Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Solar Based High Step-Up DC-DC Converter with Various Filters

This paper proposes a converter that employs a proper filter to the load. This particular design protects installers and users from electrical hazards. Exclusive of extreme duty ratios and the frequent turns-ratios of a coupled inductor, this converter achieves a high step-up voltage-conversion ratio; the leakage inductor energy of the coupled inductor is efficiently recycled to the load. These features explain the module's high-efficiency performance. The detailed operating principles and steady-state analyses of continuous, discontinuous, and boundary conduction modes are described.
Shape Memory Alloy Driven Soft Robot Design and Position Control Using Continuous Reinforcement Learning

In this paper, we introduce a new Shape Memory Alloy (SMA) driven biomimetic soft robot and developed its mathematical model of a spherical coordinate system. Aiming at the problem of the control of the soft robot, a new reinforcement learning (RL) based position control algorithm, Soft robot Position Control Deep Deterministic Policy Gradient (SPCDDPG), is proposed. Our approach enables the soft robot to learn the target reaching task by performing continuous actions without any prior knowledge of the configuration. Moreover, we developed a simulation system for learning the target reaching task and designed a reward function for the system. Finally, the experimental results demonstrate the effectiveness of the proposed method which is tested and validated through extensive numerical simulations under different conditions.
The application of image super-resolution reconstruction based on compressed sensing in the intelligent mobile terminal

The image super-resolution technology has a wide range of applications and development prospects due to hardware limitation and various kinds of degradations of the digital imaging system. However, it is a difficult problem to share and promote it based on the rapid development of web technology. In this paper, we proposed a scheme to apply the technology of image super-resolution in intelligent mobile terminal by sharing way. Firstly, the algorithm of single image super-resolution reconstruction based on Compressed Sensing is proposed and deployed to the Cloud Host in the server terminal. Then, the service background is built by using the thinkPHP framework and the site operation environment is constructed combined with the XAMPP toolkit, so the algorithm can be called and run by the script files of batch processing. Correspondingly, in intelligent mobile terminal, the preprocess image can be uploaded asynchronously to the Apache Server of the Cloud Host through designing the plugin by HTML5. In addition, the plugin of the Boostrap is used and the concept of Responsive Web Design is utilized subsequently, so the mobile terminal has good interactivity. Finally, image super-resolution can be done by interrupt request, and the result can be obtained dynamically in intelligent mobile terminal. Experimental results indicate that this scheme makes it possible to apply the latest theory in mobile terminal, which is feasible to solve the problem of poor quality of image in the field of engineering where the imaging process is not ideal.
Synergistic Union of Word Embedding and Knowledge Graph for Words Semantic Similarity Measure

This paper presents a method for measuring the semantic similarity between words. The previous work on words semantic similarity measure have focused on either knowledge based or word embedding based. However semantic similarity between words do not perform well within specific domains. Therefore in this paper we introduce a word semantic similarity measure namely KVEC that was created by the synergistic union of word embedding based and knowledge graph based semantic similarity methods. Through experiments performed on proverbial word similarity dataset, we show that KVEC semantic similarity method whose maximum correlation coefficient reached 0.818, which outperform both, knowledge based method and word embedding method training on corpus.
Mathematical Model of Weather Conditions Influence on Properties of Photovoltaic Installation and Method of its Identification

Interval discrete model of the real photovoltaic installation located at Gdynia Maritime University is proposed in the paper. This model takes into account the influence of weather condition parameters on the properties of applied photovoltaic panels. The model allows calculate the values of power generated by mentioned installation for selected seasons and during the day with guaranteed accuracy. The model is simple for adjustment because it is essentially a difference equation. Therefore, it requires the first discrete values of really generated power.
Feature Analysis and Optimisation for Computational Personality Recognition

Automatically classifying human personality traits through analysis of their social network behaviors is an important yet challenging task to date considering the low accuracy of current researches. In that detection of significant features is an essential part of a personality recognition system, this paper proposes an in-depth analysis of features that contributes to the recognition of a given trait. Besides the common features of social network used by most current researches, text style features and TF-IDF-based psychological features are proposed and prove to be effective to predict certain personality trait. Also particle swarm optimization (PSO) feature optimization algorithm has been adopted to select the best combination of features. Simulation results show that with the best combination of features, the F-measure value of the personality recognition has been improved around 12%.
Multi-user Grouping Based Scheduling Algorithm in Massive MIMO Uplink Networks

Massive Multiple-Input Multiple-Output (MIMO) system, in which base station (BS) is equipped with a large number of antennas, makes it possible to serve many users in the same time-frequency block by multi-user (MU) scheduling. However, it still faces a lot of challenges, especially the high computational complexity caused by the high dimension matrix operation for user selection. Grouping based scheduling algorithm is a good way to solve the high complexity problem, that is, all the users are divided into several groups and scheduled based on the grouping result. In this paper, we focus on the uplink MU scheduling in massive MIMO system and propose a new grouping based scheduling algorithm. In the grouping process, all the resource blocks (RBs) are divided into several segments, and then each user is grouped into a most suitable segment based on channel condition and the number of users in each group. In the scheduling process, each group schedules users separately on the basis of proportional fairness (PF) principle and correlation of channel between the scheduled users. Analysis and simulation results show that our algorithm presents a good performance with low complexity.
Analysis, Design, and Performance of Isolated Three-Port UPS Converter for High-Power Applications

In this paper, an uninterrupted power supply (UPS) and an isolated three-port bidirectional converter (TPBC) are developed by connecting a three-phase voltage source converter(VSC), a battery energy- storage system (BESS) and a grid-connected boost converter-fed three-phase DC/AC converter through a single three-winding isolation transformer(Tr). This design presents features that are suitable for multiple voltage electrical systems. The three-port power exchange is multi-directional which can be delivered either separately or simultaneously. In consideration of the safety requirements, electrical isolation is achieved between any of the two ports. Besides the power-flow concept, different operating principle stages, power flow equations, small signal model, network simulation model, and control strategy are discussed. Furthermore, the steady-state analysis are evaluated to determine the power flow equations. The control strategy proposed is based on single-phase shift control, with the small signal model for optimized controller design, which can predict the accurate frequency response and obtain a fast dynamic response. Meanwhile, this can be used in many applications of electronic power converters. The simulation results have been provided under various operating conditions, which confirms the effectiveness of the proposed control strategy. The result showed that the power management feature of the system was successful and waveforms are obtained. Finally, theoretical considerations and simulation results for different operating stages are verified by MATLAB / SIMULINK
EpiSense: Towards a smart solution for epileptic patients’ care

Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable interventions; 3) alleviating the mental health issues associated with epilepsy, such as anxiety and depression; and 4) the lack of availability of large scale datasets related to epileptic patients with different profiles, needed to advance research in epilepsy. In this work, we propose EpiSense – a smart healthcare solution for epileptic patients’ care. EpiSense leverages sensory, mobile, and web technologies, as well as machine learning techniques for the real-time detection of epileptic seizures. As part of the system, a patient’s mobile app. is provided to allow the detection of seizures’ occurrence in real time and the sending of alarm notifications to care takers, for appropriate actions. Moreover, a web portal enables doctors to view the progress of their patients and get notified about seizures’ occurrence and statistics. The EpiSense system was designed and implemented, and three machine learning models were tested for real-time epileptic seizure detection. This work gives interesting insights about the possibility of using sensory technologies and data analytics for the improvement of epileptic patients’ care, and offers the possibility of personalized healthcare management.
Towards a Framework for Gamified Programming Education

Computer programming is a difficult subject that can only be mastered with lots of practice. It is therefore of primary importance to rise and retain students' engagement during a programming course, a task in which gamification has been proven as a competent method. Even though there are numerous reports on applying gamification to programming courses, there are no available open resources or dedicated platforms that could be used by programming teachers to gamify their courses, meeting both the requirements of being easy to adopt and leaving the decisions on the scope of the course and the level of gamification to the teachers themselves. In order to fulfill this gap, a consortium of four European institutions initiated a common project to develop open gamified programming exercises and interactive course materials for popular programming languages. In this paper, we report the results of the first stage of this work, which defined the range of gamification concepts to be covered within the developed framework and its evaluation by students.
An HDL-described Fully-synthesizable Sub-GHz IoT Transceiver with Ring Oscillator based Frequency Synthesizer and Digital Background EVM Calibration

This paper presents a RTL-described sub-GHz IoT transceiver(TRX) in 65nm CMOS with 3.3/4.6mW RX/TX power consumption. Most of the TRX is fully-synthesizable with foundry provided standard cells. The TRX has ring oscillator(RO) based a fractional-N injection-locked(IL-PLL) PLL, a digital frequency shift keying(FSK) transmitter, and a digital-intensive heterodyne receiver with all-digital intermediate frequency(IF) stage. To mitigate the adverse effects of layout uncertainty and process voltage temperature(PVT) variations, extensive digital background calibration is proposed. Enabled by the digital calibration, the RO-based injection-locked PLL achieves 0.8ps jitter and -61dBc reference spur, with 40MHz reference frequency and 1.1mW power consumption. The synthesizable transmitter achieves 0.9% error vector magnitude(EVM) and -41dB adjacent channel power ratio(ACPR) with 21.7% system efficiency at 0dBm output power. The digital intensive receiver achieves -94dBm sensitivity and 36dB adjacent channel rejection(ACR), with 3.3mW power consumption.
Sliding Mode Control of Grid-connected Inverters Using Inverter Output Current

In this paper, the switching command is produced by a sliding mode controller so that inverter output current follows the load current. To this end, an appropriate sliding surface for inverter output current is first defined, whereby control law is derived. In grid-connected mode of operation in microgrids, it is desirable that the microgrid meets its local load and does not receive power from the main grid as far as possible. Moreover, the voltage and frequency of the point of common coupling (PCC) are determined by the main grid having high inertia. Hence, in this paper the control strategy is focused on the inverter output current. Furthermore, the performance of the proposed sliding mode controller is compared with that of a PI controller. Also, hyperbolic tangent function is used instead of sign function in sliding mode control approach to cope with the chattering phenomenon. Finally, the results obtained via simulations performed in MATLAB/SIMULINK environment show the superior performance of proposed controller for a variety of load types such as linear and nonlinear load.
Automatic Vision Based Classification System Using DNN and SVM Classifiers

In this paper, we construct an automatic classification vision system that is designed to recognize Malaysian herbs that are typically used for medical or culinary purposes. The proposed system employs two classifiers, Support Vector machine (SVM) and Deep Neural Network (DNN). The two classifiers have been implemented using OpenCV-Python. For the training test SVM achieved 86.63% recognition accuracy and DNN (TensorFlow) achieved 98% recognition accuracy. For the real life testing SVM achieved 74.63% recognition accuracy and DNN achieved 93% recognition accuracy. In the proposed system a total of 1000 leaves were used. A total of 50 samples of herbs were collected for each class and they were divided into two datasets. The first dataset which consisted 60% of the herbs samples were used for the training purpose and the other dataset with 40% of the herbs samples were used for the testing purpose. The time taken for each recognition process was 4 seconds for SVM and 5 seconds for DNN classifier. Also, the proposed system is capable of identifying the herbs leaves even though they are wet, dried and deformed with a recognition accuracy of 52.50%. Finally, based on the experiments that were done, the system proved to be very efficient and accurate with the highest recognition rate being 98%. The results indicate that the techniques used in the proposed system are significantly efficient when compared to the various techniques employed in the existing literature.
Breaking nyquist limitations in reflectometry-based wire diagnosis systems by compressive sampling

Reflectometry is a wire diagnosis technique to detect and localize electrical defects in Network Under Tests efficiently. In order to achieve a very precise online diagnosis without altering the functioning of a Network Under Tests, reflectometry methods should use specific wideband Multi-Carrier signals, whose generation requires dedicated numerical tools. The basic architecture of any reflectometry system for the injection of the signal in the Network Under Tests requires appropriate Digital-to-Analog Converters. Even for the most demanding requirements in reflectometry, their specifications in terms of resolution and sampling rate can easily be found. Yet, measuring the reflected signal should be performed using Analog-to-Digital Converters capable of reaching high sampling frequencies together with sufficient resolution. Such converters are either extremely expensive or beyond the current state of the art. In that respect, this paper presents a new architectural approach for designing such reflectometry systems based on a Compressive Sampling method bypassing the Nyquist rate.
Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation

Aggregation is a vital behavior when performing complex tasks in most of the swarm systems, such as swarm robotics systems. In this paper, three new aggregation methods, namely the distance-angular, the distance-cosine, and the distance-Minkowski <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the cosine, angular, and Minkowski distance functions, which are used here to compute distances among robots’ neighbors. Relying on these methods, each robot identifies its <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-nearest neighborhood set that will interact with. Then, in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance compared to the conventional distance-weighted <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.
SOS: Save Our Social Network Accounts

Social media platforms such as Facebook and Twitter have become universally prevalent for connecting people, sharing information, or spreading messages and ideas. However, the social dominance of these platforms have also made them attractive targets for malicious intruders. Successful attacks against personal social media accounts can cause serious harm to their users, due to the possibility of revealing intimate private information. Discovering these hacking attempts can be done by detecting anomalous (out of the normal patterns) behavior over the user’s account. However, personal accounts show inconsistent behavioral patterns over time, making it highly challenging to extract definitive features for each user, and accordingly to detect anomalies. In this paper, we propose a novel methodology based on subjective logic and machine learning techniques to detect and extract the most highly defining features of each personal account. These features are then used to build a behavioral profile for each user, which can be used to detect anomalies. We conducted 2 studies, collected data from 47 Facebook users and 616 Twitter users. We extracted features from the collected data and assigned each feature a weight as a scale of importance according to the user’s deviation in this feature. We simulated 2,209 different attacks in the first study and 379,456 different attacks in the second study. Our results show a detection accuracy of 94.9% and 87.54% for the first and second studies, respectively.
Text Features Extraction based on TF-IDF Associating Semantic

The TF-IDF (term frequency-inverse document frequency) algorithm is based on word statistics for text feature extraction. Which considers only the expressions of words that are same in all texts, such as ASCLL, without considering that they could be represented by their synonyms. Separating words with the same or similar meanings will result in the loss of partial information when text feature were extracted. The representation of words needs to extract the similarity of words, and the similarity among words needs to be obtained by the meaning of words in texts. In order to improve the accuracy of text feature extraction, this paper uses the word2vec model to train the word vector in the corpus to obtain its semantic features. After excluding words with low TF-IDF value, the density clustering algorithm is used to cluster the remaining words according to word vector similarity. As a result, similar words are clustered together and can be represented to each other. Experiments show that using the TF-IDF algorithm again, constructing a VSM (vector space model) with these clusters as feature units can effectively improve the accuracy of text feature extraction.
Speech Enhancement Based on Cepstral Mapping and Deep Neural Networks

In this paper, we present three strategies to achieve speech enhancement, which is based on Cepstral Mapping and Deep Neural Networks (DNN). Firstly, we apply DNN to directly predict the clean speech Cepstral feature given noisy Cepstral input. Then, by waveform reconstruction, we can obtain desired clean speech. Comparing with the method of directly mapping log-power spectral (LPS), our method is able to be more effective to recover speech harmonic structure and gain the higher speech quality. Additionally, we also utilize DNN to estimate ideal Wiener filter by giving noisy Cepstral input. Finally, a fusion framework is proposed to acquire enhanced speech signal, which combines Cepstral feature mapping and Wiener filter. Experiments show that the proposed algorithms are able to achieve the state-of-the-art performance in improving the quality and intelligibility of noisy speech.
Feed-Forward Based Fuzzy Logic Controlled BESS for Enhancing the Frequency Stability of Islanded PEA-Microgrid in Thailand

With an advancement of the power system according to the economic growth, the frequency improvement of the microgrid remains a challenge. Hence, this paper proposes the Feed-Forward based Fuzzy Logic controlled (FFFL) BESS for enhancing the frequency of islanded Provincial Electricity Authority of Thailand (PEA) microgrid system, which consists of the residential and the Electric Vehicles (EVs) load centers, DGs and BESS. The proposed FFFL method is to compensate the current to both of the direct and the quadrature axis reference currents for utilizing in the BESS current controllers. The simulation results implemented on DIgSILENT PowerFactory software are completely demonstrated that the proposed method is able to enhance the frequency with an acceptable range of the PEA grid code requirement, when the microgrid is detached from the main grid, in comparison with the conventional BESS control methods. Moreover, from the simulation results with the proposed scheme correlated with the traditional BESS control technique, the number of loads being shed by the UnderFrequency (UF) relay is also significantly reduced with a superior performance.
Package Design Methodology for Crosstalk Mitigation between DC/DC Converter and ADC Analog Inputs in Complex SoC

Going beyond the traditional approach used in package design based on layout guidelines, in this paper a chip-package co-design methodology is developed to optimize the final design. Optimization is achieved thanks to the identification of the main coupling mechanisms and pointing out their exact location at package-level. Measurements carried out on a laminate ball grid array (BGA) proved the effectiveness of this methodology to identify and optimize the most severe crosstalk (mainly due to inductive coupling) between the gate drivers of the DC-DC converter and ADC analog inputs in a microcontroller for automotive applications.