Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design for Distributed Secondary Voltage Control in Islanded Microgrids: Communication Topology and Controller

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study.
PPG and EMG Based Emotion Recognition using Convolutional Neural Network

Emotion recognition is an essential part of human computer interaction and there are many sources for emotion recognition. In this study, physiological signals, especially electromyogram (EMG) and photoplethysmogram (PPG) are used to detect the emotion. To classify emotions in more detail, the existing method of modeling emotion which represents the emotion as valence and arousal is subdivided by four levels. Convolutional Neural network (CNN) is adopted for feature extraction and emotion classification. We measure the EMG and PPG signals from 30 subjects using selected 32 videos. Our method is evaluated by what we acquired from
Pricing Personal Data Based on Data Provenance

Data have become an important asset. Mining the value contained in personal data, making personal data an exchangeable commodity, has become a hot spot of industry research. Then, how to price personal data reasonably becomes a problem we have to face. Based on previous research on data provenance, this paper proposes a novel minimum provenance pricing method, which is to price the minimum source tuple set that contributes to the query. Our pricing model first sets prices for source tuples according to their importance and then makes query pricing based on data provenance, which considers both the importance of the data itself and the relationships between the data. We design an exact algorithm that can calculate the exact price of a query in exponential complexity. Furthermore, we design an easy approximate algorithm, which can calculate the approximate price of the query in polynomial time. We instantiated our model with a select-joint query and a complex query and extensively evaluated its performances on two practical datasets. The experimental results show that our pricing model is feasible.
Simultaneous Reconstruction and Denoising of Spectral CT Images

Spectral CT based on photon-counting detectors offers the ability of obtaining CT images for different energy bins with a single X-ray excitation. The signal-to-noise ratio (SNR) of the reconstructed CT images at an energy bin is however drastically decreased. We present a study of different reconstruction methods that integrate explicit denoising in the reconstruction. The spectral CT reconstruction is then formulated as an optimization problem, in which four popular denoising techniques, including guided filter, wavelet transform, nonlocal means, and block-matching and 3D filtering, are investigated and the split Bregman algorithm is utilized to accelerate convergence speed. The so-obtained simultaneous reconstruction and denoising methods are evaluated in terms of root mean square error (RMSE), SNR and material decomposition performance. The results show that with the mechanism of simultaneous reconstruction and denoising, the images reconstructed from spectral CT have smaller RMSEs and higher SNRs while giving better performance in material decomposition.
Evaluation of DC-Link Voltage Switching Ripple in Multiphase PWM Voltage Source Inverters

This article presents a generalized approach toward the dc-link voltage switching ripple analysis in the two-level multiphase pulsewidth modulation (PWM) voltage source inverters with a balanced load. Since the voltage ripple is one of the crucial sizing criteria for a dc-link capacitor, a simple and practical equation for designing the dc-link capacitor, based on the maximum (peak-to-peak) value of the dc-link voltage ripple, has been proposed for the multiphase inverters. The amplitude of the dc-link voltage switching ripple is analytically derived as a function of operating conditions. The effect of the number of phases on the dc-link capacitor size is investigated as well. It is found that considering the same total output current, the dc-link capacitor size is reduced by increasing the number of phases up to seven. However, from the point of view of the dc-link capacitor size, there are no benefits of further increasing the number of phases. Reference is made to two commonly used modulation strategies—sinusoidal PWM and continuous symmetric centered PWM (i.e., space vector). The mathematical models are derived with an aim to provide the precise dc-link capacitor sizing and hence improve the power density of the whole system. The comparison of different phase numbers has been made. Proposed theoretical developments are verified by the simulation and experimental tests.
Iteratively Refined Feasibility Checks in Robotic Assembly Sequence Planning

Due to shorter product life cycles and increasing customization, production lines must be able to quickly adapt to novel product variants. This requires the automatic generation of assembly sequence plans from product specifications, as manual engineering of plans is slow and labor intensive. The main challenge in assembly planning is that the search for a valid plan must take the capabilities of the robotic system that will execute the plan into account. But checking the feasibility of executing a plan requires a simulation of the system, which slows down the search for a valid and executable plan. We therefore propose and implement two ideas to reduce search times. First, we iteratively refine the feasibility check of an assembly plan from levels taking only parts into account (which is fast) to high-fidelity levels including motion planning and full robotic simulations (which is high fidelity, but slow). Slower levels are only checked if faster levels succeed. The second is that errors in these levels are propagated upwards as symbolic rules that prune the search tree. We demonstrate how our contributions reduce the need for high-fidelity simulations on a two-armed robotic system that assembles product variants out of aluminum profiles.
A Multiband Rectenna for Self-sustainable Devices

This paper presents the design of a multi-band cross-polarized dipole antenna for use in energy harvesting. The antenna is designed to work from 1.53 GHz to 2.47 GHz in its lower lower frequency band and from 4.90 GHz to 5.63 GHz in its upper frequency band, covering the GSM1800/1900, UMTS2100, 2.4G, and 5G Wi-Fi bands. The design and simulation are covered which show that the antenna is cross-polarised with omni-directional radiation patterns.
Passive Optical Networks: Review and Road Ahead

This paper presents a realistic description on various Passive optical network (PON) technologies. A brief comparison between salient features of different PON technologies is also discussed. The paper elucidates the crucial driving factors which envisage innovation of next generation PON technology. To enable the new brand era of high quality triple-play services, many new techniques and procedures are embraced cognizant of the fact that backward compatibility with existing technology is the exigency of industry demand. Thus a common element which can support different types of technology on a same network architecture is discussed in this paper. The paper sheds light on some viable future technologies like 100G EPON, OCDMA-WDM PON and IFDMA-WDM PON and analyses their advantages over existing Next generation technology.
Automated Indoor Drone Flight with Collision Prevention

This paper details a system that provides positioning, obstacle detection and avoidance, pathfinding, and energy monitoring suitable for indoor operation designed for the Crazyflie 2.0 drone. The positioning subsystem was achieved by a hybrid of Received Signal Strength Indicators and Dead Reckoning. Obstacle detection and avoidance used an IR sensor to detect objects in the drone’s path, allowing the drone to look for alternate paths. Pathfinding used a modified Node Array A* algorithm implemented in a 3D model of the testing area within the Unity engine. Finally, energy monitoring used the drone’s built-in Python library to log voltage values and were sent to the Unity system, which reroutes the drone upon detecting a low battery voltage. The system was able to provide a basic autonomous navigation system that prioritizes safety of the drone and its flying environment.
TENSOR COMPRESSIVE HYPERSPECTRAL IMAGING BASED ON TOTAL VARIATION NORM MINIMIZATION

This paper focuses on tensor compressed sampling (TCS)-based hyperspectral imaging. In contrast to conventional compressed sampling (CS), which maps multidimensional signals into very long vectors, TCS keeps the structures of multidimensional signals and encodes them with different small sensing operators along different dimensions, remarkably reducing resource overhead. This paper proposes a new TCS reconstruction model for hyperspectral images, which exploits their spatial piecewise smoothness property. Furthermore, to solve this model, we develop a TCS inversion algorithm of low computational complexity as an extension of the split Bregman algorithm. Experimental results demonstrate that our method significantly improves recovery accuracy compared to the existing TCS reconstruction methods.
Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring

To achieve real-time electrocardiography (ECG) telemonitoring, one of the major obstacles to overcome is the scarce bandwidth. Compressed sensing (CS) has emerged as a promising technique to greatly compress the ECG signal with little computation. Furthermore, with edge-classification, the data rate can be reduced by transmitting abnormal ECG signals only. However, there are three main limitations: limited amount of labeled ECG data, tight battery constraint of edge devices and low response time requirement. Task-driven dictionary learning (TDDL) appears as an appropriate classifier to render low complexity and high generalization. Combining CS with TDDL directly (CA-N) will degrade classification and require higher complexity model. In this paper, we propose an eigenspace-aided compressed analysis (CA-E) integrating principal component analysis (PCA), CS and TDDL, sustaining not only light complexity but high performance under exiguous labeled ECG dataset. Simulation results show that CA-E reduces about 67% parameters, 76% training time, 87% inference time and has a smaller accuracy variance to the CA-N counterpart.
Comparison of monthly means daily UVA from CERES with ground-based measurements

The importance to monitor surface UVA solar radiation is mainly associated with its consequences over the human health as well as plants growth. High-quality ground-based measurements of UVA present better accuracy with respect to satellite retrievals. On the other hand, satellite data allows a better spatial coverage, something of great importance on areas with lack of ground-based monitoring points as in Argentina. Thus, the use of satellite products validated with ground-based measurements allows to geographically extending the analysis to regions without terrain instruments. The aim of this work is to evaluate the monthly mean of daily UVA product from CERES (CERES_SYN1deg) by mean of comparisons with ground-based measurements from UVA radiometers in four sites of the SAVER-net along Argentina for different periods, depending on the available ground-based dataset. The CERES monthly mean daily UVA presents very good correlation with respect to ground-based instruments for all sites with correlation coefficients higher than 0.98. The maximum mean bias error reaches ~21% in the worst case. Although it is needed longer ground-based datasets to validate satellite data, we can conclude that the monthly daily mean UVA from CERES presents good agreement with respect to ground-based measurements. Finally, monthly climatology was calculated and maps of these parameters were developed to analyse the seasonal variability along Argentina.
Towards a Security Cost Model for Cyber-Physical Systems

In times of Industry 4.0 and cyber-physical systems (CPS) providing security is one of the biggest challenges. A cyber attack launched at a CPS poses a huge threat, since a security incident may affect both the cyber and the physical world. Since CPS are very flexible systems, which are capable of adapting to environmental changes, it is important to keep an overview of the resulting costs of providing security. However, research regarding CPS currently focuses more on engineering secure systems and does not satisfactorily provide approaches for evaluating the resulting costs. This paper presents an interaction-based model for evaluating security costs in a CPS. Furthermore, the paper demonstrates in a use case driven study, how this approach could be used to model the resulting costs for guaranteeing security.
Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.
Analysis of Big Data Cloud Computing Environment on Healthcare Organizations by implementing Hadoop Clusters

Big data analytics is emerging as a very promising field for extracting relevant information from very large data sets and improving decision making with increased efficiency. The paper describes the big data analytics in healthcare, discusses its benefits and its associated components, and an improvised big data healthcare architecture is proposed which processes and analyzes data on large scale healthcare data on cloud computing environment with Hadoop clusters. Healthcare applications are being processed through cloud services rather than using traditional software systems.
A Novel Fully Automated CAD System for Left Ventricle Volume Estimation

Left ventricular (LV) volumes, and emptying and filling f unction r emain i mportant i ndices i n conditions such as heart failure. These parameters are derived from the volume curve contained by the inner border of the LV of the heart, throughout the emptying and filling p hases of the cardiac cycle, and the peak emptying and filling rates. The gold standard uses the Simpson rule to estimate volume from stacks of short axis images acquired using cine MRI. In this study, a deep learning, automated supervised approach to estimate ventricular volumes is introduced. Unlike prior methods that required hand-crafted image features to segment the inner contour, the proposed approach uses an automatically selected region of interest (ROI), and intelligently determines the optimum features directly from the ROI information. These derived features are then inputted into a deep learning regression model, with the estimated volume as the output results.
A Semi-Supervised Novel Recommendation Algorithm

Traditional text processing models have been studied with short texts, but there are few studies for long texts recommendation. Novels as the long texts have higher preprocessing dimensions, more semantic textual relationships and complex relationship of characters compared with short texts, it makes long text recommendation difficult. In order to address the novel recommendation issue, this paper proposes a semi-supervised novel recommendation algorithm with tag-topic model. In the paper, we build a tag list set, finds sample data containing test data tag elements in the sample set, and performs topic model training on the text content of the sample data to obtain the topic distribution vector. Combine the sample set and test set data, topic model is used to perform training on its text content to obtain the topic distribution vector. The cosine similarity calculation is performed on the topic distribution vector, and the recommended data list is obtained through Top5 calculation. The experimental results show that tag-topic model recommendation method not only can obtain novel recommendation results by long texts recommendation, but also helps solve the time-consuming problem based on tag recommendation, and effectively recommends interesting novels for readers.
Optimizing Android Facial Expressions Using Genetic Algorithms

Because the internal structure, degree of freedom, skin control position and range of the android face are different, it is very difficult to generate facial expressions by applying existing facial expression generation methods. In addition, facial expressions differ among robots because they are designed subjectively. To address these problems, we developed a system that can automatically generate robot facial expressions by combining an android, a recognizer capable of classifying facial expressions and a genetic algorithm. We have developed two types (older men and young women) of android face robots that can simulate human skin movements. We selected 16 control positions to generate the facial expressions of these robots. The expressions were generated by combining the displacements of 16 motors. A chromosome comprising 16 genes (motor displacements) was generated by applying real-coded genetic algorithms; subsequently, it was used to generate robot facial expressions. To determine the fitness of the generated facial expressions, expression intensity was evaluated through a facial expression recognizer. The proposed system was used to generate six facial expressions (angry, disgust, fear, happy, sad, surprised); the results confirmed that they were more appropriate than manually generated facial expressions.
Recommendation system of authorities and content based on Twitter for language therapy through data mining techniques

According to latest estimations of the World Health Organization (WHO), approximately 15% of persons have some disability. From this group, a significant percentage of persons present different kinds of communication and language disorders. Additionally, it is important mentioning that language supports other essential processes in children development, such as learning, skills to interact with his/her peers, and establishing relationships. On the other hand, nowadays exist several digital platforms like blogs, twitter, and in general, social networks, where experts and practitioners contribute with contents related to speech and language therapy. For these reasons, in this paper, we present a recommender system to support the content filtering, identify experts or interest groups related to communication disorders. Our recommender uses data mining techniques to perform the contents filtering, and a clustering approach to classifying the wich twitter contents are related to speech-language therapy area. In order to validate our proposal, we have conducted several tests with the support of a team of experts. The achieved results show that the recommendations of the system are useful, coherent, and understandable.
Resilient Communications and Information Systems for Disaster-Preparedness Using UAVs, Beacons and Data Aggregators, via Delay-Tolerant Networks on Sub-GHz Frequencies

We identified use cases where resilient communications systems may help in disaster-preparedness in the Philippines. The use cases involve critical information and communications. When networks are down due to typhoons, earthquakes and other disasters, information becomes critical. By introducing low cost and easy-to-deploy communications systems, we were able to demonstrate improved information flow in post-disaster operations. We were able to gather data seamlessly and test the range of transmission in different terrains. Using multiple UAVs, we were able to demonstrate cooperative missions between ground teams and rescue teams. The systems deployed by the team improved the flow of information without the use of legacy communications networks.
Parameter Estimation of an Electrochemical Li-ion Battery Model Using Innovative Global Harmony Search

The electrochemical model is one of the important model of battery, since it represents the real dynamics in the cell. The parameters of the model have physical meanings, so estimating the parameters, especially aging parameters might be helpful to efficient cell operation. There are many parameter estimation algorithms now on. Among the algorithms, harmony search is better than other algorithms like genetic algorithm, particle swarm optimization or gradient-descent methods, since harmony search has fast process characteristic which is suitable to electrochemical model. The 5 algorithms are applied to same parameter estimating problem to compare their performance in the aspect of objective values and parameter errors. The results show that innovative global harmony search is effective to estimate the better solutions with small parameter errors.
GiveCare: A Monitoring and Communication Smart Watch App with Pulse Sensor and Analysis for Caregivers Assisting Elderly and Persons with Speech Impairment

Nowadays, the use of Assistive Technology is only limited to hospitals, and in most cases, the technology cannot be integrated in homes due to its sophistication. In hospital environments, it is crucial that nurses and caregivers are keen of their patients’ needs and as much as possible, patients should be given prompt assistance. This is the reason why a monitoring and communication platform may be of help to the caregiver and their patients, elderly or with disability, particularly a speech-impaired person, in improving the way patients’ needs are attended to. This study primarily aims to provide a home-based system that would hasten communication between the patient and the caregiver. A smart watch will be utilized to facilitate a more convenient means of communication between the two.