Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Step Vehicle Classification System for Traffic Monitoring in the Philippines

Traffic congestion is a major problem in the Philippines. A vehicle classification system, for a proposed Philippine metropolitan traffic monitoring system, would be useful for various applications such as: road monitoring, law enforcement, and emergency vehicle prioritization. This paper implements a two-step vehicle classification system that classifies vehicles into three vehicle sizes and seven vehicle classes. Vehicle images are first extracted using the ViBe background subtraction algorithm on a Philippines metropolitan traffic sequence. The dataset used is comprised of properly segmented vehicle images. Geometry-based and texture-based features were extracted from the isolated image of each vehicle. Feature selection is used to determine the best features to be used in each classifier. Various machine learning models (kNN, SVM, MLPN) are used to implement the two-step vehicle classification system. The implemented vehicle size classifier uses a kNN model with an F-measure of 97.41%. Next, the medium vehicle classifier uses an SVM model with an F-measure of 90.68%. Finally, the large vehicle classifier uses an MLPN model with an F-measure of 97.97%. The resulting two-step vehicle classification system has an overall F-measure of 91.41%. This designed two-step classification system assumes that the input vehicle is properly segmented by background subtraction.
A High Bandwidth Evanescently Coupled Waveguide InGaAs/InP UTC-PD at Zero Bias

We present the fabrication of evanescently coupled InGaAs/InP UTC-PD. The InGaAs absorption layer of the PD is gradedly doped. As a result, a PD having 4 × 15 um 2 absorber size has an over 21 GHz bandwidth under zero bias, which is the largest one for evanescently coupled or edge coupled UTC-PD, to the best of our knowledge.
Frequency coupling characteristics of electromagnetic oscillation in grid‐connected converters

Under certain conditions, grid-connected voltage source converter (VSC) may result in electromagnetic oscillations. Many coupling components with different frequencies could be observed in converter outputs when oscillation happens. The characteristics and mechanism of frequency coupling phenomenon in oscillations of VSC are studied. The magnitude relationship between first-order components in the three-phase domain is drawn by impedance model. The two first-order components will result in higher-order components due to the non-linearity of VSC controller. There are mainly three coupling paths in a controller: phase-locked loop, DC voltage fluctuation and overmodulation. The coupling mechanism and frequency distribution characteristics are investigated in this study. Theoretical results are validated by simulations with PSCAD/EMTDC.
CyberPDF: Smart and Secure Coordinate-based Automated Health PDF Data Batch Extraction

Data extraction from files is a prevalent activity in today's electronic health record systems which can be laborious. When document analysis is repetitive (e.g., processing a series of files with the same layout and extraction requirements), relying on data-entry staff to manually perform such tasks is costly and highly insecure. Particularly analyzing a large list of PDF files (as a widely used format) to extract specific data and migrate them to other destinations for later use is both tedious and frustrating to do manually. This paper addresses a very practical requirement of batch extracting data from PDF files in health data document analysis. Specifically, we propose a Coordinate Based Information Extraction System (CBIES) to instrument a smart and automatic PDF batch data extraction tool, releasing health organizations from duplicate efforts and reducing labor costs. The proposed technique enables users to query a representative PDF document and extract the same data from a series of files in the batch analysis manner swiftly. Furthermore, since security and privacy considerations are essential part of any health record systems, it is included in our approach. Based on CBIES, we implement a prototype tool for PDF batch data extraction technique named, CyberPDF. The tool exhibits great efficiency, security and accuracy in multi-file data processing, supported through an empirical evaluation in this research.
An Ultra-Low Power Direct Active-RF Detection Wake-Up Receiver with Noise-Cancelling Envelope Detector in 65 nm CMOS Process

This paper presents an ultra-low power wake-up receiver (WuRx) based on direct active-RF detection architecture. A noise-cancelling technique is implemented on the RF envelope detector to minimize the noise due to the circuit components and to obtain a higher signal-to-noise ratio while maintaining its power within tens of microwatts. Compared to other wake-up receivers, this work focused on designing the receiver with reduced system complexity through a single-ended topology and minimized number of circuit components; thus, ultra-low power operation is attained. At 2.4 GHz RF operating frequency, a - 50 dBm sensitivity is achieved with 100 kbps data rate. Power consumption is measured as 13.7 µW from a 1 V supply voltage, obtaining 138 pJ/bit energy efficiency. The prototype receiver was designed using a 65 nm CMOS technology process and has a layout area of 0.194 mm2.
Flowzilla: A Methodology for Detecting Data Transfer Anomalies in Research Networks

Research networks are designed to support high volume scientific data transfers that span multiple network links. Like any other network, research networks experience anomalies. Anomalies are deviations from profiles of normality in a research network's traffic levels. Diagnosing anomalies is critical both for network operators and users (e.g., scientists). In this paper we present Flowzilla, a general framework for detecting and quantifying anomalies on scientific data transfers of arbitrary size. Flowzilla incorporates Random Forest Regression(RFR) for predicting the size of data transfers and utilizes an adaptive threshold mechanism for detecting outliers. Our results demonstrate that our framework achieves up to 92.5% detection accuracy. Furthermore, we are able to predict data transfer sizes up to 10 weeks after training with accuracy above 90%.
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inherent traffic patterns and combine the STD at the prediction time interval to consider the future disturbances. The experimental results on two real-world datasets demonstrate that DeepSTD outperforms the state-of-the-art methods.
The Silhouette Index and the K-Harmonic Means algorithm for Multispectral Satellite Images Clustering.

In this paper, we investigate the performance of the Silhouette index by applying the K-Harmonic Means algorithm to cluster different types of remote sensing datasets. The preliminary experimental results showed that the junction between the K-Harmonic Means algorithm and the Silhouette index is suitable for remote sensing images which present an insignificant degree of overlapping between clusters. Indeed, the Silhouette index returns a clear maximum which is synonym of the correct number of cluster. In contrast, the Silhouette index fails to return the real number of clusters when we deal with remote sensing data of which the clusters present a high degree of overlapping. To cope with this drawback, the angle based method was introduced to detect the correct number of clusters; the results prove the efficiency of the proposed process. Also, two comparisons were conducted, the first one between the well-known K-Means algorithm and the K-Harmonic Means and the second between the Silhouette index and the WB index in order to demonstrate the effectiveness of K-Harmonic Means algorithm and also the Silhouette index.
Weighted-Correlation based Band Reordering Heuristics for Lossless Compression of Remote Sensing Hyperspectral Sounder Data

The redundancies in remote sensing hyperspectral sounder data give an ample opportunity to compress the images in various domains. A novel scheme of band reordering for the lossless compression of remote sensing hyperspectral sounder images is presented in this paper. The algorithm returns upon a well-organized technique for three-dimensional image band reordering. The presented band reordering method uses different weights on their inter-band correlation value for obtaining the optimal band ordering. The performances of the proposed band reordering method are tested using the Consultative Committee for Space Data Systems (CCSDS) 123 lossless predictor and three-dimensional context-based adaptive lossless image coding algorithms (3D-CALIC). The experimental results show the significant improvement on compression gain by using the proposed band ordering technique for different hyperspectral sounder data. The improvement is about 4–5% using 3D-CALIC and 6.5–7.5% using CCSDS.
Digital Metering of Electrical Power Components Using Adaptive Non-Uniform Discrete Short Time Fourier Transform

Abstract This paper presents an adaptive Goertzel filter bank based discrete short time Fourier transform (DSTFT) implementation algorithm, called adaptive non-uniform discrete short time Fourier transform (ANDSTFT) for online measurement of electrical power components using IEEE Standard 1459–2010. The proposed ANDSTFT algorithm utilizes effective combination of non-uniform discrete Fourier transform (NDFT) and window method to detect the spectrum of each individual finite short time segment of power signals at distinct, arbitrarily located frequencies. Compared with the well-established technique such as windowed FFT interpreted DSTFT based approaches, the proposed method offers (i) better accuracy (ii) higher degree of immunity and insensitivity to noise, and (iii) reduced computational complexity per sample interval. The simulation results have been given and its response time and accuracy have been compared with the conventional windowed FFT interpreted DSTFT based techniques. Real-time implementation of the proposed approach has also been presented.
Low-Complexity Video Classification using Recurrent Neural Networks

Deep learning has led to great successes in computer vision tasks such as image classification. This is mostly attributed to the availability of large image datasets such as ImageNet. However, the progress in video classification has been slower, especially due to the small size of available video datasets and larger computational and memory demands. To promote innovation and advancement in this field, Google announced the YouTube-8M dataset in 2016, which is a public video dataset containing about 8-million tagged videos. In this paper, we train several deep neural networks for video classification on a subset of YouTube-8M. Our approach is based on extracting frame-level features using the Inception-v3 network, which are later used by recurrent neural networks with LSTM/BiLSTM units for video classification. We focus on network architectures with low computational requirements and present a detailed performance comparison. We show that for 5 categories, more than 96% of the videos are labeled correctly, where for 10 categories more than 89% of the videos are labeled correctly. We demonstrate that transfer learning leads to substantial saving in training time, while offering good results.
LOW-COMPLEXITY MESSAGE PASSING MIMO DETECTION ALGORITHM WITH DEEP NEURAL NETWORK

The channel hardening-exploiting message passing detection (MPD) algorithm achieves good performance in large-scale multiple-input multiple-output (MIMO) systems. However, for not-so-large systems with larger loading factors, the MPD suffers slow convergence rate and higher complexity. In this paper, a deep neural network (DNN)-aided message passing detector, DNN-MPD, is proposed. A modified MPD algorithm is firstly presented to compensate the approximation error in MPD, aiming at accelerating the convergence and attaining better performance. The DNN-MPD is then constructed by unfolding the modified MPD, and deep learning techniques are utilized to optimize the modification factors. Numerical results are presented to demonstrate the performance of the DNN-MPD compared with the original MPD. Through one training, the DNN-MPD can be used for multiple online detections, and it reaches faster convergence rate, outperforming the original MPD in the bit error rate (BER) performance in MIMO systems with large loading factors.
Automatic Traffic Sign Identification System for Real Time Operation

The traffic sign detection, recognition of signs is a vital part of automatic traffic sign identification system. Its main objective is to guide the driver to be aware of the essential traffic information during driving on the road and to be a major part of automated on-road driving system. The aim of the proposed work is to detect traffic sign which has illumination variation, partial occlusion and to classify using less computational resources and with reduced time complexity in real time environment. Motion model is designed to perform prediction for estimating the traffic sign with the help of kalman filter. Appearance model in the form of adaptive training based detector is used for the final traffic sign detection.
Transmission Performance Improvement of PAM-4 Signal Direct-Detected by Ge-Si Photodiode using Volterra Equalization

We demonstrate a 4-level pulse-amplitude-modulation (PAM-4) transmission system direct-detected by germanium-silicon (Ge-Si) photodiode (PD). Volterra-equalization is used to compensate signal distortion and 40-Gibt/s and 50-Gbit/s PAM-4 signals after 20-km and 10-km fiber transmissions are achieved.
Outage Performance of Two-hop Networks with Multiple SWIPT Relays under Nonlinear EH Model

Simultaneous wireless information and power transfer (SWIPT) has wide application in Internet of Things and wireless sensor networks. However, most existing works adopted the ideal linear energy harvesting model and perfect channel state assumption to analyze the system performance, which mismatches the nonlinear feature of practical circuits and neglects the channel estimation error. Therefore, this paper studies the outage performance of two-hop networks with multiple SWIPT relays under nonlinear EH model with imperfect channel state consideration. Decode-and-forward relaying protocols and time switching (TS) SWIPT receiver architecture are employed. The closed-form expression of the outage performance is derived over Rayleigh fading. Mont Carlo simulation results are provided to verify the correctness of the derived analytical model. It also shows that by increasing the number of relays, system outage probability could be effectively reduced and the smaller the selected number of relays, the smaller the system outage probability.
Patient Identification Based on Wrist Activity Data

In the hospital environment, authentication is required to allow patients to access constrained areas such as dedicated patient rooms. Identification is necessary for any authentication design, as the purpose of the authentication is to verify the identity of the user. This paper proposes a novel patient identification approach based on sensory data collected by activity sensors placed on the patients' wrist. It uses a support vector machine based classifier to identify different patients based on statistical and biophysical features extracted from the data. The evaluation results show that the proposed approach can achieve a high identification accuracy of 94.21%.
Optimized Measurements Coding for Compressive Sensing Reconstruction Network

Compressive Sensing (CS) is an emerging technology which can encode the original signal into several incoherent linear measurements and reconstruct the entire signal from a few measurements. Different from former coding schemes whose distortion mainly comes from the quantizer, the distortion is both related to quantization and measurement rate (MR) in CS based coding schemes. In this paper, we present an end-to-end image compression system based on CS. The presented system mainly integrates the conventional compressive sensing coding and the reconstruction with dead-zone quantization. We propose an optimized measurements coding scheme for our CS reconstruction network. We design the system parameters, including the choice of sensing matrix, the trade-off between quantization and MR, and the reconstruction network. Furthermore, the effective method can jointly control the quantization step and MR to achieve near optimal quality at any given bit rate. Therefore, our method can achieve a better balance between reconstruction quality and storage space.
Nonlinear Feature Extraction Based on Kernel Adaptive Marginal Fisher Analysis for Target HRRP Recognition

Feature extraction techniques have commonly been used as a principled way to understand the high-dimensional data. In this paper, we proposed an improved FE method based on marginal fisher analysis (MFA), called kernel adaptive MFA (KAMFA), to overcome the target-aspect sensitivity in radar target high resolution range profile (HRRP) recognition. Unlike many existing methods, such as linear discriminant analysis and principal component analysis, KAMFA aims at extracting the nonlinear feature of the samples by using the kernel trick. Moreover, KAMFA achieves better constraint to the intra-class compactness and interclass separability by assigning a weight, adaptively, to each sample based on the similarity measurement of geodesic distance between samples. Finally, the feasibility and effectiveness of the proposed method is verified on three ground target HRRP datasets with promising results.
Single-Event Burnout Hardness for the 4H-SiC Trench-Gate MOSFETs Based on the Multi-Island Buffer Layer

In this article, the performance and triggering mechanism of the single-event burnout (SEB) of a 4H-SiC trench-gate (TG) MOSFET structure are evaluated by the 2-D numerical simulations. The novel N+ island buffer 4H-SiC TG MOSFET and the conventional TG 4H-SiC MOSFET are analyzed and compared to examine whether an N+ island region introduced in the second buffer can effectively reduce the impact ionization located at the N− drift/N+ buffer junction and improve device tolerance to the SEB. The TCAD simulation results revealed that compared with the conventional structure, which is a simple double-buffer structure, the N+ island buffer-hardened structure changed the burnout threshold voltage, improving the SEB performance significantly. In addition, the results proved that the impact ionization played an important role in the SEB triggering mechanism, significantly affecting the SEB performance of the 4H-SiC TG MOSFET. The performance of the hardened N+ island buffer with a different dopant concentration of N-buffer 2 and a size of N+ island is discussed. The specific burnout threshold voltage at the optimal parameters of the proposed structure is 47% higher than that of the conventional structure.
Si-Based Hybrid Microfluidic Cooling for Server Processor of Data Centre

Si micro-fluid heat sink, which has attracted extensive attentions, can achieve high heat dissipation capability, while requiring very low pumping power and small coolant volume. Based on simulation investigation, a Si-based micro-fluidic solution has been developed for high performance processor cooling. A stacked micro-fluid heat sink with micro-jet slot array, micro-scale draining trenches and micro-pin fins, is designed and fabricated. With volume flow rate 1L/min and pressure drop smaller than 3kPa, spatially averaged heat transfer coefficient of $\sim9.6\times10^{4}\mathrm{W}/\mathrm{m}^{2}\mathrm{K}$ can be achieved using the designed micro-fluid structure. Experimental tests have been performed, and the results agreed quite well with the simulation data. To dissipate 150W heating power of thermal test chip, the maximum chip temperature rise can be maintained less than $25^{\circ}\mathrm{C}$. The developed hybrid microfluidic solution will be a good candidate for thermal management of server processor in future advanced data center.
An Effective Dual Closed-Loop Scheme Based on Lead Compensator and Proportional Controller for Quasi Z-Source Inverter

The emergence of quasi Z source inverter (qZSI) in 2008 has grabbed researchers’ attention to explore new enhancement control schemes and modulation techniques in the past decade. This paper proposed a single-phase qZSI with lead compensator in the control system driven by unipolar carrier based pulse width modulation (CB-PWM) technique. Specifically, the lead compensator acts as qZSI’s outer voltage loop controller while the current inner control loop is attained via the conventional proportional (P) controller. When compared with traditional dual loop based proportional integral (PI)-P controller, the proposed controller demonstrates its superiority for the qZSI to achieve good dynamic, steady-state, and transient performances under both the continuous conduction mode (CCM) and discontinuous conduction mode (DCM) operations. All design procedures are presented thoroughly in this paper and the effectiveness and theoretical analysis of the proposed approach are verified through simulation using MATLAB/Simulink software.
Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network

Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.
Control System Design of Power Assisted Bike Based on Planetary Gear

A planetary gear based transmission system for the power assisted bike (PAB) is proposed in this study because of its appealing 2-input/1-output transmission feature. Considering the practical requirements for power assistance, the electronically controlled continuously variable transmission (ECVT) and the torque assistance modes were realized for the PAB system. Unlike the conventional approaches which only provides either torque assistance or automated gear-shifting function, the planetary gear based transmission enables the PAB to provide a suitable power assistance mode in accordance with different riding conditions. The feasibility and effectiveness of this study is verified by experiment.