSGDA) algorithm. Under some mild approximations, the learning converges to local minimax points. Using the structural similarity index measure (SSIM), we supply a non-asymptotic analysis of the algorithm’s convergence rate. Empirical evidences show a convergence rate, which is inversely proportional to the number of iterations, when tested on tiny colour datasets <monospace>Cats</monospace> and <monospace>CelebA</monospace> on the deep convolutional generative adversarial networks and ResNet neural architectures. The entropy regularization parameter <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> is approximated to the SSIM tolerance <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>. We determine that the iteration complexity to return to an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-stationary point to be <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}\left ({\kappa \, \log (\epsilon ^{-1})}\right)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> is a value that depends on the Sinkhorn divergence’s convexity and the minimax step ratio in the SeqSGDA algorithm.
Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.