Miracle-dz/UniFAR-FTUs huggingface dataset:
A Human-Computer Interaction System Utilizing Inertial Measurement Unit and Convolutional Neural Network

This paper acquaints a head gesture and eye blink controlled human-computer interaction device to obstruct carpal tunnel syndrome (CTS) and making the computer mouse accessible for the differently able individuals. Traditional computer mouse puts a user’s hand for too long in a pronation posture which increases inflammation in the wrist and hand. To solve this problem, in this paper as an alternative to the customary computer mouse is introduced. The system uses three-axis accelerometer and gyroscope to detect the head gesture and translate it into the position of the mouse cursor on the computer screen. The complementary filtering technique obtains a smooth and accurate output from the inertial measurement unit (EMU). To perform the left and right clicking event, the user’s need to shut the left and right eye for a moment while opening another eye. The system usages the deep convolutional neural network (CNN) to classify the individual openness and closeness of both eyes. The implementation details along with the performance evaluation results are delineated in this paper show that the system has extensive control over its performance.
A Framework for Distributed Denial of Service Attack Detection and Reactive Countermeasure in Software Defined Network

Software Defined Network is an evolving and promising architecture which allows greater control over network entities by centralising the control plane. Although on the surface SDN provides a simple framework for network programmability and monitoring, few has been said about security measures to make it more robust to hitherto security flaws. Among the identified security flaws, DDoS flooding attack continue to be one of the major security concerns as attack volumes are increasing year on year. In this paper, we developed and implement the feasibility of spoofing and flooding DDoS attack on data plane devices using Mininet emulator, floodlight and network performance testing tools. We further developed a mitigation mechanism to counter these attacks by pushing reactive flow through the controller to the attacking switch port. Our result shows that pushing flows through the controller mitigates the flooding attack with low performance overheads, and requires no change to the controllers mode of operation for deployment, which indicates a good performance of our model.
On-Chip CMOS Self-Decoupling Battery Cell System for Security Protection

This article presents an effective on-chip power analysis attack countermeasure based on a new CMOS self-decoupling battery cell system that uses a self-decoupling circuit. The self-decoupling circuit dynamically controls an on-chip virtual power supply point, <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {ddv}}$ </tex-math></inline-formula>, that can be used to power security-sensitive modules. The circuit automatically decouples an on-chip CMOS battery cell from powering a sensitive module when its voltage level reaches a designed minimum threshold level <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {dd-min}}$ </tex-math></inline-formula> and connects it for a very short charging cycle to the chip’s main voltage supply, <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {dd}}$ </tex-math></inline-formula>. The charging cycles for the experiments presented in this article are less than 10 ns and are designed to support the CMOS battery cell size and the minimum designed threshold voltage level <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {dd-min}}$ </tex-math></inline-formula>. Simulation results of test designs implemented in the 45-nm CMOS technology process show that the proposed countermeasure is efficient when used with battery cell sizes that can power the protected cryptographic module for more than ten data operation cycles before recharging. In addition, using the on-chip self-decoupling battery cell system allows for power consumption savings within the protected module of up to 43 % due to the dynamic voltage scaling generated at the virtual power supply point.
Recording and Characterization of EEGs by Using Wearable EEG Device

Electroencephalographic (EEG) record provides the state of the brain activations and is useful for the clinical diagnosis of the brain dysfunction. However, the preparation of EEG recording such as attachment of electrodes, measurement device setting, etc., is time-consuming and complex. In addition, interpretation of recorded EEG requires particular knowledge and experiences. Therefore, the use of EEG recording is limited. Recent years, simple and easy wearable EEG devices have been developed and are considered to be the practical level. Those devices are easy to attach the measurement electrodes to the scalp without special skills and do not constrain the subjects' movement. In contrast to conventional EEG recordings, the number of electrodes is limited, so the accurate interpretation and/or analysis of EEG characteristics would rather be difficult. Especially, discrimination of actual EEG activities from various contaminated artifacts are crucial for clinical application. In this study, the characteristics of the recorded EEG by using a wearable EEG device was analyzed. Automatic detection method for contaminated artifacts such eye blinking, lateral eye movement (LEM) and electromyographic (EMG) activity were developed. Results were compared with the visual inspection for the recorded data.
Analysis of Robust Feedback Linearisation Control for Wind Turbines based on Permanent Magnet Synchronous Generator

The objective of this paper is to assess parameter robustness of a controller design based on feedback linearisation applied to a wind turbine with a permanent magnet synchronous generator (PMSG). To study robust local stability, we employ an analysis of the eigenvalues of the small signal model. Some controller parameters are deduced based on measured system variables. It is therefore of interest to know bounds on measurement errors that are guaranteed to preserve local stability. Finally, simulations of a 2MW direct drive PMSG wind turbine system illustrate the effectiveness of the method.
Machine Learning Applications in Computer Emergency Response Team Operations

At the European level, a significant effort was exerted in order to define the regulatory framework for cyber security and establish Computer Emergency Response Teams (CERTs) to handle cyber threats and incidents. However, this topic is still underrepresented in the academic research, probably because of its novelty. Bearing this in mind, in this paper we reviewed the existing cyber security literature and discussed how the already existing methods could be used for providing more efficient CERT services.
A regional protection scheme for dynamic distribution network with multiple branches and DG

A new regional protection scheme based on the fault associated zone is proposed for dynamic distribution network with multiple branches and distributed generations (DG). First, the system structure of regional protection and tripping strategy are analyzed. Then, the correlation relationship of protections are depicted as a protection correlation matrix based on the relationship between protection and branches. On this basis, the associated zone recognition algorithm based on the protection correlation matrix is proposed. When the network changed dynamically, the regional switch status vector and the switch direction vector are used to adjust the protection associated zone. The simulation results of the real distribution network examples verified the effectiveness of the proposed method.
A DC-51.5 GHz Electro-Absorption Modulator Driver with Tunable Differential DC Coupling for 3D Wafer Scale Packaging

This paper presents a DC-51.5 GHz PAM-4 dual-channel electro-absorption modulator (EAM) driver realized in a 0.25-µm SiGe:C BiCMOS technology. The EAM driver is designed for 3D wafer scale packaging which integrates silicon electronics IC and InP photonics IC at wafer scale. A new asymmetric-load differential driver topology is proposed to achieve a tunable DC biasing for the EAM without extra off-chip bias-T, which significantly reduces the packaging complexity and cost. Moreover, the driver uses differential outputs to drive a single-ended EAM, which reduces the voltage swing by a factor two and reduces the power consumption. The driver has 9.4 dB gain with a 3 dB bandwidth of 51.5 GHz and −0.2 ~ −2 V tunable output DC biasing range. It delivers a differential output voltage swing of 2 Vppd at 56 Gb/s PAM-4 and consumes 219 mW per channel, resulting in a figure of merit of 3.9 pJ/bit.
A Novel Feature Extraction and Selection Technique for Chest X-ray Image View Classification

Chest X-ray (CXR) image view information can play an important role to make a computer-aided diagnosis (CAD) system more superior and robust. Moreover, if the image header file provides the view label, we can apply specialized techniques for different positional views. Since this type of information is not always available with the image, a view classifier is essential. In this paper, we present a method to classify the two most common CXR projection views-posteroanterior (PA) and anteroposterior (AP). We developed the method specifically with four key components-pre-processing, feature extraction, feature selection, and classification. The features we extracted are histogram of oriented gradients, image projection profile and our developed feature named ‘CXF30’. We generated this new feature from the signal properties of vertical image profile feature. For obtaining an optimal subset of features, we applied a genetic algorithm-based feature selection technique. Finally, for classification purpose, we trained a random forest machine learning model with the selected features. We evaluated the method on a large dataset consists of 24,000 chest X-ray images, and consequently, it achieved a high 10-fold cross-validation accuracy (above 96%). This result indicates the effectiveness of the presented method. In addition, through different experiments, we justified the quality of our extracted features and also demonstrated how feature selection process improved the performance of the model.
Design and Implementation of Connected DataLake System for Reliable Data Transmission

Currently, the edge cloud for efficient management of IoT and various devices is increasing, so there is a growing need to safely and flexibly manage the stored data in the Edge Cloud. One of the most effective ways to efficiently manage the growing edge cloud is to store the data of each distributed edge cloud to the cloud. In this paper, we design and implement a connected DataLake system based on distributed cloud storage that delivers data stored in multiple Edge Clouds securely to micro cloud storage. In addition, the system performs real-time error recovery such that the transferred data can be restored to the abnormal point when an abnormality occurs during transmission. The developed Connected DataLake system was proved to show the efficiency of the system in the KOREN/TEIN network.
Multimodal Biometric System using Grasshopper Optimization

Biometric systems are need of the day because of their various advantages over traditional authentication systems. Multimodal Biometric systems combine information from multiple sources to reach a final decision. Score level fusion combines outcomes of individual classffiers to make a final decision. However, most of the biometric systems suffer from the issue of score confliction of individual classifiers. To resolve this issue, we have proposed a novel optimized score level fusion using Grasshopper optimization where the performance optimization of individual classffiers is performed and a concurrent solution is achieved by means of proportional conflict redistribution rules. The system does not require any classifier training and exhibits high performance. The proposed system is robust against the dynamic environment and exhibits high reliability.
Direct Power Control of Dual Active Bridge Bidirectional DC-DC Converter

This paper presents a DC-DC bi-directional dual active bridge (DAB) power converter, and simulating direct power control (DPC) control for the power converter. The DPC control enables it to have an enhanced dynamic response with improved dc-link voltage stability while controlling the power flow. The output voltage is controlled by the amount of power flow while the power flow depends on the phase shift ratio between primary and secondary bridge. With the DPC control presented here, the control reference is calculated from output voltage and load current, and subsequently phase shift ratio is computed, thereby controlling the power flow. The fast control over the amount power flow provides an overall enhanced performance with improved reference voltage tracking as well as robustness against input voltage fluctuation and load variation. The proposed control scheme is simulated in Matlab Simulink environment.
To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks

Firms are now considering to offer rewards to customers who recommend the firms' products/services in online social networks (OSN). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes. Via empirical analysis of data, we first identify key factors that affect the spreading of a firm's product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the "Independent Cascade model". We also design algorithms to infer model parameters. Using these parameters in our model, we uncover conditions when social recommendation can (or cannot) improves a firm's profit. These conditions help a firm to design rewarding schemes. Finally, we extend our model to an online setting and design reinforcement learning algorithms for a firm to dynamically optimize its rewarding schemes.
Boundary Point Measurement Using Visual Technique

The global positioning system real-time kinematic (GPS-RTK) instrument is a commonly used instrument to obtain GPS coordinates in cadastral surveying, but its use is limited to unshielded environments. This study proposes a new method based on a visual simultaneous localization and mapping (SLAM) algorithm that uses a stereo-camera to replace traditional methods. The benefits of our method are a higher measurement efficiency, fewer required professionals, and fewer restrictions on the operating environment compared with the traditional RTK and total station method. First, we propose a robust non-planar camera calibration algorithm that uses three non-planar chessboards to obtain intrinsic parameters of a stereo-camera to improve the accuracy of the measurement. Second, we propose a detailed algorithm that uses a stereo-camera and known GPS coordinates to obtain unknown GPS coordinates. Using experiments, we demonstrated the effectiveness and stability of our method to provide accurate GPS coordinates.
Temperature Sensing and Data Transmission Mechanism for High Temperature Applications

Most of the electronic devices do not perform well in high temperature. In wafer fabrication, data propagation through thick metallic chamber walls need more transmitting power for successful data transmission. Measuring and transmitting temperature in closed metallic high temperature environment is a challenging issue. In this paper, a scheme to sense, process and transmit the data through fully closed and thick Aluminum chamber is proposed. The system is capable of sensing high temperature while handling for power fluctuations. Temperature is measured using tiny RTD resistors placed in different 7 locations up to 350 degree Celsius. The data is transmitted as radio-waves using ASK and FSK modulation techniques at the rate of 1 kbps. Processed demodulated data at the receiver-end provides the power supply voltage level, RTD locations ID, and the temperature in different locations of the IC inside the chamber real-time. Re-transmission technique is proposed to reduce the propagation loss. It ensures possibility of the transmitter to work in low power environment. Experimental results show the effectiveness of the proposed system to over come the limitations of measuring and transmitting real-time data.
The Conceptual Framework of Online Problem-Based Learning Towards Problem-Solving Ability and Programming Skills

Computer programming is a tough course that involved complex activity. The most important activity for students while learning programming is to learn how to create their own programs. Even though there are few learning approaches that have been proposed to assist students especially novice students in learning programming, few researchers believe that students face such difficulties due to their lack of ability to analyze problems. Due to that matter, we have intended to propose a conceptual framework of problem-based learning or PBL with the aim to improve students' problem-solving ability and programming skills. Besides, through the increase development of technology in recent years, the PBL tasks will be conducted via online learning setting. The aim of this paper is to develop a conceptual framework of online problem-based learning towards students' problem-solving ability and programming skills. The input-process-output (IPO) model was used in describing the conceptual framework of this study. Inputs lead to processes that in turn lead to outcomes. The proposed conceptual framework will be useful for guiding the online instructors to effectively organize and design teaching tasks and materials for the online PBL to take place in order to assist students' problem-solving ability and computer programming skills.
ADGS: Anomaly Detection and Localization Based on Graph Similarity in Container-Based Clouds

Docker container is experiencing rapid development with the support from the industry like Google and Alibaba and is being widely used in large scale production cloud environment. For example, Alibaba has deployed millions of containers for its internal business, and most of the online services are already migrated to the containers. Those services are usually very complex, spanning multiple containers with complex interaction and dependency relationship. Detecting potential anomalies in such a large container-based cloud platform is very challenging. Traditional detection models usually use system resource metrics like CPU and memory usage, but rarely consider the relationship among components, causing high false positive rate. In this paper, we present a novel Anomaly Detection and root cause localization method based on Graph Similarity (ADGS) in the container-based cloud environment. We first monitor the response time and resource usage of each component in the application to determine whether the system status is normal or not. Then, we propose a new mechanism to locate the root cause of the anomalies based on graph similarity, investigating the anomaly propagation rules among cluster components. We implement and evaluate our method in a container-based environment. The results show that the proposed method can detect and determine the root cause of anomalies efficiently and accurately.
Millimeter-Wave CMOS Phased-Array Transceiver Toward 1Tbps Wireless Communication

The wireless communication is one of the key technologies for realizing the future smart society. More than 100Gbps data rate is targeted for satisfying the social demand. In this presentation, some design examples of millimeter-wave transceivers will be introduced such as 120-Gbps W-band transceiver, 28-GHz bi-directional phased-array transceiver. The talk concludes with a discussion on future directions of millimeter-wave wireless communication, based on Shannon and Friis equations.
State of the Art of Solid-State Transformers: Advanced Topologies, Implementation Issues, Recent Progress and Improvements

Solid-state transformer (SST) is an emerging technology integrating with a transformer power electronics converters and control circuitry. This paper comprehensively reviews the SST topologies suitable for different voltage levels and with varied stages, their control operation, and different trends in applications. The paper discusses various SST configurations with their design and characteristics to convert the input to output under unipolar and bipolar operation. A comparison between the topologies, control operation and applications are included. Different control models and schemes are explained. Potential benefits of SST in many applications in terms of controllability and the synergy of AC and DC systems are highlighted to appreciate the importance of SST technologies. This review highlights many factors including existing issues and challenges and provides recommendations for the improvement of future SST configuration and development.
A Wasserstein Subsequence Kernel for Time Series

Kernel methods are a powerful approach for learning on structured data. However, as we show in this paper, simple but common instances of the popular R-convolution kernel framework can be meaningless when assessing the similarity of two time series through their subsequences. We therefore propose a meaningful approach based on optimal transport theory that simultaneously captures local and global characteristics of time series. Moreover, we demonstrate that our method achieves competitive classification accuracy in comparison to state-of-the art methods across a wide variety of data sets.
ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation

Treatment effect estimation refers to the estimation of causal effects, which benefits decision-making process across various domains, but it is a challenging problem in real practice. The estimation of causal effects from observational data at the individual level faces two major challenges, i.e., treatment selection bias and missing counterfactuals. Existing methods tackle the selection bias problem by learning a balanced representation and infer the missing counterfactuals based on the learned representation. However, most existing methods learn the representation in a global manner and ignore the local similarity information, which is essential for an accurate estimation of causal effects. Motivated by the above observations, we propose a novel representation learning method, which adaptively extracts fine-grained similarity information from the original feature space and minimizes the distance between different treatment groups as well as the similarity loss during the representation learning procedure. Experiments on three public datasets demonstrate that the proposed method achieves the best performance in causal effect estimation among all the compared methods and is robust to the treatment selection bias.
Study on Sub-synchronous Oscillation of Grid-connected VSC via Series Compensation Lines

This paper mainly concentrates on the mechanism studies of the sub-synchronous oscillation (SSO) caused by the grid-connected VSCs when integrated into the grid via series compensation lines (SCLs). To further analyze the impacts of VSC's terminal voltage feed-forward (TVF) on this SSO, a linearized model of VSC in current control timescale (less than 20ms) is proposed. This model divides the current control loops of VSC into two loops, namely the current controller loop and the TVF loop. By analyzing these two loops, it is able to reveal the mechanisms of the SSO caused by VSC via SCLs. The analytical results indicate that the TVF will introduce a negative damping into the system through the TVF loop when VSC integrated into the grid via SCLs. Besides, with the increase of the TVF filter bandwidth, the negative damping introduced by TVF will further deteriorate the stability of the system. Finally, the correctness of the theoretical analyses is validated by time domain simulations in MATLAB/Simulink.