Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation State of Charge (SOC) of Ultracapacitor Based On Classical Equivalent Circuit Using Extended Kalman Filter

The development of electronic devices with low power is increasingly being used in various fields of technology including energy storage. The energy storage system is a significant thing in a diverse range of industrial applications. As an emerging technology of energy storage, the ultracapacitor is a low power electronic device with high capacitance and durability than others. In this journal, the ultracapacitor becomes the main object of energy storage. Estimating the value of the SOC based on the classical equivalent circuit with the self-discharging load to monitor the percentage value when charge and discharge the ultracapacitor. The estimation state of charge of ultracapacitor using extended Kalman filter as one of the best methods in estimating. As a result, the response of the method compared with experimental data and has no big different value.
A Compact Low-Pass Filter with Simple Structure and Sharp Roll-Off

In this paper a compact low pass filter (LPF) with desirable figure of merit (FOM) and sharp roll-off is designed. The cut-off frequency of the designed filter is 3 GHz. The designed filter has a sharp transition-band of 0.88 GHz, from 3 to 3.88 GHz with corresponding attenuation levels of −3 and −60 dB, respectively. Also, the proposed filter has wide ultra stop-band from 3.8 GHz to 10 GHz with 20 dB suppression level. The dimensions of the proposed LPF is 0. 13λg × 0. 058λg, which shows excellent size reduction.
A Design of Anthropomorphic Hand based on Human Finger Anatomy

The main role of the prosthetic hand is to help people with the upper limb amputees carry out daily activities. The appearance of the prosthetic hand should also be as close as possible to the natural hand. The human hand is a very complex structure, and these complex structures have a profound effect on the movement of the hand. In order to develop a prosthetic hand with the same flexibility as a human hand, we designed the prosthetic hand according to hands anatomical features, and incorporating these structural features into design: prosthetic joints, ligaments and extensor hoods. To evaluate the performance of the proposed prosthetic hand in daily life, a grabbing experiment was conducted on common objects in our daily life. The results show that the proposed prosthetic hand has a high performance and natural appearance.
ECS: Elderly Care System for Fall and Bedsore Prevention using Non-Constraint Sensor

The Internet of Things (IoT) has become more practical nowadays. Various IoT applications are now being developed and deployed in order to attenuate our daily life problems. The global society is currently suffering problems associate to different aspects and ones that require our concern are regarding the aging society. Many and more countries are entering their aging society era while their healthcare sections have yet effective solutions to overcome the corresponding problems. Therefore, we propose the Elder Care System (ECS) for monitoring behaviours of elderly patients on the bed equipped with our designed system. The system includes notification system, in-bed position prediction system and real-time monitoring system. This paper demonstrates equipment, system architecture and dataflow. The result of our system deployment is discussed.
Ecological-Inspired System Design for Safety Manipulation Strategy in Home-care Robot

Manipulation tasks such as grasping, placing, and hand-over ability is crucial for the robot home application. In order to help the elderly at home, robots should have awareness and react carefully to prevent injury or accidents. The idea of safety strategy in robotics mainly used for industrial environment with less uncertainty. However, the home-care robot deals with many uncertainties condition and it is difficult to predict the human behavior than predict the industrial moving object. Thus, it is important to formulate a systematic approach to improving safety awareness on home-care robots. The following paper describes an approach of safety strategy for robot home manipulation task. This system uses an ecological approach based on the interconnection between perceptual system and action system according to spatial-temporal context of the updated information from environment. Some recommendation and preliminary experiments are conducted to show the possibility of further development for future research.
Research on Fatigue Driving Detection Method Based on Lightweight Convolutional Neural Network

In order to solve the problem of poor real-time performance and low accuracy of a single detection target of common driver fatigue driving detection method based on facial features in practical applications, a fatigue driving detection method based on lightweight convolutional neural network is proposed. First, the driver's facial feature point data set is made through MTCNN (multi task convolutional neural network). Then the data set is used to train a lightweight convolutional neural network to detect the accurate feature point position of the eyes and mouth. Finally, the open and close state of the driver's eyes and mouth is judged based on the feature point coordinates. According to the open and closed state of the eyes and mouth of the continuous multi-frame image, the driver is judged to be in the state of fatigue. The experimental results show that the processing time of the single frame image by the algorithm is 23.3 millisecond; the single detection accuracy is up to 99.4%, and the detection accuracy of fatigue driving can reach 95%. The algorithm is better real-time performance and higher accuracy, so it has certain engineering significance and application prospects.
Steady Flow Approximation using Capsule Neural Networks

CFD (Computational Fluid Dynamics) solvers have been very popular for fluid flow simulation which has been proved to be imperative to solve modern problems relating to analysis, design, and optimization in the field of aerodynamics. Nevertheless, CFD simulations are usually memory intensive and computationally demanding, iterative time-consuming processes. Such drawbacks often affect productivity and limit the design space exploration and forbid interactive design. The real-time prediction of fluid flow helps us to overcome these drawbacks. There have been many successful implementations of the application of Deep Neural Networks for the fluid flow prediction. Especially until now, CNNs (Convolutional Neural Networks) have been proven to be cutting edge solution for such approximations. However, CNN poses some challenges. We propose a new fluid flow approximation model for prediction of velocity field in non-uniform steady laminar flow based on Capsule Neural Networks.
The Effects of Coefficient Quantization on New CIC FIR Filters

Novel selective, highly effective and low complexity CIC (Cascaded-Integrator-Comb) FIR (Finite Impulse Response) filters, for applications in communication systems, are described here. The paper proposes the use of classical CIC filters as functions in generating new filters, by spreading the delays in the CIC filter comb stages. To verify the frequency response characteristic improvements of the suggested CIC FIR filters, comparative study of the frequency responses of the generated filters with classical CIC filters is provided. Quantization has a great influence on digital signal processing applications. The effects of coefficient quantization as one type of quantization that influences a filter characteristic are investigated here.
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems

U-Nets have been established as a standard neural network architecture for image-to-image problems such as segmentation and inverse problems in imaging. For high-dimensional applications, as they for example appear in 3D medical imaging, U-Nets however have prohibitive memory requirements. Here, we present a new fully-invertible U-Net-based architecture called the iUNet, which allows for the application of highly memory-efficient backpropagation procedures. As its main building block, we introduce learnable and invertible up- an downsampling operations. For this, we developed an open-source implementation in Pytorch for 1D, 2D and 3D data.
Crosstalk Suppression in Semi-Intrusive Load Monitoring Systems Using Hall Effect Sensors

Semi-intrusive load monitoring (SILM) is an appliance load monitoring approach using multiple meters, each meter measuring power for a subgroup of appliances. As an effective solution for demand response programs, SILM is used to get granular power measurements at the level of individual appliances in buildings. Hall effect sensors (HES) on each wire attached to a circuit breaker in distribution panels are one means of providing SILM. However, HES are greatly affected by crosstalk noise generated by neighboring wires, up to 35% of interfering signals. To remove crosstalk noise, this work proposes a blind source separation (BSS) approach designed to deal with sparse matrices, making SILM measurements accurate for home energy management systems. Our approach leverages two key elements: (i) a BSS algorithm based on non-correlation for sparse mixing matrix; (ii) a sensor gain compensation that leverages smart meter readings. The results demonstrate that the total power estimation error is reduced from 15% to 2% on the Tracebase dataset, and from 55% to 9% on our HES dataset monitored in a family home. Furthermore, the proposed approach outperforms standard BSS algorithms such as FastICA and InfoMax. This work shows that HES can be used for load monitoring in smart buildings.
Monte Carlo Tree Search Strategies in 2-Player Iterated Prisoner Dilemma Games

This study compares a player using Monte Carlo Tree Search (MCTS) against a variety of well-known Prisoner’s Dilemma strategies in 2-player tournaments. The MCTS player has a simple structure and a reasonable computation budget. Nevertheless, it is highly competitive against all tested strategies. As the MCTS player constructs its game tree, it updates the probability of cooperation in response to an opponent’s cooperation or defection. The trajectories of these updatings over the course of play are found to converge toward optimal counter-strategies against the particular opponent being played. In some cases the speed of progress toward an optimal counter strategy hinders the MCTS player.
Edge Intelligence: Challenges and Opportunities

With the development of edge computing and AI technology, how to run AI applications on the edge of network in a more efficient and effective way has become a new research hotspot. Edge intelligence, the next stage of edge computing, is a research field closely integrated with industrial scenario. Compared with traditional cloud intelligence, edge intelligence has better performance in service response, reliability and privacy protection. In order to achieve a better understand of this field, we summarize the previous research. In this paper, we first reviewed the background and development of edge intelligence. Then,we discussed the key technologies involved in edge intelligence. Finally, the challenges and opportunities of edge intelligence are prospected.
A Realistic Polar Influence Propagation Model for Location based Social Networks

Location-based social networks (LBSNs) have gained significant popularity in recent years. LBSNs are a special type of social networks bridging the gap between online social networks and offline physical world. It is due to this special property that influence propagation in LBSNs have become an interesting research topic in past few years. Significant research work discussing the dynamics of influence propagation in LBSNs has been done so far. But the current influence propagation models still lack important parameters which make the model more aligned with real world scenarios. Based on existing works, this research paper proposes an improved realistic influence propagation model using mobile crowdsourced data obtained from a renowned LBSN. The proposed influence model incorporates polarity of influence associating it with positive or negative state. A new interest-match coefficient is also proposed which is based on real-world similarity between interests. The experimental results indicate that the proposed influence propagation model is meaningful and better aligned with reality.
PHY Design and Implementation of a Galvanic Coupling Testbed for Intra-Body Communication Links

Intra-body communication (IBC) is a novel key research area that will foster personalized medicine by allowing in situ and real time monitoring in daily life. In this work, the energy efficient galvanic coupling (GC) technology is used to send data through intra-body links. A novel sound card-based GC testbed is designed and implemented, whose main features are: (i) low equipment requirements since it only employs two ordinary PCs with sound card support and Matlab software, (ii) high flexibility since all the parameters setting may be easily modified through the PC control panel and Matlab programs, (iii) real time physiological data transmissions, and (iv) almost error free communication by developing specific physical (PHY) layer techniques, which are implemented and tested with a real chicken tissue in the experimental evaluation. A signal to noise ratio (SNR) calculation is also proposed with the twofold purpose to be used for frequency offset compensation and as metric to evaluate the proposed architecture. The developed GC testbed may be easily replicated by the interested research community to carry out simulation-based experiments, thus fostering new research in this field. Moreover, the Matlab source code of the proposed GC transceiver is freely available online on Code Ocean.
A Scheme for RAID-6 Scaling Based on N-code

The arrival of the era of big data means that data has an explosive increment of tend. Storage system is important more and more. Enterprise information puts more demands on storage systems. In order to cope with the situation where the storage capacity may face insufficient storage space. The industry generally adopts the scaling the existing RAID storage system to solve the current storage capacity problem. RAID that is a widely used storage system. Because it has high data reliability. Redundant arrays of independent disks has been good expand. Expanding the original storage capacity has become an effective way. The RAID-6 storage system is used by the industry as a commonly RAID scaling storage system. Because RAID-6 has advantages of higher reliability and more convenient addition of disks. Among them, optimizing the migration process and reducing the amount of data migration are also commonly used scaling approaches. This paper proposes a scaling scheme based on N-Code code for RAID-6 storage system-SNC. Through real experimental data, it can be found that SNC scheme can reduce the amount of data migration to satisfy the requirement of minimal data migration. Compared with the Round-Robin scheme, this scheme reduces the amount of data migration of 76.52-88.94% and shortens the total data migration time of 54.5-62.4%.
An Empirical Panel Data Analysis on Technology Adoption and Economic Growth in 12-Selected Asian Countries

The contribution of new technology is synonym with developed nations when compares to the developing countries that commonly adopt the new technology. This paper attempts to empirically investigate the link between technology adoption and the economic growth of twelve selected developing Asian countries. The sources of technology adoption are from Mobile Cellular Subscription, Passenger Car in Use, Secure of internet Server and Fixed Telephone Line that could help in promoting economic growth of the nations. First, this study will apply three competing econometric models known as the Pooled OLS, the Random Effects Model and Fixed Effects Model to identify the suitable model that best explained the data of these nations. Next, from the model we could determine the significant sources which have effect on the economic growth of the nations. The empirical results indicate that Secure of Internet Server is the only source showing a positive significant impact on the Gross Domestic Product of these nations. Despite being the critical factor of technology adoption that would promote growth through e-commerce activities, countries are worry with the rapid increase of cybercrime threats. Hence, the movement toward sovereign control has become necessary for some countries to protect themselves from malicious cyber-attacks.
Jamming Resilient Tracking Using POMDP-Based Detection of Hidden Targets

This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.
Distributed Stable Global Broadcasting for SINR-Based Multi-Channel Wireless Multi-Hop Networks

Recently, the problem of Stable Global Broadcasting (SGB) with continuous packet injections at a source node has attracted considerable attention and much work has been carried out. However, existing SGB algorithms are all centralized and under the graph-based interference model. How to achieve efficient distributed SGB under the more realistic Signal-to-Interference-plus-Noise-Ratio (SINR) interference model is still an open issue. In this paper, we focus on the design of distributed SGB algorithms for multi-channel wireless multi-hop networks under the SINR model. We first present an efficient Backbone-based Multi-channel Concurrent Scheduling (BMCS) strategy and prove an upper bound of $1/2$ packets/slot for the broadcast capacity (i.e., the maximum supportable packet injection rate for all possible SGB algorithms). By iterating the BMCS strategy in different ways, we present two distributed SGB algorithms, one for deterministic packet injection model corresponding to the broadcast capacity (called SGB-DPI) and another for stochastic packet injection model (called SGB-SPI). We prove that: 1) both SGB-DPI and SGB-SPI meet the queue stability and latency stability constraints for providing stable global broadcasting services, and 2) SGB-DPI is throughput-optimal. We evaluate the performance of our proposed algorithms through simulations. The simulation results validate our theoretical analysis and also show that even under the stochastic packet injection model, SGB-DPI has a comparable and even better throughput performance than SGB-SPI.
An automated fracture detection from pelvic CT images with 3-D convolutional neural networks

The demand for an automatic bone fracture detection in the emergency section of the hospitals is high for quick diagnosis while maintaining the quality. Previous studies on fracture detection with computed tomography (CT) images or X-ray images have a performance limitation because those methods are based on 2-D image analysis and cannot consider the 3-D internal structure of pelvic bones. This study proposes an automated bone fracture detection from 3-D CT images. Firstly, it introduces a new 3-D annotation method of fractures (called 3-D surface annotation). By using 3-D shape data of pelvic surfaces, it decreases the annotation load significantly. The proposed method estimates the degree of fracture for each point on the pelvic surface. The degree is estimated by 3-D convolutional neural networks (CNN) using 3-D distribution of CT values inside the pelvic surface. The proposed method was validated by using 103 subjects. The accuracy, precision, recall, and specificity for the test data were 69.5%, 61.1%, 56.4%, and 77.7%, respectively.
Guiding Multiplayer MCTS by Focusing on Yourself

In n-player sequential move games, the second root-player move appears at tree depth n + 1. Depending on n and time, tree search techniques can struggle to expand the game tree deeply enough to find multiple-move plans of the root player, which is often more important for strategic play than considering every possible opponent move in between. The minimax-based Paranoid search and BRS+ algorithms currently achieve state-of-the-art performance, especially at short time settings, by using a generally incorrect opponent model. This simplifying model enables Alpha-Beta pruning, thus allowing the search to reach follow-up root player moves at greater search depths. This paper introduces abstraction over opponent moves to MCTS in multiplayer games, and uses its synergies with progressive widening in order to outperform these state-of-the-art minimax-type baselines. Progressive widening makes the search tree selective and deep enough to reach the root player’s next moves, and abstraction over opponent moves generalizes value estimates of the root player’s moves online across different opponent moves. In contrast to paranoid search approaches, opponent models do not have to be simplified. Experiments show that combining progressive widening with opponent move abstraction (MCTS-OMA-PW) leads to improved performance in the multiplayer games Chinese Checkers, Rolit, and Focus. Our work thus paves the way for improved multiplayer search by online generalisation that focuses on the root player’s actions, with the potential of improving real-time MCTS applications as well as training in expert iteration and other meta-algorithms where short time settings are relevant.
fMRI Imaging Based Human Brain Parcellation Methods: A review

In the context of functional magnetic resonance imaging (fMRI) images, human brain parcellation is a very critical issue for human brain network generation, analysis and functional connectivity researches. Thus, brain organization has been defined with specific topographies at different scales where brain is dividing into different areas or regions which are interconnecting closely between them and each one is represented by a node with specific local properties.Typically, a huge number of recent parcellation studies prove the important role of fMRI based approaches to parcelate the brain into different interest regions by using several distinct generated Atlases.In this paper, we present a comparison between different Atlases that represent the map of parcellation. More specifically, we study the initial steps which are applied later with graph theories that has been used in a huge number of previous works in order to detect and describe neurodegenerative diseases. In our work, we have started by calculating the mean correlation matrix using distinct Atlases. We have chosen popular Atlas such as probabilistic Atlas, YEO Atlas, Power Atlas, Crad Atlas, Fair Atlas and Dos Atlas.Our experimental results are based on testing fMRI images taken from the famous database for Alzheimer named ADNI.