Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Control for Three-Phase Unbalance Compensation under Three-Phase Four-Wire Power System

With the increasing unbalanced loads connect to the three-phase four-wire power system, three-phase unbalance caused by these loads will reduce the service life of electrical equipment and increase line loss. To solve these problems by reducing the three-phase unbalance, a three-level neutral point clamped compensator based on model predictive control strategy is proposed. Considering distorted point voltage of common coupling affects the performance of the compensator, fundamental positive-sequence voltage extraction is applied to eliminate the influence. Based on model predictive control strategy, a new method to track and compensate zero-sequence current is also proposed. Assumed zero-sequence output voltage of the inverter is used to predict the next period current. Compared with the values of the cost function generated by the 27 switching states, the optimal switching pattern that minimizes the cost function is selected for the next period control. The simulation results show the validity and accuracy of the strategy algorithm.
Turbine Location Wind Speed Forecast Using Convolutional Neural Network

Traditional wind speed forecast usually regards wind farm as a point to make forecast, but in a wind farm, wind speed of wind turbines in different geographical locations is not the same. For many wind turbines with wide geographical distribution in a wind farm, this paper gives a forecast method based on convolutional neural network (CNN) to forecast the wind speed at each wind turbine location. In this method, the wind speed and direction characteristics of all wind turbines at different geographical locations are input into the CNN network as variables, and local low-dimensional features of the original data are mapped to high-dimensional features through convolution operation of CNN, thereby realizing the wind speed forecast. The main advantage of this method is that by automatically studying the informative spatial correlation of wind speed, rather than artificial extracting , multi-task forecasts（MTF）are made and the wind speed forecast at different wind turbines locations is more informative and accurate.
HematoporphyrinÂ–mediated photosensitization of aflatoxin-producing Aspergillus species isolated from meat products -

Aspergillus spp. grow on foods and feeds generating aflatoxins, which is the most potent carcinogen, therefore the present study aimed to evaluate the deactivation of growth and aflatoxin production of A. flavus and A. parasiticus in the presences of hematoporphyrin dihydrochloride and LED light. One hundred samples of different meat products (hamburger, luncheon, sausage and pastrami) have been used for the study. The isolates were identified classically and molecularly by amplification of ITS regions. The aflatoxin regulatory gene (aflR gene) was amplified and a phylogenetic tree was employed to aflR gene and the AFLR protein sequences. Different concentrations of haematoporphyrin combined with visible light (530 nm) were tested against aflatoxin-producing Aspergillus isolates in vitro and in vivo, using a piece of hamburger. A total of nineteen isolates (15 A. flavus and 4 A. parasiticus) were recovered from the collected samples. Aflatoxin-B1 was detected in 6 isolates (4 A. flavus and 2 A. parasiticus), and aflR gene sequences was assigned in GenBank accession no. KY769955-KY769960. The phylogenetic analysis of aflR genes and the predicted AFLR protein sequences among the strains were greater than 94%. haematoporphyrin photosensitization completely inhibited the growth and aflatoxin production in vitro and in vivo of both A. flavus and A. parasiticus at a concentration of 250 µg/ml. The biochemical analysis of the contaminated hamburger piece showed a decrease in the protein, carbohydrates, and fat content. The results suggest that haematoporphyrinÂ–mediated photosensitization has a potential effect on the inhibition of the growth, spore formation and aflatoxin production of Aspergillus strains in meat products.
Studies on heterosis and inbreeding depression for yield and its attributes in greengram [Vigna radiata (L.) Wilczek]

Five crosses were evaluated to study the extent of heterosis and inbreeding depression for yield and its attributing traits in greengram. Heterosis over mid parent, better parent and standard variety was significant for pods/plant and seed yield/plant. The crosses exhibiting high heterosis also expressed high inbreeding depression for pods/plant, pods/cluster and seed yield/plant. Heterosis coupled with inbreeding depression revealed the predominance of non-additive gene action for most of the characters studied. Owing to its autogamous genetic architecture and biological constrains of the crop the heterosis could be exploited only by isolating the desirable segregants for yield attributes adopting bi-parental or recurrent selection in early segregating generations followed by single plant selection in subsequent generations.
Evaluation of performance and heart rate variability during intensive usage of a BCI-controlled hand exoskeleton

Brain-computer interfaces (BCI) in combination with assistive robotic devices, such as wearable robotics, has the potential of augmenting the capabilities of disabled people to carry out activities of daily living with success. To improve applicability of such systems, workload and stress should be reduced to a minimal level. In this paper, the degradation of the performance of EEG hand-exoskeleton control with the exhaustive use of the interfaces is analysed through the monitoring of user’s physiological reactions. Eleven BCI-naive volunteers participated in the study. The participants performed several open/close hand motor imagery trials for 6 minutes. After completing the task, both the NASA TLX questionnaire and self-assessment manikin (SAM) were submitted to the user. The results broadly suggest that there are significant differences (p-value<0.05) in heart rate variability (HRV) changes between subjects that showed good and poor performance using the BNCI. In addition, these objectives results are corroborate with the results of subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and Self-Assessment Manikin (SAM) respectively. Our main finding is that the subjects’ performance using a BCI-controlled hand exoskeleton produce physiological reactions in that subjects.
Secure Hybrid A/D Beamforming for Hardware-Efficient Large-Scale Multiple-Antenna SWIPT Systems

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users’ confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption.
Visual learning as Object Recognition to Recognize Image for Mental Disorder Children

Technology has improved a lot comparing to the previous years. The improvement can be seen from various development of a program that may benefit human life. One example of the advancement of technology is artificial intelligence (AI). AI has taken a considerable part in becoming the primary necessity in computer programming. One famous example of AI is object recognition. An arrangement of codes is established for the machine to recognize a picture that is inputted. Even so, the use of object recognition programs that may help mental disorder children is still low –- considering that they are still in the age where education is crucial. However, the standard approach of teaching them is not the right way to make them understand since they need individual adjustments and appropriate training for them to follow. Therefore. We are proposing an application that will help to educate mental disorder children through the use of object recognition in visual learning. The app will have built-in features such as vocalize the object’s name, which will help mental disorder children to know and remember how it is pronounced. Other features to further improve the education has guessed the word and draw the object which will help them to understand how the object is shaped. Moreover, Researches have proved that learning visually will help mental disorder children to understand better.
Resource Allocation for Cooperative Transmission in Optical Wireless Cellular Networks With Illumination Requirements

Visible light communication (VLC) often suffers from line-of-sight path blockages and high levels of inter-cell interference. Thus, the analysis and design of cooperation techniques become crucial to address these key impairments. This paper studies the performance of different resource allocation schemes that are suitable for multi-cell cooperative transmission when tri- and tetra-chromatic light-emitting diodes (LEDs) and optical orthogonal frequency-division multiple access are utilized. Firstly, guidelines are derived for maintaining the same spatial distribution of the signal-to-interference-plus-noise ratio (SINR) in every sector of the multi-cell environment in case of stand-alone (non-cooperative) and cooperative transmission. Secondly, the possible resource allocation configurations for both stand-alone and cooperative transmission modes are identified for different LED types and available orthogonal resources (i.e., frequency sub-bands per color and sectors per cell). Finally, the data rate gain of the multiple resource allocation configurations are also analyzed, while verifying the illumination constraints. The obtained results confirm that the proper design of cooperative transmission configurations will be of paramount importance to provide reliable wireless link in ultra-dense VLC deployments.
Patch-occupancy survey of elephant (Loxodonta africana) surrounding Livingstone, Zambia

Wild elephants represent the biggest human–wildlife conflict issue in Livingstone, Zambia. However, little is known about their movements. This survey investigated elephants’ habitat use outside a core protected and fenced zone that forms part of Mosi-oa-Tunya National Park, Zambia. Using ‘patch-occupancy’ methodology, indications of elephant presence (feeding behaviour, dung and tracks) were surveyed. The survey aimed to assist proposed future monitoring exercises by defining the geographical extent that should be considered to improve accuracy in species abundance estimates. Results were supplemented using collected indications of elephant presence from prior monitoring exercises, and during this survey. Elephant presence was confirmed up to 8 km from the boundary of the protected core habitat, focussed in: (1) an unfenced zone of the national park, (2) along a road leading from the national park to the Dambwa Forest to the north and (3) along two rivers located to the west (Sinde River) and east (Maramba River) of the core area. Detection probability of elephant presence was high using these methods, and we recommend regular sampling to determine changes in habitat use by elephants, as humans continue to modify land-use patterns. Conservation implications: Identification of elephant ranging behaviour up to 8 km outside of the Mosi-oa-Tunya National Park in southern Zambia will assist in managing human– elephant conflict in the area, as well as in assessing this seasonal population’s abundance.
Prediction of chronic Insomnia using Machine Learning Techniques

The world is changed extremely over the last decade by the power of technology. Consequently, human lives are undergoing multiple changes that have both positive and negative effects on human health. A lot of virtual involvements, lack of physical activity and extreme use of radio-wave devices are leading people into various health-related issues and Insomnia is one of them. The disorder is also known as sleeplessness. This can occur independently or can occur as a result of another problem. This may turn into permanent disease and chronic(long-time) insomnia can seriously damage a human brain. However, the presence of insomnia can be detected by different medical tests according to various internal factors of sleep. But this kind of approach is not only expensive but also time-consuming. Expensive tests and equipment are also not available in many developing countries. To bridge this gap we have decided to build an intelligent model based on a machine learning approach that is able to predict chronic insomnia. For acquiring best results 7 different machine learning classifiers were used where our Logistic regression model outperformed all of them. With an accuracy of 98%, our model can easily classify insomniac and normal people.
Control for gravity compensation in tendon-driven upper limb exosuits

Soft wearable robots, or exosuits, are a promising technology to assist the upper limb during daily life activities. So far, several exosuit concepts have been proposed, some of which were successfully tested in open-loop control. However, though simple and robust, open-loop control is cumbersome and unintuitive for use in daily life. Here, we closed the control loop on the human-robot interface of the Myoshirt. The Myoshirt is an upper limb exosuit that supports the shoulder joint during functional arm elevation. A direct force controller (DF) as well as an indirect force controller (IF) were implemented on the Myoshirt to assess their suitability for autonomously tracking human movement. In a preceding testbench analysis, a direct force controller with linear friction compensation (DFF) could be excluded, as linearly compensating friction aggravated the force tracking error in the ramp response (RMSE mean|sd: $32.75 \mid 10.95 \mathrm{N}$) in comparison to the DF controller ramp response $(27.61 \mid 9.38 \mathrm{N})$. In the same analysis, the IF controller showed substantially better tracking performance $(17.12 \mid 0.99 \mathrm{N})$. In the subsequent movement tracking analysis including five participants (one female), the position tracking error and smoothness (median(RMSE), median(SPARC)) were similar with the DF $\left(3.9^{\circ},-4.3\right)$ and IF $\left(3.4^{\circ},-4.1\right)$ controllers and in an unpowered condition $\left(3.7^{\circ},-4.2\right)$. However, the force tracking error and smoothness were substantially better when the IF controller $(3.4 \mathrm{N},-4.5)$ was active than with the DF controller $(10.4 \mathrm{N},-6.6)$. The magnitude response in the Bode analysis indicated that both controllers were obstructing the human movement at higher frequencies, however with 0.78 Hz, the IF controller satisfied the bandwidth requirement for daily life assistance, while the DF controller $(0.63 \mathrm{Hz})$ did not. It can be concluded that the IF controller is most suitable for assisting human movement in daily life with the Myoshirt.
Towards Ethical Requirements for Addictive Technology: The Case of Online Gambling

Today many interactive online platforms are equipped with immersive and attention-grabbing elements to increase user engagement and persuade more online presence, interaction and transactions. Excessive and obsessive use of technology combined with harm can be seen as a behavioral addiction. While technology companies started to introduce tools to mitigate addictive behavior, their principles, design process and success are questionable. Given the potential conflict between the revenue model and such tools, our work aims to define ethical requirements categories that act as a reference point for analysts and designers. We base our discussion on online gambling, which provides a clear example of addictive technology. Reno Model I–V was examined to discover main guidelines for enabling conscious online activity. As a result, we identified three main ethical goals: 1) creating an environment that supports informed choice, 2) monitoring player data to identify risk factors, and 3) introducing measures to tackle problematic online behaviour. We then refined these upper level goals into more concrete functionalities and metrics.
A Novel Joint Sum and Per Antenna Power Constraints Model based on a Comparative Approach of Optimal Transmission Strategies and Capacity of MIMO channels

To find an efficient way to retain maximum information from a multiple input multiple output (MIMO) system has always been a great challenge to the scientific community over the last two decades. This paper elaborately analyzes a literature focused on analyzing capacity of MIMO channels with multiple linear transmit covariance constraints while extensively investigating optimal transmit strategies for MIMO channels with joint sum power constraints (SPC) and per antenna power constraints (PAPC). The uniqueness of the notion relies on the fact that I have considered both the constraints on the same channel and an interesting merge is observed when our model discusses efficiency of the one researched on, a suggestion of a possible closed loop solution for it emerges. Our model developed a minimax duality for multiple access channels in order to overcome the fluctuations of alternating optimization, and implemented it alongside concave-convex procedure to find the saddle point. The changes in capacity on varying the number of receiving antennas is critically analyzed and convergence plot is compared as well, to recommend a practical implication on achieving the best fit.
Lane Departure Warning System using TDA3x_rvp Board

The man's desire to own luxury cars has always shown an increasing growth. From past 10 years, the number of vehicles that are sold in India have skyrocketed. This trend has a direct impact on the number of accidents that are occurring on Indian roads. Nearly, 1.35 million road traffic fatalities occur annually. In majority of the cases, accident occurs when one car comes into the path of the other which is caused due to the risk of driver losing his control over the car. This raises the need for advanced technologies to alert drivers of potential hazards and allow drivers to take good time taking control of the vehicle. One of the Advanced Driver Assistance Systems (ADAS), lane departure warning system (LDWS) helps drivers to avoid these tragedies. In this paper, the above mentioned system is developed using TDA3x_RVP board as this is a highly optimized and robust set of tools designed to meet automotive industry requirements. It is also conveniently flexible in the existing framework and can be expanded to incorporate other applications. The proposed approach detects the lanes by determining the number of left lane points and the right lane points based on certain rho and theta threshold parameters, and warns the driver in case of unintentional lane shifts that can be either left lane or right lane.
Research on Calculation Method of Transformer Magnetizing Current Based on Neural Network Fitting

Characteristics of transformer magnetizing current which is difficult to obtain can be used to assess transformer under DC bias effectively. Considering that the distortion of magnetizing current is directly affected by the DC current in the transformer neutral point, it is critical to research on fitting calculation method based on neural network which taking DC current as input and several characteristics of magnetizing current as output. Firstly, typical characteristics of magnetizing current that reflect distortion effectively are selected as output of the fitting calculation. Then, the neural network algorithm is used to fit the DC current and several typical characteristic of magnetizing current respectively, and the corresponding relationship between the DC current and the magnetizing current is analyzed. Finally, a transformer simulation model has been built to verify the effectiveness of this fitting calculation method which provides the calculation basis for evaluating the state assessment of transformer under DC bias.
Effects of a Soft Robotic Glove using a High Repetition Protocol in Chronic Stroke: A Pilot Study

Hand function rehabilitation is one of the most important aspects to improve the quality of life and independence for stroke survivors. Access to therapy is a key factor in one’s ability to perform rehabilitation exercises and recover motor function. The aim of this research is to show the viability of using a soft robotic glove to perform home-based rehabilitation of the hand function of stroke survivors. Soft wearable robotic devices are a promising approach to hand rehabilitation due to their lightweight, compliant, and low-cost design. The goal of this wearable technology is to reduce hyperexcitability of the flexor muscles through cyclic stretching of the fingers, active-assisted exercise, and task-oriented training. In this pilot study, four chronic stroke survivors were provided 300 repetitions of opening and closing their hand with the soft robotic glove over 6 training sessions. This high-intensity, high-repetition protocol was found to be safe and well-tolerated by all subjects. Results demonstrated average increases of 18.5° and 17.3° in the relaxed metacarpophalangeal and proximal interphalangeal joint angles, respectively, of the index finger and an average increase of 33 Newtons in grip strength. This work motivates further development of this home-based soft robotic rehabilitation device and lays the groundwork for a longer term study.
Software Features Prioritization based on Stakeholders’ Satisfaction/Dissatisfaction and Hesitation

In this paper we present a practical method that can be applied to support the prioritization of large sets of candidate software features in a requirements prioritization process. We consider as prioritization criteria the satisfaction/dissatisfaction of users from offering/not offering software features as part of an upcoming software release. There is often an asymmetry between users’ satisfaction and dissatisfaction when these two factors are considered as prioritization criteria of features: some features may generate satisfaction to users, if included in the next software release, but do not create the same value of dissatisfaction, if they are not included, and vice versa. This asymmetry may introduce additional hesitation and uncertainty to stakeholders when they adopt satisfaction/dissatisfaction as prioritization criteria. The suggested method initially requires from stakeholders to systematically rank all candidate features based on satisfaction and dissatisfaction criteria. Then, the method is used to quantify the hesitation of stakeholders that is inherent in each features ranking. The final features’ priorities are computed by calculating objective weights for all stakeholders’ rankings. The method assumes the larger the hesitation (lack of knowledge and indeterminacy) associated with each stakeholder ranking, the smaller will be the weight of that ranking in the calculation of the final features’ priorities.