Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving PV Array Output During Partial Shading using Voltage Balancing Approach

Inhomogeneous insolation on a solar PV plant due to cloud, nearby construction, tree branches etc. reduces its power output significantly. Further, multiple peaks in power-voltage characteristics of the PV array are also observed due to partial shading, which calls for sophisticated and costly MPPT controller to make the the array operate at its global MPP. However, the enhancement in array output using global MPP is also limited by the array interconnections. This leaves scope for further improvement in array power output. In this work, a voltage balancing technique using DC-DC converters is developed to improve the array power output during partial shading. The results of simulation carried out to validate the proposed method for different shading level and pattern show its effectiveness in improving the power output. In addition, a case study on constant power load (CPL) using a PV-battery system demonstrates that the proposed strategy can also double as a demand-response controller for a PV array to feed CPL.
The Research of Electronic Countermeasure Intelligence Correlation Analysis Based on Machine Learning

Under the current informationized war conditions, the right to make information is of vital importance, and mastery of various sources of information has become a key factor in winning information wars. Radiation source intelligence is one of the important information on the battlefield. If you can grasp the tactics of enemy radiation sources in advance, you can grasp the opportunities of war. This paper will study the tactics of radiation sources, mainly from the synergistic operation of radiation sources, the law of switching machines, the situation of radiation sources and the networking of radiation sources.This paper applies machine learning to the tactical correlation analysis of radiation sources. The correlation analysis of the sorted radiation source data is performed by the correlation algorithm, time series analysis and statistical method in machine learning. The correlation between time and space is obtained, and the results are visualized and displayed through the system platform.
Multi-scale FAST feature extraction heterogeneous design based on FPGA

Mage feature corner detection, is the first step in many visual positioning algorithms, such as tracking, positioning, slam. The fast feature corner point extraction method can greatly improve the real-time performance of positioning, thereby improving the positioning accuracy of objects in high-speed motion scenes, what’s more the accelerated implementation of this process is also emerging through hardware system, and it has be valued and used by more and more developers and researchers, However Resource consumption problem and video stream data make this process difficult, So we design an accelerator by FPGA heterogeneous design scheme to fix the problems and The system processes 640x480 resolution images at 50fps with a delay of 44ms.
Boundary Protection System Based on Software-Defined Networking

The framework of separated data and control planes in software-defined networking (SDN) with high programmability makes it more flexible to manage and control network traffic. In this paper we propose a boundary protection system based on software defined networking, which is composed of an intrusion detection module (IDM) and a boundary protection module (BPM). The IDM is embedded into the SDN switches on the data plane to realize the monitoring and warning of abnormal events. The BPM is deployed upon the Open Network Operating System (ONOS) controller to install flow rules on the SDN switches on the data plane with the assistance of ONOS controller. It accesses ONOS controller via a Restful API. Once an abnormal event is detected, the IDM reports it to the BPM, the BPM can make intelligent decisions to prevent abnormal packets in the whole network, this promote the defensive capacity of the boundary protection system. Also the BPM realizes functions such as protocol filtering, blacklist controlling, ACL controlling and warning logging.
DOUBLE SARSA BASED MACHINE LEARNING TO IMPROVE QUALITY OF VIDEO STREAMING OVER HTTP THROUGH WIRELESS NETWORKS

The adaptive streaming over HTTP is widely advocated to enhance the Quality of Experience (QoE) in a bitrate constrained IP network. However, most previous approaches based on estimation of available link bandwidth or fullness of media buffer tend to become ineffective due to the variability of IP traffic patterns. In this paper, we propose a Double State-Action-Reward-State-Action (Sarsa) based machine learning method to improve user QoE in IP network. The Pv video quality estimation model specified in ITU-T P.1203. 1 recommendation is embedded in the learning process for the estimation of QoE. We have implemented the proposed Double Sarsa based adaptation method on the top of HTTP in a 4G wireless network and assessed the resulting quality improvement by using full reference video quality metrics. The results show that the proposed method outperforms an existing approach and can be recommended in standardization of future audio-visual streaming services over wireless IP network. We observed the average improvement of 7% in PSNR and 25% in VQM during the live streaming of video.
Grouping Subarray for Robust Estimation of Direction of Arrival

Multiple Signal Classification (MUSIC), Steered Response Power-PHAse Transform (SRP-PHAT) and Generalized Cross Correlation (GCC) are the well known techniques for Direction of Arrival (DoA) estimation, using microphone array. However, in real time scenarios, these techniques encounter limitations such as computational complexity and thresholding difficulties. In this paper, a novel and robust method is introduced in which DoA is estimated using the concept of subarray decomposition to provide better performance with effective thresholding and minimal computational complexity.
Facial Expression Recognition Using Geometric Landmark Points and Convolutional Neural Networks

Facial Expression Recognition (FER) is a prevalent research area in human-computer interaction. In general, any FER technique contains three steps: feature extraction, dimensionality reduction, and classification. Dimensionality and feature selection are major issues concerning FER. Huge amount of memory and processing are required to process image as a whole. A more suitable alternative to this problem is geometric features. In this paper, facial landmark detection has used for feature extraction and Convolutional Neural Network Classifier. The method has tested on publicly available benchmark datasets: JAFFE, MUG, CK, and MMI. The results with the proposed methodology outperform the existing methods found in the literature.
Stereo Visual Odometry for Mobile Robot

This paper presents a visual localized approach, based on Speeded Up Robust Features (SURF) from a stereo system with optimization tool constraints to obtain high matching precision between images. The contribution of this paper presents a robust visual odometry and a 3D reconstruction algorithm based on Adaptive Iterative Closest SURF Point (AICSP). This algorithm combines the robustness of SURF to detect and match a good feature, and the accuracy of the Adaptive ICP algorithm, which is used to give more importance for near 3D weighted points with their inverse depth. The proposed algorithm is validated and compared to other optimization techniques based on Singular Values Decomposition (SVD) and Quaternion. Experimental results show robustness, accuracy and acceptable outcomes from our algorithm in both: indoor and outdoor environments using Pioneer 3-AT.
A Wide-Band Dual Circular Polarization UHF RFID Reader Antenna Based on Miniaturized Branch Line Coupler

In this paper, a dual circularly polarized (CP) antenna with broad bandwidth for ultra high frequency (UHF) radio frequency identification (RFID) reader application is presented. The proposed antenna consists of two layers of FR4 substrates with thickness of 1.6 mm and one layer of air. The air layer can be utilized to achieve higher gain and broader bandwidth. The rectangular radiation patch is situated on the top surface of the upper substrate. To decrease the overall size of the antenna, a miniaturized branch-line coupler based on capacitance loading technology is used as the feeding network. And the miniaturized branch-line coupler is located on the bottom side of the lower substrate. One port of the coupler is used as input, one port is connected to a 50 ohm load, and other two ports are the output, which have a fixed 90 degree phase difference. To realize circular polarization radiation, the two output ports are placed orthogonal. The circular polarization of different rotation (left-hand circular polarization (LHCP) or right-hand circular polarization (RHCP)) can be realized when the input port and the load port are exchanged. The ground plane with a pair of bended slot is on the top side of the lower substrate. Energy is coupled to the radiation patch though the slot on the ground. To enhance the directionality of the radiation pattern, the slot has a length of $0.42\ast \lambda_{0}$, where $\lambda_{0}$ is the wavelength of 0.9 GHz in free space. When one port is excited, the proposed antenna obtains a −10 dB bandwidth of 190 MHz (0.81–1.0 GHz) and 3-dB AR bandwidth of 160 MHz (0.85-1.01 GHz). The peak gain is 7.4 dBi at the frequency of 0.9 GHz. In the global UHF RFID band (0.840–0.96 GHz), the gain is greater than 3 dBi. Thus the proposed antenna can be well used in long distance read RFID system and can be used in different areas, such as America, Japan, Europe and so on.
Wireless ECG Patient Monitoring System Performance Aimed at Off-/On-body Communications

World Health Organization (WHO) reported that heart disease, stroke, lower respiratory infections and chronic obstructive lung disease have remained the top major killers in the world during the past decade. In Indonesia, it covers 37% or about 1.8 millions mortality cases. Hence, human vital sign monitoring such as electrocardiograph (ECG) monitoring system is very essential for mitigating and preventing people hearth's fail to be handled a proper assistance from healthcare physicians. For that purpose, we propose a real time ECG monitoring system, which the ECG data is monitored wirelessly directly without any server for data pooling (centralized database). ECG QRS wave and arrhythmia detection are important features for ECG monitoring system. Up to now, there are several studies ECG wave detection, however, they are mainly focus on internet of things - based telemedicine system for data transfer. In this case, real time monitoring system could be disrupted by a pooling time and packet loss due to its IP-based connection. Hence, we propose a wireless ECG patient monitoring system based on point to point communication scheme at 2.4 GHz band by using 3-lead acquisition system on single channel wireless ECG device. The system is developed based on AD8232 chip for acquisition, NRF24101 wireless link platform for data transfer and Arduino™microcontroller for ECG data processing. In order to evaluate the performance of wireless ECG system, our developed wearable textile antennas (as Tx and Rx antenna) are attached on the human body and experimentally evaluated within indoor environment, simulating off-/on-body communication system. The wearable textile antenna is lightweight, small and insensitive to the resonant frequency due to the human body effect. The measurement is conducted by embedding the antennas at some body positions of the volunteers. The experimental results show that the proposed ECG monitoring system is able to transfer and display real time ECG signals well for off-/on-body communication schemes. In addition, to validate the results, the obtained ECG data are compared and analyzed with the healthcare clinic equipment. According to QRS wave analysis, the ECG signals have reasonable RR, PR, QRS and ST wave interval in comparison with the standard ECG signals. We convince that our developed ECG patient monitoring system is stable by applying our developed textile antennas and it is promising to be adopted in the hospital for future implementation.
Inferring Control Intent During Seated Balance Using Inverse Model Predictive Control

Patients with low back pain are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Model Predictive Control (iMPC) problem. The standard Model Predictive Control (MPC) structure includes constraints, therefore, it allows us to model the physiological constraints of motor control. We devised an iMPC algorithm to solve iMPC problems with experimentally collected output trajectories. We used experimental data of one healthy subject during a seated balance test that used a physical human–robot interaction. Results show that the estimated MPC weights reflected the task instructions given to the subject and yielded an acceptable goodness of fit. The iMPC solution suggests that the subject's control intent was dominated by minimizing the squared sum of a combination of the upper-body and lower-body angles and velocities.
ECHO: Efficient Zero-Control Network-Wide Broadcast for Mobile Multi-Hop Wireless Networks

Network-wide broadcasting is a key requirement in military networks for supporting applications such as situation awareness and emergency messages. Current methods are based on flooding, or use control packets which limits scalability. We present a novel protocol called ECHO that constructs and maintains a broadcast backbone without using any control packets. Instead, using a field in the data packet header, a node listens for an “echo” of the specific packet that it transmitted to determine its membership in the backbone. ECHO is deterministic, source-independent, fully distributed, accommodates mobility, and balances battery consumption across nodes. We prove the correctness of ECHO and show that its communication complexity is O(N) lower than that of Flooding and Multi-Point Relay (MPR) in dense networks. Simulations over random mobile networks show that ECHO uses 2.5x-9x fewer transmissions than Flooding to achieve similar delivery ratio. Experiments on a 12-node testbed of goTenna mobile mesh networking devices show that ECHO reduces transmissions by 3x, and increases battery life by >2x over Flooding. ECHO's performance advantages are crucial for scalable broadcast in low-power, low-capacity military multi-hop wireless networks.
Shoreline data based sea-land segmentation method for on-orbit ship detection from panchromatic Images

Sea-land segmentation is a key procedure of ship detection. However, most of the existing sea-land segmentation methods are designed for on-ground ship detection. Consequently, the computational costs of these methods are too expensive to be implemented in on-orbit platforms. To tackle this problem, first, a shoreline database is built according to the global geographic information systems; then, the sea-land segmentation method is proposed by utilizing the corresponding coastline data, which is obtained from the database according to the latitude and longitude of the candidate area. Compared to the existing sea-land segmentation methods, this method is more suitable for on-orbit platforms, because of the less computational costs, higher efficient, and acceptable robustness. The experimental results based on raw panchromatic images demonstrated that the proposed method had a good performance in sea-land segmentation for on-orbit processing.
Design of RFID Tag Antenna Based on the Cole-Cole Model of Human Abdomen

In this work, electromagnetic human abdominal model is proposed with four layers including skin, fat, muscle, and small intestine. For each layer, fourth-order Cole-Cole model is chosen to calculate the permittivity. Combining with the electromagnetic propagation in multilayer media, we present reflection coefficients and phase information of incident wave among UHF band with different thickness of each layer. Then we give priority of consideration of each layer for RFID antenna design. According to RFID principle, we present the gain loss of RFID antennas attached to human abdomen, which shows that general gain loss of RFID antennas is 7 dB–13 dB compared with the antenna used in air.
Automatic Construction of Attack Graphs in Cyber Physical Systems Using Temporal Logic

This paper describes a novel approach for the automatic construction of attack graphs, a powerful visual representation to depict system vulnerabilities in cyber physical systems. The proposed approach generates a maximally complete set of execution traces by performing formal verification reachability analysis using temporal logic formalism. Temporal logic is used to analyze network state transition events for attack graph generation. The attack graph workflow is constructed with a scanner, a locator and a synthesizer for formal verification, vulnerability detection and sensitivity quantification respectively. Maximum Likelihood reasoning and attack covariance estimation is used to locate vulnerabilities and generate attack event trees. A data handshake communication protocol in a generic cyber physical system is analyzed as a use case to illustrate the effectiveness of this formal approach.
Accurate Specified-Pedestrian Tracking from Unmanned Aerial Vehicles

Recently, accurate target tracking is widely used in the field of Unmanned Aerial Vehicles (UAV). In this paper, we focus on the application of detecting and following a walking pedestrian in real time from the moving platform with many interferences. We present a scheme that uses CNN model (YOLO-V2) to detect pedestrian and matches the walking pedestrian with a postprocessing and feature queue and Locality constrained Linear Coding algorithm. After that the ground station receives and analyses the video stream from the parrot and sends back commands to control the motion of UAV. At the beginning of the tracking process, the UAV is hovering when one pedestrian will be selected as the special target. Visual information is acquired only through a front camera without assistant sensors. A parrot Bebop 2 is adopted in the experiment, which is the basis for doing experiments outdoors and experimental result verify the effectiveness of our solution.
Reactive Power Dispatch of Inverter-Based Renewable Distributed Generation for Optimal Feeder Operation

Driven by technical challenges and environmental needs, there has been significant interest towards integrating more renewable energy sources (RESs) into the electricity grid. However, the high penetration level of RESs is usually accompanied with a few operating challenges such as voltage variation, reverse power flow, and frequent operations of Volt/Var control devices. To address these challenges, this paper presents a centralized coordinated voltage and reactive power control for a medium-voltage (MV) active distribution network (DN) dominated by photovoltaic (PV) and wind power systems. The proposed model minimizes the operating cost by minimizing the real power loss and reducing the number of OLTC operations while maintaining the network voltage within the allowable limits. The objective is to determine the required reactive power from the inverter based RESs (IBRESs) and the optimal setting of the OLTC. The coordination problem is formulated as a mixed-integer nonlinear programming (MINLP) optimization problem and solved by GAMS software. The proposed model is validated by a two-feeder MV distribution system connected to a sub-transmission system via a substation transformer. The results show that the proposed model can optimize the operational cost of active DNs. Hence, utility and customer satisfaction can be met.
A Fuzzy Probabilistic Power Flow Method Based on Fuzzy Copula Model

With increasing renewable energies integrated into power grid, the variability and uncertainty in power system are becoming dramatic and unpredictable. Probabilistic Power Flow was proposed to handle the uncertainties, by introducing random variables with specific probability distributions into calculation. However, the current PPF usually adopts correlation coefficient matrix as its correlation model, which is incomplete for probabilistic modelling. And the deterministic distributions in PPF cannot fully describe the randomness of renewable energy. To address the problem, this paper proposed a fuzzy Copula model for building a more complete and real probability model. And based on the fuzzy model, a whole scheme of Fuzzy Probabilistic Power Flow (FPPF) was established. Case study showed that FPPF can help obtain more possible distribution scenarios of objective random variables, indicating its superior reliability and practicability than conventional PPF.
A Nested Tensor Product Model Transformation

The tensor product model transformation (TPMT) is an emerging numerical framework of the Takagi–Sugeno (T–S) fuzzy (or polytopic) system modeling for a linear matrix inequality based system control design. A nested TPMT (NTPMT) is proposed in this paper, which merges the dimensions of the tensors and performs the TPMT iteratively. The resultant fuzzy model is in a multilevel nested tensor product (TP) structure. The vertex tensor obtained by NTPMT has fewer dimension results than the original TPMT so the number of vertices or fuzzy rules, which have been the main bottleneck for further application of the TPMT in higher dimensional systems, is expected to decrease significantly. It is also proven that the NTPMT contains the hierarchical fuzzy logic, meaning that the NTPMT is capable of conducting hierarchical fuzzy modeling and reduction. Furthermore, because the inclusion of multiple TPMTs is prone to augment the conservativeness of the resultant fuzzy model, a suboptimal convex hull rectification algorithm for the TPMT is developed based on a newly defined tightness measure, and then extended to render the NTPMT as less conservative as possible. Finally, numerical simulations on two real physical systems (two- and four-parameter dimension) are verified to demonstrate the performance of the methods.
Detection and Tracking of Human Beings in a Video Using Haar Classifier

Detection and tracking of moving objects has immense relevance when it comes to tasks such as video surveillance and computer vision applications. The design of a video surveillance system is directed on automatic identification of events of interest, especially on tracking and classification of moving objects. A video surveillance system consists of three phases: moving object recognition, tracking, and decision making. This paper focuses on detection of human beings in a scene, and tracking those people as long as they stay in the scene by identifying individual persons. Automating the video surveillance process will help in effortless monitoring of the sensitive areas with less human resource utilisation.
Approaches to Codec Independent Speaker Identification in Voip Speech

The performance of automatic speaker identification (ASI) systems on Voice over Internet Protocol (VoIP) speech varies with the type of codec used in the VoIP communication. The type of codec used depends on the service provider of the user. Thus there is a need for the codec-independent ASI systems to identify the speaker. Three modeling approaches based on UBM-GMM framework and i-vector framework are proposed to identify the speaker independent of codec used. These frameworks are also evaluated for the mismatch conditions with respect to the codec used in training and testing. The proposed approaches are evaluated on VoIP speech from four codecs with different bit rates along with uncoded speech.