Miracle-dz/UniFAR-FTUs huggingface dataset:
Small-Dimension Feature Matrix Construction Method for Decoding Repetitive Finger Movements From Electroencephalogram Signals

Brain computer interface (BCI) has been widely studied to allow people to control external devices as an extension of capabilities or a replacement of lost functions. The decoding algorithm of brain signals is a crucial part in BCI, since its performance determines the efficiency of the interface. Decoding performance can be improved by generating optimal feature matrix. The objective of this paper is to propose and implement a decoding algorithm with optimized small dimension feature matrix on identifying motor intention of finger movement using electroencephalogram (EEG) signals. An experiment was designed and conducted, in which EEG was acquired from 10 healthy subjects during the left or the right index finger movement. Event-related desynchronization (ERD) topography was analyzed during motor tasks. A degree feature extraction algorithm was proposed based on the graph theory together with Support Vector Machine (SVM) to classify two kinds of index finger movement, which takes three factors into consideration: frequency bands, amplitude and range of ERD. The results showed that the algorithm can classify the finger movement effectively for 7 subjects based on a three-dimension optimized feature matrix, consisting of the maximum degree, average degree and clustering range. The proposed algorithm is not limited by the size of samples and can indicate the source area of the neural activities. Results also demonstrate that the proposed degree feature extraction algorithm can smooth signal noise and enlarge the feature differences between the contralateral and the ipsilateral hemispheres.
Comparison of Naive Bayes and K-NN Method on Tuition Fee Payment Overdue Prediction

Attending basic education is an obligation for all Indonesian citizens. The financial cost is one of the input components to implement an education or even can be considered as the main requirement in achieving the goal of education. For a private education institution in Indonesia, the financial cost is mainly covered by students’ tuition payments. SMK Al-Islam Surakarta is a private school that manages all its students to pay school tuition fees monthly. According to its last year’s administrative report, the number of students who are late in paying school tuition fee is around 60%. Since the school’s operational costs are heavily depended on their income from tuition fees, this considered an essential problem and has to be managed and predicted as well. This research will discuss techniques in predicting the late payment of tuition fees. From many popular methods available in this area, we observed two of them namely Naive Bayes and K-Nearest Neighbor (K-NN). This study will compare the accuracy between those two methods. The data used for the lab work is the official education basic data of Al-Islam Surakarta Vocational School in 2017/2018 totaling 236 data. To increase its accuracy, this study also combines the prediction methods with feature selection technique Information Gain which is commonly used to select an optimal parameter for the prediction process. In the end, the system is tested using the Confusion Matrix method. The results showed that the Naive Bayes Method with Information Gain attribute selection produced the highest accuracy of 69%.
A Modified MOEAD with an Adaptive Weight Adjustment Strategy

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is widely used to dispose multiobjective optimization problems (MOPs). The performance of MOEA/D is not ideal when deals with MOPs which are with degenerated curve. Aiming to this weakness, an adaptive weight adjustment strategy named BPO is proposed in this paper. In order to solve this problem, BPO strategy is based on the number of reference points referencing the archive. The results of tests show that MOEA/D with BPO strategy can obtain ideal performance. MOEA/D with BPO strategy can improves the weakness of MOEA/D effectively on MOPs which are with degenerated curve.
Compressing Convolutional Neural Networks by L0 Regularization

Convolutional Neural Networks have recently taken over the field of image processing, because they can handle complex non algorithmic problems with state-of-the-art results, based on precision and inference times. However, there are many environments (e.g. cell phones, IoT, embedded systems, etc.) and use-cases (e.g. pedestrian detection in autonomous driving assistant systems), where the hard real-time requirements can only be satisfied by efficient computational resource utilization. The general trend is training larger and more complex networks in order to achieve better accuracies and forcing these networks to be redundant (in order to increase their generalization ability). However, this produces networks that cannot be used in such scenarios. Pruning methods try to solve this problem by reducing the size of the trained neural networks. These methods eliminate the redundant computations after the training, which usually cause high drop in the accuracy. In this paper, we propose new regularization techniques, which induce the sparsity of the parameters during the training and in this way, the network can be efficiently pruned. From this viewpoint, we analyse and compare the effect of minimizing different norms of the weights (L1, L0) one by one and for groups of them (for kernels and channels). L1 regularization can be optimized by Gradient Descent, but this is not true for L0. The paper proposes a combination of Proximal Gradient Descent optimization and RMSProp method to solve the resulting optimization problem. Our results demonstrate that the proposed L0 minimization-based regularization methods outperform the L1 based ones, both in terms of sparsity of the resulting weight-matrices and the accuracy of the pruned network. Additionally, we demonstrate that the accuracy of deep neural networks can also be increased using the proposed sparsifying regularizations.
Optimal Control for Deployment of Charged Spacecraft Formation Flying Based LQR

The properties of coulomb force (CF) that nearly propellantless and exhaust plume contaminatless compared to the conventional chemical liquid propulsion thrusters (e.g., micro PPT, FEEP…etc.), make it attractive use for the close formation flying of the spacecraft at high Earth orbit (HEO). This paper presents the charged feedback controller using the optimal control based on the Linear Quadratic Regulator (LQR) method. The interaction between the CF that generated from charged sphere devices on the Target and Chase crafts are exploited to deploy the Chaser from the Target. Finally, the simulation results showed the effectiveness coulomb feedback-based LQR controller can be efforted the initial position of the Chaser entry to the design orbit as expected. The command force expenditure is on the order of -9 to 48 milli-Newton, in which the magnitude of coulomb charged has bounded.
Design and Testing of Autonomous Steering System Implemented on a Toyota Ha:mo

Recent advancements in the field of communication, control and embedded systems have triggered researchers to develop intelligent transportation system which can be ubiquitous, smart and autonomous. This paper presents the implementation of an autonomous driving car system for an ultra-small electric vehicle, branded as Toyota Ha:mo. The main objective of this work is to maximize the utilization of these available electric vehicles on campus by autonomously moving them from underutilized areas to an area where they are in high demand. Our indigenously developed system includes an Arduino based motor control system for steering, a camera and image processing subsystem for a lane detection and road sense intelligence and a global position system (GPS) module for outdoor localization. For experimental purpose, the autonomous system was installed on an electric vehicle. The results show that the autonomous electric vehicle passes the autonomous steering test for the required turning radius and achieves almost every benchmark set by Toyota. Therefore, our work provides a promising foundation for efficient utilization of resources in a campus-wide transportation system and opening new horizons for further enhancements and improvements.
Exploring the Parametric Energy Transfer in a Multi-Mode Piezoelectric Resonator with Nonlinear Harmonics

In this work, we investigate the parametric energy transfer phenomenon in a multi-mode nonlinear piezoelectric resonator over a wide frequency span through a simple, all-electrically interfaced setup. We propose a 4-port PZT-based parametric resonator with two lowest modes around 170 kHz ($f_{o}$) and 340 kHz ($2 f_{o}$) designated for energy receiving and pumping, respectively. The mere $2 f_{o}$ electrical excitation for the energy transfer into other tones is unique and more efficient as compared to the conventional nonlinear operation. By modulating the piezoelectric stress at 340 kHz with actuating signal of CMOS compatible threshold ($v_{ac} > 500\ \mathrm{mV}$), the energy up- and downconversion are recorded in real time by a spectrum analyzer (SA). The wide modulation bandwidths (MBW) of 25 kHz (7.3%) and 30 kHz (8.8%) at $2 f_{o}$ are observed with $v_{ac}$ of 500 mV and 1 V, respectively, where the signal bandwidth of transferred energy at $f_{o}$ is half of the MBW. Furthermore, the mechanical displacements of the resonator and the modes are also verified by a Laser Doppler Vibrometer (LDV) from DC to 800 kHz, showing a displacement amplification factor of 50X at $f_{o}$ as parametric effect is triggered. Compared to the conventional nonlinear harmonics operation under the same input signal (500 mV), the output signal of each mode has been significantly enhanced due to the proposed parametric excitation.
3D Pocket-Shape Dermis-Equivalent as a Skin Material for a Robotic Finger

Skin-equivalent, the in vitro human skin model consisting of dermal and epidermal layers, has the potential to be the human-like and self-healing cover for robots. In this paper, we report a method to fabricate a 3D pocket-shape dermis-equivalent, the main component of the skin-equivalent, and practically cover a robotic finger with it. To fabricate the robotic finger wearing this dermis-equivalent, we designed a small wire-driven robot and covered it with the pocket-shape dermis-equivalent using the proposed method. The robotic finger was able to repeat the alternative bending/unbending motion without splitting the dermis-equivalent. This pocket-shape dermis-equivalent can be applied for the human-like and self-renewable cover for robots.
A Systematic Literature Review on Composition of Microservices through the Use of Semantic Annotations: Solutions and Techniques

The aim of semantic web is to provide meaning to data shared by a network, therefore making it readable by humans and computer applications. The objective of this is to give the information and services a well-defined meaning by tagging web content with machine processable descriptions. Within the semantic web there are annotations, which relate data and classes offering a structured data. In addition, an ontology is an essential part of the semantic web, because it provides a vocabulary of terms and includes properties between concepts. There are several proposals over semantic annotations included in primary studies, but it is necessary to condense them in a secondary study, hence this paper presents a systematic literature review about the composition of microservices through the use of semantic annotations. This allows developers to determine which techniques are the most used in the semantic web. In order to perform this secondary study, the Kitchenham’s methodology was applied. After its application, 68 articles were firstly obtained, once the inclusion and exclusion criteria was applied, only 39 studies were selected, which include automatic and manual review.
Self-localization and positioning vehicle navigation system based on computer vision and PID control

In this work a control system is presented for the autonomous navigation of vehicles using a monocular vision system to detect reference patterns in a controlled environment. This control system is based on the data extraction from the vision system camera feed to generate position vectors, which enable the localization of the vehicle in the environment, without having to depend purely on odometry avoiding the error that it generates. The infrastructure developed for this paper is a low cost 4-wheeled vehicle with a minimal quantity of sensors and a VGA camera for the computer vision system, all components commercially available.
Intelligent Controller based Host-to-Host Congestion Control: Fuzzy Logic Controller

This paper employs an intelligent control technique (Fuzzy logic) in the area of Internet congestion control. Generally, congestion problem occurs when traffic load increases. This problem decreases network utilization and increases traffic delay. Hence, an intelligent congestion control is required to avoid the mentioned problem. Nevertheless, the main challenge in utilizing fuzzy controller is how to choice the number of membership functions. A greater resolution is achieved with a large number of membership functions. In this paper, two different cases as considered: the first case employs 3-memberships while the second case employs 5-memberships. The proposed controller is evaluated by using NS2 simulation tool and the results are compared with the most recent protocols. The results indicate that the proposed controller increases the throughput and decreases both the packet loss and packet delay.
Adaptive On-Line Self-Calibration of Errors in Multi-Stage-Noise-Shaping (MASH) Sigma-Delta Modulator Gyroscope

This paper introduces a novel adaptive self-calibrated multistage-noise-shaping (MASH-like) electro mechanical sigma delta modulator sensing scheme for MEMS Coriolis gyroscope. The proposed system adopts an online adaptive MASH2-0 modulator for a MEMS tuning-fork gyroscope which without using any additional mechanical calibration method. It effectively removes quantization errors that leaks through at the output due to parameters mismatching between gyroscope sensing element imperfections and interface digital modulator. It also allows the correction of spectral distortion and gain mismatch in the signal path due to non-ideal signal transfer function. Results demonstrate that the proposed adaptive MASH2-0 gyroscope achieved an inherent system stability, better dynamic range and better noise performance when compared with a common 4th-order single loop $\text{EM}-\Sigma\Delta\mathrm{M}$ gyroscope.
Highly Sensitive Surface Plasmon Resonance Based Photonic Crystal Fiber Refractive Index Sensor

This paper represents a highly sensitive octagonal lattice photonic crystal fiber (PCF) for refractive index (RI) sensing. Gold is externally coated to support surface plasmons which is chemically stable. The numerical investigations are based on finite element method which show that the maximum wavelength and amplitude sensitivity of the proposed sensor is 28,000 nm/RIU and 1962.63 RIU−1 respectively in x polarization for a wide sensing range from 1.35 to 1.44. Besides, the maximum confinement loss of 0.28617 dB/cm as well as the highest refractive index resolution of 3.57×10−6 RIU in x polarization are gained for this biosensor. This suggested design can acts as a favourable device for detecting any unknown biochemical and biomolecule analytes as it provides high sensitivity, great sensing resolution and low loss.
“Slower is Faster” by Considering of Give-way Evacuation Behavior

Emergencies such as building fire accidents, terrorist attacks or earthquake may result in severe crowd disasters. Hence, to efficiently evacuate crowd from these life-threatening circumstances has always been of great concern to safety experts. The “Faster is Slower” effect has been identified as every individual from crowds try to run through the exit fast, which will unavoidably exhibit competitive behavior during the evacuation. As a result, temporal clogging caused by these “faster” evacuees will hinder the overall evacuation process. However, it is unclear that whether the “slower” individuals who tend to give way to others will facilitate the evacuation. This paper aims to investigate the give-way behavior of evacuees by modifying the well-approved Social force model in a room evacuation scenario. We compare the evacuation performance of our developed give-way individuals with the evacuees developed in the original Social force model. The simulation results show that give-way evacuees are able to speed up the overall process of evacuation nearly 90 seconds when the exit is narrow (i.e. 0.6m). Nevertheless, as the exit width increases, the advantage of give-way behavior gradually diminishes. When the exit reaches 1m wide, longer evacuation time are required by give-way evacuees. Our work suggests that the give-way behavior of evacuees could facilitate the overall evacuation and generate the “slower is faster” effect when the exit is narrow.
Model-Based Fault Detection in DC Microgrids

Recently DC Microgrids (DC-MGs) are more attractive and effective in renewable energy resources (RERs). In this paper, for the protection of devices and detecting parametric faults, two model-based fault detection strategies are presented for DC-MGs. The proposed strategies include a fault detection filter (FDF), named H_/H∞ based filter, and Kalman based filter. H_/H∞ based filter can minimize disturbance effects and maximize the fault effects on the so-called residual signal. The results of H_/H∞ based filter are compared with the Kalman based filter. Results show that the H_/H∞ based filter is more effective for detection of parametric faults in DC-MGs.
A Novel Analysis Approach for Dual-Frequency Parallel Transmission-Line Transformer With Complex Terminal Loads

In this paper, a parallel transmission-line transformer with complex terminal loads is introduced for dual-frequency design. Through analyzing the different cases of complex terminal loads conditions, there are four different mapping patterns of designable regions on the Electrical Length-Electrical Length (EL-EL) plane. By adding an extra vertical Frequency Ratio (FR) axis, a novel 3D cube can be created on the FR-EL-EL coordinate system, then the designable ranges of frequency ratio can be easily drawn in this 3D cube. Therefore, the visible 3D mapping approach is newly reported for matching the complex terminal load of parallel transmission-line transformer at two different frequencies. For the certain pattern, a clearly boundary could be drawn to distinguish the designable and undesignable ranges. For verification, two examples are designed, fabricated and measured. Simulated and measured results are matched very well.
HDA Video Transmission Scheme for DASH

In recent years, with the development of Internet communication technology, more and more people tend to get information through the network. And people prefer multimedia video streaming services. Traditional streaming media technology cannot meet people’s needs because of its limitations. Recently, HTTP-based Dynamic Adaptive Streaming Over HTTP (DASH) has emerged as a new approach. In this paper, we consider two hybrid digital and analog video transmission schemes for DASH and propose three methods of enhancing the video quality of experience (QoE) of DASH users. First of all, considering the importance of data to video quality, we propose an energy-aware recombination method of reorganizing and packaging data and define the priority of the generated DASH layer in transmission. Second, we design an adaptive bitrate allocation algorithm to improve bandwidth utilization and video quality. Finally, we propose a retransmission mechanism to address the situation in which the digital stream at the receiving end cannot be decoded due to channel distance errors. Experiments show that our two schemes are superior to traditional DASH schemes.
Design of High Voltage Output for CMOS Voltage Rectifier for Energy Harvesting Design

This paper presents a modified design of CMOS differential voltage multiplier circuit block for energy harvesting circuit for wireless sensor networks (WSN) application. The design simulation and layout was carried out using 65nm CMOS process. The extraction of high DC voltage from rectifier block is always a severe bottleneck for energy harvesting. In this work, a simple mechanism to eliminate (Vth) of the MOS transistor by adding an auxiliary PMOS transistor is proposed. Also, an additional two capacitor (Cs) is split and connected to the differential output. Moreover, the conventional and modified voltage multiplier was simulated and implemented with three stages with a load capacitance of 100pF. The simulation result shows that the modified voltage multiplier obtain a higher voltage conversion ratio (Gv) of 3.96, while the conventional voltage multiplier only obtained a Gv of 2.96. Accordingly, the proposed modified rectifier circuit achieved a peak efficiency of 22.41 % and can able to operate a device with a power requirement of 1.2V to 1.8V and with a continuous output current of 3mA.
Everything must change? Challenges for animated storytelling in VR

VR enables the viewer to freely choose the point of view: Instead of being bound to a predefined screen format, he/she “becomes” the camera. This poses a huge challenge for communicating a narrative, because essential storytelling tools like framing and editing are not available to the filmmaker anymore, or must be radically rethought. On the basis of the author's current animation project ShakesVRare (a collaboration with the Shakespeare Institute in Stratford-upon-Avon), the essay at hand explains the reasons and challenges for creating a linear narrative in VR and demonstrates possible solutions.
Compendious elucidation on faults management in wireless sensor networks (wsn)

Wireless Sensor Networks (WSN) becomes a point of intrest in technologies that is being utilized in several modules. The WSN have been deployed in the unattended environment, hostile and harsh for a long interval of time. By doing the good quality of service, a great challenge has been created. That introduced the faults, communication failure and sensor failures or even in a change in topologies as well. This study focuses on the various faults that can cause the network failure or may become the reason for packet loss. Numerous reliability issues arise when packet loss situation occurs in the network. Moreover, it leads to the reduction of the network performance and decrement inefficiency of the network. To tackle such kinds of faults, one should apply different fault management techniques so as to ensure reliability and increase performance as well as reliability. In this study, we reviewed several aspects related to the fault management of WSN.
Sequence Directed Hybrid Fuzzing

Existing directed grey-box fuzzers are effective compared with coverage-based fuzzers. However, they fail to achieve a balance between effectiveness and efficiency, and it is difficult to cover complex paths due to random mutation. To mitigate the issue, we propose a novel approach, sequence directed hybrid fuzzing (SDHF), which leverages a sequence-directed strategy and concolic execution technique to enhance the effectiveness of fuzzing. Given a set of target statement sequences of a program, SDHF aims to generate inputs that can reach the statements in each sequence in order and trigger potential bugs in the program. We implement the proposed approach in a tool called Berry and evaluate its capability on crash reproduction, true positive verification, and vulnerability detection. Experimental results demonstrate that Berry outperforms four state-of-the-art fuzzers, including directed fuzzers BugRedux, AFLGo and Lolly, and undirected hybrid fuzzer QSYM. Moreover, Berry found 7 new vulnerabilities in real-world programs such as UPX and GNU Libextractor, and 3 new CVEs were assigned.