Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient’s body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI < 5) and severe OSA patients (AHI > 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs.
Freshness Assessment of Milkfish (Chanos Chanos) Using LabVIEW: Image Processing

In a fast-changing world, tradition must adapt with technology. In many industries today, quality control is a method that should be consistent and reliable. One of the industries that are highly in need of this is the global seafood export industry. As humans are subjective, there is a requisite for a technology that would implement certain standards in the industry. This research paper focuses on milkfish (chanos chanos) and the factors affecting its freshness. First are the eyes. They must be clear at the sclera. Red eyes are not necessarily the standard in fish freshness as the fish may be injured during its travel. Second are the gills. The gills must not be brown in color. Third are the scales. Freshly caught fish gives off a metallic shine to its scale as opposed to days-old fish that looks matte. Through the use of Laboratory Virtual Instrument Engineering Workbench (LabVIEW), specifically the IMAQ functions, an image of a fish may be processed and analyzed. Standard set in the program will determine if a fish is fresh. This will then result to a more accurate and stable quality control assessment.
Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.
Classification of Plant Seedling Images Using Deep Learning

The agricultural sector has recognized that for crop management to thrive, acquiring relevant information on plants is needed. However, studies have shown that agricultural problems remain difficult in many parts of the world due to the lack of the necessary infrastructures. Using a public dataset of 4, 234 plant images from Aarhus University Signal Processing group in collaboration with University of Southern Denmark, that consist of descriptions under a controlled condition concerning camera radiance and stabilization. This paper uses a convolutional neural network for training and does data augmentation to identify 12 plant species using a variety of image transforms: resize, rotate, flip, scaling and histogram equalization. The trained model achieved an accuracy categorization of 99.74% during validation and 99.69% during testing, with specificities and sensitivities of 99%. In future works, we plan to utilize the model by training it to other types of plants like herbal-medicinal plants and other crops in other countries. Moreover, the proposed method can be integrated into a mobile application with the goal to provide farmers efficient farming practices.
Numerical Investigations of Micro-Scale Diffusion Combustion: A Brief Review

With the increasing global concerns about the impacts of byproducts from the combustion of fossil fuels, researchers have made significant progress in seeking alternative fuels that have cleaner combustion characteristics. Such fuels are most suitable for addressing the increasing demands on combustion-based micro power generation systems due to their prominently higher energy density as compared to other energy resources such as batteries. This cultivates a great opportunity to develop portable power devices, which can be utilized in unmanned aerial vehicles (UAVs), micro satellite thrusters or micro chemical reactors and sensors. However, combustion at small scales—whether premixed or non-premixed (diffusion)—has its own challenges as the interplay of various physical phenomena needs to be understood comprehensively. This paper reviews the scientific progress that researchers have made over the past couple of decades for the numerical investigations of diffusion flames at micro scales. Specifically, the objective of this review is to provide insights on different numerical approaches in analyzing diffusion combustion at micro scales, where the importance of operating conditions, critical parameters and the conjugate heat transfer/heat re-circulation have been extensively analyzed. Comparing simulation results with experimental data, numerical approaches have been shown to perform differently in different conditions and careful consideration should be given to the selection of the numerical models depending on the specifics of the cases that are being modeled. Varying different parameters such as fuel type and mixture, inlet velocity, wall conductivity, and so forth, researchers have shown that at micro scales, diffusion combustion characteristics and flame dynamics are critically sensitive to the operating conditions, that is, it is possible to alter the flammability limits, control the flame stability/instability or change other flame characteristics such as flame shape and height, flame temperature, and so forth.
Convolutional Neural Network Accelerator with Reconfigurable Dataflow

Convolutional-Neural-Network (CNN) is used in broad applications. There are dataflows for convolutional layers in CNN such as row-stationary and weight-stationary. However, these dataflows have strengths and weaknesses. This paper analyzed two representative dataflows and introduce the dataflow-reconfigurable CNN accelerator that takes advantage of both dataflows.
Application of Fuzzy Logic on Astronomical Images Focus Measure

Focus accuracy is an essential factor that affects the astronomical observations quality. The accurate measure of celestial objects properties depends on focus. Automatic focusing is necessary for astronomical observations imaging systems. This paper presents a modified focus measure operator. It also proposes the use of fuzzy logic, to transform images, because of its tolerant of imprecise and incomplete data. The focus operators are applied into two sequences of star clusters observations. The experimental results show that the suggested measure overall score exceeds the previous operators.
A Modified Unequal Wilkinson Power Divider Using T-Shaped Transformers

Conventional microstrip unequal Wilkinson power divider is difficult to be realized when the power ratio is not small because high impedance transmission line is usually used. Although the previous unequal divider in 2012 can relax the high impedance line usage, the structure results in low impedance line issue. Therefore, this study proposed a modified unequal Wilkinson power divider using T-shaped transformers can avoid high and low impedance lines simultaneously.
Point Cloud Registration Refinement in an Urban Environment using 2D Edge-Maps

As 3D point cloud acquisition sensors become increasingly prevalent in urban environments (e.g., LiDAR sensors for autonomous vehicles), the need arises to find efficient ways to align large amounts of such 3D data, often in real-time. In this work, we propose a novel method for 3D point cloud registration refinement in an urban environment (e.g., between Terrestrial LiDAR Scans - TLS - and Airborne LiDAR Scans - ALS), assuming an initial coarse registration is available. The proposed method is based on estimation of the direction of gravity, wall detection, projection of the point clouds on a perpendicular horizontal plane, and conversion into 2D edge-maps. Then, two methods are considered for alignment between the 2D edge-maps: a 2D variant of the well-known ICP (Iterative Closest Point) algorithm, and Edge-Map Phase-Correlation (EMPC). We demonstrate the usefulness of the proposed methods for registration in this challenging task, where the 2D variant of ICP achieves a meaningful advantage over 3D ICP in terms of runtime, while maintaining comparable registration accuracy.
A Two-Level Hybrid Model for Anomalous Activity Detection in IoT Networks

In this paper we propose a two-level hybrid anomalous activity detection model for intrusion detection in IoT networks. The level-1 model uses flow-based anomaly detection, which is capable of classifying the network traffic as normal or anomalous. The flow-based features are extracted from the CICIDS2017 and UNSW-15 datasets. If an anomaly activity is detected then the flow is forwarded to the level-2 model to find the category of the anomaly by deeply examining the contents of the packet. The level-2 model uses Recursive Feature Elimination (RFE) to select significant features and Synthetic Minority Over-Sampling Technique (SMOTE) for oversampling and Edited Nearest Neighbors (ENN) for cleaning the CICIDS2017 and UNSW-15 datasets. Our proposed model precision, recall and F score for level-1 were measured 100% for the CICIDS2017 dataset and 99% for the UNSW-15 dataset, while the level-2 model precision, recall, and F score were measured at 100 % for the CICIDS2017 dataset and 97 % for the UNSW-15 dataset. The predictor we introduce in this paper provides a solid framework for the development of malicious activity detection in IoT networks.
Single Sample Per Person Face Recognition Based on Sparse Representation with Extended Generic Set

The single sample per person(SSPP) face recognition is one of the most essential problems of face recognition. Moreover, the testing samples are typically corrupted by nuisance variables such as expression, illumination and glasses. To address this problem, plenty of methods have been proposed to surmount the adverse effect of variances to testing samples in complex surroundings, but they are not robust. Therefor we proposed the Single sample per person face recognition based on sparse representation with extended generic set (SRGES). First, a set of general sample set were introduced, and the variation information of generic face samples set was extracted and extended to the single training sample set. Then, reconstruction error of testing sample is generated on training samples set by sparse representation model. Finally, the recognition is achieved depending on this sparse reconstruction error. The experimental results on the AR database, Extended Yale B database, CAS-PEAL database and LFW database displayed that the proposed algorithm is robust to variation feature for SSPP face recognition, and outperforms the state-of-art methods.
Integration of Retention-aware Refresh and BISR Techniques for DRAM Refresh Power Reduction

Due to few cells in memory that have shorter retention time, DRAM controller have to raise the refresh frequency to keep data integrity, and hence produce unnecessary refresh for the other normal cells, which result in large refresh overhead and the delay of memory access. In this paper, we propose an integration scheme to integrate retention-aware refresh and BISR techniques. Based on the RAAR method, our strategy can choose the most appropriate way of weak cell fixing to minimize the waste of non-weak row refresh. A dynamic programming algorithm with a state transition equation is proposed to resolve this problem. Experimental results show that with this BISR integration scheme, we can further reduce refresh power than without applying it.
Data Center Heat Distribution Modeling Using Onboard Sensors

Uneven cooling in data centers can be detrimental to server and cooling unit performance. To maintain a desired cooling environment, an appropriate thermal management strategy is necessary such as creating effective visual thermal maps to characterize cold air and hot air distribution. Numerically analyzing heat distribution models along with prescribed environmental sensor measurements as boundary conditions generates thermal maps. Enabling reliable and real-time heat distribution monitoring suggests the need for computationally efficient and accurate models and optimized sensor placements.This study presents a customized or Custom heat distribution modeling which considers internal server heat, and applies onboard server sensor measurements. With the Navier-Stokes model using external and onboard sensor measurements as the baseline, the Laplacian-convection-diffusion (LCD) and the Custom models as well as the onboard versions of all models provide comparable mean absolute errors or MAEs. The Custom model produces competitive results especially in using both external and onboard sensors where cooling vents are located. The Custom model converges faster than Navier-Stokes but slower than LCD. In essence, the Custom model using onboard sensors reasonably provides the most computationally accurate, efficient and attainable solution suitable for real-time monitoring and effective in reducing electrical costs and in avoiding any downtime in the data center.
Comparison Study of Big Data Processing Systems for IoT Cloud Environment

There are a number of popular choices for using relational data processing systems and there are many complex factors that decide what gets deployed for specific data processing tasks such as integration of the data processing system with an overall solutions stack, open-source vs. closed-source and manageability and so on. One such factor is the performance of the system. In this paper, we examined this factor of performance of NoSQL (MongoDB) and SQL system using the YCSB benchmark. The results showed that NoSQL systems continue to provide a good performance advantage over their SQL counterparts, but the NoSQL alternatives are competitive in some cases.
Performance and Reliability of Asymmetrical Underlapped FinFET based 6T and 8T SRAMs in Sub-10nm Domain

In this paper, the performance and reliability of optimized 6T and 8T SRAM circuits using high ION/IOFF ratio Asymmetrical Underlapped FinFETs are determined at a reduced supply voltage of 500mV. Performance of both SRAM designs are evaluated during read and write operations. 6T SRAM achieves 44.97% improvement in the read energy compared to 8T SRAM. However, 6T SRAM write energy degraded by 3.16% compared to 8T SRAM. Read stability and write ability of SRAM cells are determined using Static Noise Margin and N-curve methods. Moreover, Monte Carlo simulations are performed on the SRAM cells to evaluate process variations. Simulations were done in HSPICE using 7nm Asymmetrical Underlap FinFET technology.
Cloud and Edge Processing by Precoder Design for Secrecy Rate Maximization

In this paper, we investigate the sum secrecy rate of cloud and edge processing in multi-cell downlink system by adopting the artificial noise precoding (ANP). To elaborate, in cloud processing, we consider the outdated channel state information (CSI) inevitably induced by fronthaul link which connects the cloud and radio units in return for centralized processing. In contrast, the radio unit (RU) in edge processing only manages its cell using timely but local CSI. Furthermore, under per-antenna power constraint, we obtain a sub-optimal precoder to maximize the sum-secrecy rate for both processing. With simulation results, we compare the secrecy rate of the cloud and edge processing, and show that the sub-optimal precoder performs well.
Data-Driven Approach for Fault Prognosis of SiC MOSFETs

This article proposes an unsupervised learning approach for fault prognosis of silicon carbide (SiC) mosfets. The proposed approach utilizes the changing trend of a device's voltage, current, temperature, and other device characteristics with its degradation. The failure modes of semiconductors are reviewed along with existing methods for fault prognosis. The proposed approach is the first to address prognostics of SiC devices, and it can avoid the effects from system noise and data errors. It is not limited to offline analysis and is targeted at online implementation. It is easy to implement on standard digital platforms, and has fast computational speed. Offline data analysis is performed to verify the effectiveness of the proposed method, and a processor-in-the-loop system is used to verify its ability to perform online fault prognosis.
Fabrication of In-situ Localized Surface Plasmon Enhancement of Au Nano-particles on Multiple Quantum Wells Nanorods

With a Ni/SiO2/Au/SiNx compound function layer, in-situ localized surface plasmon of gold nano-particles on the top of multiple quantum wells nanorods was fabricated, whose integrated photoluminescence intensity show a 1.9-fold enhancement compared with bare nanorods.
A Bayesian Hierarchical Model for Speech Dereverberation

In this paper, the problem of speech dereverberation in a noiseless scenario is addressed in a hierarchical Bayesian framework. Our probabilistic approach relies on a Gaussian model for the early speech signal combined with a multichannel Gaussian model for the relative early transfer function (RETF). The late reverberation is modelled as a Gaussian additive interference, and the speech and reverberation precisions are modelled with Gamma distribution. We derive a variational Expectation-Maximization (VEM) algorithm which uses a variant of the multichannel Wiener filter (MCWF) to infer the early speech component while suppressing the late reverberation. The proposed algorithm was evaluated using real room impulse responses (RIRs) recorded in our acoustic lab with a reverberation time set to 0.36 s and 0.61 s. It is shown that a significant improvement is obtained with respect to the reverberant signal, and that the proposed algorithm outperforms a baseline algorithm. In terms of channel alignment, a superior channel estimate is demonstrated.
Sensorless Control of Linear Vernier Permanent-Magnet Motor Based on Improved Mover Flux Observer

Linear vernier permanent-magnet (LVPM) motors have a bright future in long stroke applications, because of their merits of high efficiency, high force capability, and low cost. However, the position sensor for the LVPM motor costs huge. In this article, a sensorless control based on an improved mover flux observer is proposed. The proposed mover flux observer combines a disturbance observer and a feedback controller. It can be easily implemented, which results in low computation burden. Due to simple tuning parameters of this observer, the mover flux observer can have strong robustness to the dc bias and the initialization errors of integrators that are main weakness of flux model. This proposed observer also does not introduce error in either the estimated mover flux amplitude or phase. Then, a phase-locked loop is used to detected position information from the mover fluxes. Finally, the LVPM motor operates by using the estimated position and speed signals. Simulation and experimental results of the LVPM motor demonstrate the accuracy and dynamic performance of the proposed sensorless operation.
A SUPERVISED MULTI-CHANNEL SPEECH ENHANCEMENT ALGORITHM BASED ON BAYESIAN NMF MODEL

In this paper, we introduce a supervised multi-channel speech enhancement algorithm based on a Bayesian multi-channel non-negative matrix factorization (MNMF) model. In the proposed framework, we consider the probabilistic generative model (PGM) of MNMF, specified by Poisson-distributed latent variables and gamma-distributed priors. In the training stage, the MNMF parameters of the speech and noise sources are estimated via the variational Bayesian expectation-maximization (VBEM) algorithm. In the enhancement stage, the clean speech signal is estimated via the MNMF-based minimum variance distortionless response (MVDR) beamformer. To further improve the enhanced speech quality, we efficiently combine the MNMF-based beamforming technique with a classical unsupervised single-channel enhancement method. Experiments show that the proposed method can provide better enhancement performance than the selected benchmarks.