Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Energy Solutions in Power Sector: Overview of the Challenges in Nigeria Perspective

In the last few decades, power sectors are known to be growing faster worldwide with changing climatic condition meanwhile sustainable development is needed to confront some of the challenges. Sustainable energy technologies are one of the numerous requirements in the context of sustainable development. Increasing human population density and urban size requires a corresponding increase in technology-oriented energy development. Smart energy systems are technology-oriented information and communication technologies used in different parts of the world for improvement in access to energy to improve energy generation, transmission and distribution processes. The application of smart systems for energy harvest is a paradigm shift in the current global power sector. A smart application encompasses identifying, sensing and processing competences to utilize system networks to communicate among energy stakeholders using different internet networking devices. Smart energy systems enable ambiance communication intelligence to achieve some objectives. In the current state of researches with regards to the application of smart energy systems, there are lots of conspicuous challenges, especially in developing countries. Therefore, in this paper, the synopsis of the challenges threatening the diffusion of smart grid technologies in Nigeria is presented.
Mobile Robot Localization Based on Optical Sensor

This paper presents a method of using optical sensor for mobile robot localization, which improves the accuracy of position estimation by using extended Kalman filter to fuse three kinds of sensor data. By installing the optical mouse sensor on the edge of the robot's central axis, the linear and angular speed of the robot can be calculated. These help to solve the problem of wheel-slip and the accumulation error of yaw angle that obtained by low-cost MEMS IMU (Inertial Measurement Unit). An extended Kalman filter method that fuse the data from wheel encoder, IMU and optical mouse sensor is implemented. Eventually, experiments of position estimation were carried out on our own two-wheel drive robot. By comparing the accuracy of fusion algorithm in many ways, it indicated that the location result that of three kinds of sensors is better than that of one or two sensors in traditional way, and the error of position estimation is generally within 0.6%.
Comparative Analysis of Energy Efficiency and Latency of Fog and Cloud Architectures

Today, we are witnessing the overpopulation of connected objects due to the ubiquitous computing or pervasive computing known as the Internet of Things. Recent developments in the field of industry have heightened the need for the Internet of Things. Therefore, the use of IoT implies the industrial internet of things. In fact, to be interested in its evolution, it is necessary to ensure the ease of its deployment as well as the gains of the industrial society on the economic level. The main challenges of IoT are: Speed computing, energy saver, bandwidth saver and providing low latency. However, these parameters have a serious effect on the use of the Internet of Things, which is necessary to find a solution to optimize its uses in the industry. This article seeks to address these issues by analyzing the IoT literature and its theoretical modeling of fog computing architecture and comparing its performance with the traditional cloud computing model. We suggest a global model and architecture, on which industrial companies can rely in order to optimize the internet of things resources and provide a better result.
A Reconfigurable Vivaldi Antenna with bandwidth control

This paper proposes a reconfigurable Vivaldi antenna with bandwidth control. The Vivaldi antenna is incorporated with two pairs of circular slot resonator for the purpose of switching. Pin diodes are used to switch ON/OFF the circular slot resonators one pair at a time. The bandwidth of the Vivaldi antenna can be controlled from a wideband (1. 08GHz to 3GHz) to two different narrower bandwidths at a fixed center frequency of 2. 6GHz. A DC biasing network is formed around the antenna to aid switching. The design is simulated in CST microwave studio using FR4 substrate and has been confirmed through fabrication and measurement in an anechoic chamber. This antenna is good for applications requiring bandwidth control such as Long Term Evolution (LTE).
A hot embossing robot for bonding microfluidic chips: design, control and test

The microfluidic technology proposed in recent years has been applied in many fields because of its advantages, such as integration, automation, miniaturization and low cost. Microfluidic chips, which are a realization way for the microfluidic technology, have become a research hotspot and their manufacturing process is studied thoroughly. Bonding is a key step that used for sealing a chip in the manufacturing process, and the bonding quality determines whether the chip can be used in practice. This study developed a hot embossing robot for bonding microfluidic chips: first, the mechanical structure of the robot was designed according to the physical characteristics of microfluidic chips and requirements of the hot embossing process; second, an intelligent temperature control method was established to predict and self-adaptively adjust the operating temperature; at last, multilevel experiments merging engineering and biology were designed and performed. The results showed the great and stabilized bonding effect, and proved the validity and practicability of this hot embossing robot.
JEDoDF: Judicial Event Discrimination Based on Deep Forest

With the rapid development of natural language and the implementation of the Wisdom Court, intelligent judicial assistants has become a new application of natural language processing in the judicial field. The text classification method based on word vector and deep neural network implements statistical classification of judicial documents, but it can not achieve the inherent logical interpretation of judicial cases. A method of extracting semantic logic tree from judicial case texts is proposed, and event tree can be interpreted by deep forest. Judicial documents are divided into several sub-tree fragments by sentence segmentation, and each sub-tree fragment is analyzed by dependency syntax to obtain core subject-predicate-object triples. TF-IDF algorithm is used to calculate the weights of triples, and get the core sub-event sequence, and use pruning algorithm to construct the max heap. The designed triple encoding algorithm realizes max heap vectorization of event tree, and embedded deep forest algorithm to realize classification discrimination of judicial text event tree. The experimental results show that the proposed event tree construction method combined with the deep forest algorithm can greatly improve the logical interpretation and accuracy of judicial text.
Probabilistic Computational Model for Correlated Wind Speed, Solar Irradiation, and Load Using Bayesian Network

This paper proposes a Bayesian network (BN) that can construct the nonlinear dependence among wind speed, solar irradiation, and load. The correlations of random variables (RVs) are analyzed using the Pearson correlation coefficient, Kendall rank correlation coefficient, and Spearman rank correlation coefficient. According to Bayesian theory, the Bayesian information criterion (BIC) and maximum likelihood estimation (MLE) methods are employed to determine the structure and parameters of a BN. Then the BN model of RVs is established. The constructed model is the joint probability distribution of RVs that can present the nonlinear dependence and marginal distribution (MD) of RVs without limitation. The testing samples of wind speed, solar irradiation, and load are generated using the autoregressive integrated moving average (ARMA) model. Then these samples are utilized to construct the probability model of a BN and C-vine copula, whose modeling accuracy and efficiency are compared, and the quality of their output synthetic samples are analyzed. In the modified IEEE 118-bus test system, two kinds of synthetic samples are used to calculate the probabilistic load flow (PLF), whose accuracy and efficiency based on the BN are tested. The validity of the BN model is verified.
BSENet: A Data-Driven Spatio-Temporal Representation Learning for Base Station Embedding

Base station (BS) plays a critical role in the wireless network. There has been some research on exploring the spatio-temporal information of BS in different fields. However, these works lack re-usability, which require the new researcher to re-do the work of representing the spatio-temporal information. To solve this problem, we propose a neural network model based on autoencoder and representation learning called BSENet to learn embedding of BS based on raw data. The embedding contains spatio-temporal information of BS. In this way, other fields that are related to BS can make use of spatio-temporal information with BSENet embeddings. Besides the spatial information, BSENet can maintain the independence of BS. Moreover, we use bi-directional LSTM to get temporal information and propose a time dropout method to improve the generalization ability. We propose a soft threshold to consider all spatial relations. In addition, we introduce weight to enhance compatibility. We treat the missing states as the inputs to deal with the missing values. The results of clustering show that BSENet embedding is better than other embeddings. In experiments of mobile traffic prediction, BSENet embedding helps a temporal model to improve the Mean Squared Error (MSE) from 0.01463 to 0.01334, which is similar to 0.01302 of a spatio-temporal model. At the same time, the training time increases a little but is still $5.0\times $ faster than spatio-temporal model.
Epilepsy Detection Using Artificial Neural Network and Grasshopper Optimization Algorithm (GOA)

Epilepsy affects about 1 % of the contemporary population and sternly reduces the wellbeing of its patients. It is a neurological disorder of the central nervous system that is usually characterized by sudden seizure. The possibility of detecting and predicting epileptic seizure has engrossed mankind already for over 35 years. One of the main tools in detecting and predicting the Epilepsy seizures are the Electroencephalograms (EEG), which record the brain activity by measuring the extracellular field potentials due to neuronal discharges. This EEG is quite difficult and complex to interpret even by an expert neurologist, even so, it is time-consuming, often challenging, sets in human error as well as delay in treatment. In this research, a hybrid classification model using Grasshopper Optimization Algorithm (GOA) and Artificial Neural Network (ANN) for automatic seizure detection in EEG is proposed called GOA-ANN approach. Nine parameters (mean value, variance value, Standard deviation value, energy value, entropy value and maximum value, RMS value, kurtosis and skewness) were extracted and used as the features to train the ANN classifiers. GOA was used for selecting the best features in order to obtain an effective EEG classification. In comparison with other research, the result was able to detect epilepsy and enhance the diagnosis of epilepsy with an accuracy of 98.4%. The research was also compared with Artificial Neural Network using Feed-Forward network, the result shows that GOA_ANN approach performed better.
A Vision-Based Path Planning and Following System for a Miniature Robotic Fish

Most existing path following algorithms for middle-or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation.
Translation Validation of Code Generation from the SIGNAL Data-Flow Language to Verilog

The SIGNAL is a high-level synchronous data-flowlanguage for the design and implementation of safety-criticalembedded systems. It provides a unified framework forspecification, modeling, formal analysis, and automatic codegeneration for different general-purpose languages like Java, C, and C++. However, fully implemented and verified open sourcetool for code generation from SIGNAL to Hardware DescriptionLanguage (HDL) is not available. This paper describes theformal verification of the generated Verilog code from theSIGNAL language. Proving the correctness of generated code isvery important when it is for safety-critical embedded systems. We use the translation validation technique for verifying thecorrectness of the generated code. In this approach, thePolychrony Toolset builds the models of source SIGNALprograms with its associated model checker SIGALI. The opensource tool Yosys generates models for target Verilog programsin the SMT-LIB standard format. We transform the modelgenerated by Yosys to the model accepted by the SIGALI modelchecker. Finally, we use the SIGALI model checker to validatethe translation by symbolic simulation between both source andtarget program models. The target program may have fewerbehaviors than the source program therefore if the model of thetarget program implies the model of the source program, itmeans the target program preserves the semantics of the sourceprogram, and the translation is correct.
An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.
Using Kalman Filter to Process Auditory Brainstem Responses in Subjects with Normal Hearing

Auditory brainstem response (ABR) audiometry is a widely used tool for detecting hearing loss. The ABR is generated within the first 10–15 ms in the brainstem as responses to external sound stimulations following the stimulus onset. However, due to the small amplitude of the ABR signal, a large number of repeated averaging trials are usually needed to obtain meaningful ABR waveform, leading to long time consumption in the clinical practices. The subjects need to be sedated in special cases to keep them still for the long recording time. In this study, the method of Kalman filter was used to adaptively separate noises from ABR signals so that less trials were necessary to improve the efficiency. The performance of the Kalman filter method was compared with the traditional averaging methods under different conditions. The results showed that Kalman filter could obtain reliable ABR waveforms by wisely filtering out noises with a reduced number of repeated trials as less as 500 times, and the morphology converged to a smooth and clean curve as the number of trials increased. The study showed that the Kalman filter might provide a useful tool to improve the efficiency and signal quality of the ABR measurements.
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

Rapid, non-destructive detection of wheat <italic>Fusarium</italic> head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (<inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.82$ </tex-math></inline-formula>, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (<inline-formula> <tex-math notation="LaTeX">$R^{2}= 0.69$ </tex-math></inline-formula>, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI’s ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB.
A Meta-Database System Approach for Business Data Sharing among Multiple Target Databases

Today, to share business data among multiple target database systems is a challenging problem, in particular, in cases of companies combinations, business integration and commercial cooperation. To solve this problem, this paper proposes a meta-database system approach to business data sharing. Our new methodology is to build a meta-database system to manage multiple target databases such that end-users can share business data by cross-search over the target databases but each target database can be managed in its own traditional way. Moreover, a new target database can be easily added into the system and an old one can also be easily deleted from the system. The major advantage of this approach is that it can provide the fastest means and lowest cost for business data sharing. Therefore, it is very effective for those business cases that require urgent data sharing. By providing a general-purpose package and related tools, this methodology can be easily applied in various areas. This is the first attempt to solve the business data sharing problem by using a meta-database system.
UG-Net for Robotic Grasping using Only Depth Image

In this work, we present a real-time, deep convolutional encoder-decoder neural network to realize open-loop robotic grasping using only depth image information. Our proposed U-Grasping fully convolutional neural network(UG-Net) predicts the quality and the pose of grasp in pixel-wise. Using only depth information to predict each pixel's grasp policy overcomes the limitation of sampling discrete grasp candidates which can take a lot of computation time. Our UG-Net improves the grasp quality comparing to other pixel-wise grasping learning methods, more robust grasping decision making within 27ms with 370MB parameters approximately (a light competitive version is also given). In the physical experiment, we achieve 93.08% and 93.23% grasp success rate on a 3D-printed adversarial object benchmark set and a household object benchmark set.
Accomplishing Robot Grasping Task Rapidly via Adversarial Training

This paper proposes a robotic imitation learning method which integrates the deterministic off-policy reinforcement learning and generative adversarial network. This method allows the robot to implement the grasping task rapidly by learning the reward function from the demonstration data. Firstly, the discriminator is used to learn the reward function from demonstrations, which can guide the generator to complete the robot grasping task. Secondly, the deep deterministic policy gradient method is used as the generator for learning action policy on the basis of discriminator. In particular, the demonstration data is also input into the generator to ensure its performance. Finally, three experiments on the Push and Pick-and-Place tasks are conducted in the GYM robotic environment. Results show that the learning speed of our method is much faster than the stochastic GAIL method, and it can effectively train from the demonstration data in different states of the task. The proposed method can complete the robot grasping task without environmental reward quickly and improve the stability of the training process.