Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Delay-Constrained ALOHA and CSMA: A Learning-Based Low-Complexity Approximate Approach

Supporting delay-constrained traffic becomes more and more critical in multimedia communication systems, tactile Internet, networked control systems, and cyber-physical systems, etc. In delay-constrained traffic, each packet has a hard deadline—when it is not delivered before the hard deadline, it becomes useless and will be removed from the system. This feature is completely different from that of traditional delay-unconstrained traffic and brings new challenge to network protocol design. We focus on delay-constrained wireless communication in this paper. Many works design centralized scheduling policies in the downlink while a few works investigate distributed wireless access protocols in the uplink for delay-constrained traffic. In this work, we study the widely-used (slotted) ALOHA and CSMA wireless access protocols but under the new delay-constrained setting. Our goal is to compare delay-constrained ALOHA and CSMA for different system settings and thus give network operators guidelines on protocol selection. We use two Markov chains to analyze delay-constrained ALOHA and CSMA, respectively. However, the number of states of Markov chains increases exponentially with respect to the number of users in the network. Therefore, we can only compare the exact performance of delay-constrained ALOHA and CSMA for small-scale networks. To address the curse of dimensionality, we design a single-user parameterized ALOHA (resp. CSMA) system, where the parameters are to be learned to approximate the original multi-user ALOHA (resp. CSMA) system. We use small-scale networks to learn the best parameters and then apply the low-complexity approach to compare the approximate performance of delay-constrained ALOHA and CSMA in large-scale networks. We finally reveal the conditions under which ALOHA (resp. CSMA) outperforms CSMA (resp. ALOHA) in the delay-constrained setting.
Analysis of Operation Modes and Control for a Multiple Torque Component Single Air Gap Magnetless Machine

The fault-tolerant capability of the electrical machine system in the event of certain faults is critical for application in the fields of aerospace, in-wheel vehicle, etc. This paper analyzes the operation modes and control principle of a novel type of machine with multiple electromagnetic torque components. Firstly, the topology and operation principle of the electrical machine are briefly illustrated, and then the mathematical model, and especially the torque component is introduced. After that the control principle considering the characteristics of multi-torque components is analyzed. Finally, the operation modes of the machine under different working conditions are analyzed.
Adaptive MPC-based Cost Function for Capacitorless VAR Compensator in Distribution Networks

In the post-industrial digital-economy era, dealing with constant power loads along with distributed energy resources, including photovoltaic systems, energy storage, and electrified transportation are the new challenges. This is particularly important in the distribution portion of the electrical power system level where power quality and system integrity directly impact sensitive loads. The D-STATCOM is one solution to address the power quality issue as it provides dynamic reactive power and harmonic compensation. The backbone of the well-known voltage source converter, which is commonly used is the electrolytic capacitor. These capacitors have well-known failure characteristics that are accelerated in austere environments. A D-STATCOM technology that does not utilize electrolytic capacitors has the potential for increased reliability and service life with the goal of 40 years or more to match the lifetime of other power systems device. This paper proposes a method to further improve the efficiency and reliability of the capacitor-less D-STATCOM devices by penalizing high fidelity performance to reduce the average switching frequency using finite control set model predictive control (MPC). Simulation and experimental results using a testbed of 7.5kVA demonstrate a 30% reduction in switching frequency while still meeting 5% THD standards.
Adaptive Methodology for Process-Voltage-Temperature Verification

In pre-silicon verification of integrated circuits the Process-Voltage-Temperature coverage is typically done through a grid in the Voltage-Temperature space and Monte Carlo sampling of the Process space. The main limitations of this approach are the low coverage in the Voltage Temperature space and the high number of the required simulations. This paper proposes a circuit verification methodology that distributes the experiments in such a way that more experiments are concentrated in regions where the electrical parameter is outside or close to the specification limit. This is achieved by modeling the dependency of electrical parameters on the Process-Voltage-Temperature variables. The predictions of the model are then used to determine the new samples to be simulated, encouraging the ones that are expected to exceed the specification limit. We compare our results with a traditional Monte Carlo grid approach and evaluate both methods on an industrial benchmark circuit (a voltage regulator). The experimental results show that we obtain at least the same coverage while reducing the total number of simulations by 77%.
The Classification of Diabetic Foot Based on Faster

In order to realize the clinical automatic identification of the Wagner level of diabetic foot and solve the clinical problem that a large number of professional doctors need to spend time to detect and identify the patient’s foot disease level, this paper proposes a Faster R-CNN based diabetic foot Wagner level classification detection model. The image data of the sick feet we collected comes from the top hospitals and the network and the professional doctor team carries out the data set annotation and inspection, and the data validity and scientificity are guaranteed. Our model uses a convolutional neural network which has the advantages of automatic feature extraction, strong generalization ability, and high recognition accuracy. The detection model is based on two model schemes of resnet101 network and VGG16 network respectively. Experiments show that the precision of the Faster R-CNN model based on resnet101 is higher than that of VGG16. In view of the uneven distribution of the number of foot pictures in some grades, we use the method of changing the contrast of the picture, adding random Gaussian noise, and flipping to enhance the data. The final result shows that the accuracy of our improved method is increased to 95.24% on the original basis. It’s good to provide effective solutions for the current clinical assisted primary screening of diabetic foot severity.
Effect of Surface Acetylated-Chitin Nanocrystals on Structure and Mechanical Properties of Poly(lactic acid)

In this work, the miscibility between chitin nanocrystals (ChNs) and poly(lactic acid) (PLA) was expected to be improved by surface acetylation of ChN. The reaction of acetic anhydride onto the ChN surface was confirmed by FTIR and C-13 NMR, while XRD and TEM proved the crystalline structure and rod-like morphology were maintained. The acetylated ChN (AChN) was incorporated into a PLA matrix by solution blending, and resulted in an increase of tensile strength and Young's modulus and they reached to the maximum value as 45 and 37% higher than neat PLA film, respectively, with the loading level of AChN reaching to 4 wt %. The enhancement could be attributed to that acetylation improved dispersion of AChN in the PLA matrix and interfacial adhesion between AChN and PLA. The performances of the nanocomposites based on PLA and chitin nanocrystals derived from renewable resources have good potential for industrial applications. (C) 2013 Wiley Periodicals, Inc.
Adaptive Sliding Mode Control for Multi-agent Time-varying Formation Tracking with Dynamic Actuator Faults

This paper investigates time-varying formation tracking problem for second-order multi-agent system with dynamic actuator faults, time-varying disturbances and noise. Based on sliding mode control method, a new distributed adaptive sliding mode controller is proposed. By Lyapunov stability theory, it can be verified that the multi-agent system with our designed controller can achieve the time-varying formation task and resist the negative influences caused by the adverse factors mentioned above effectively. Unlike some works which can only work for smooth disturbance, our proposed method is effective to any bounded noise and disturbance including unsmooth ones. Furthermore, a numerical example and several simulation results presented in this paper can also demonstrate the effectiveness of the proposed control method.
Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities

The objective of an autonomous underwater vehicle (AUV) in an exploration mission is to maximize its information gathering about the environment it explores. In a mine countermeasures (MCM) survey the goal is to detect and localize targets. The work presented addresses the efficacy of reinforcement Q-Learning-based planning to localize targets in an area where the sonar signal-to-noise-ratio (SNR) may vary, and consequently, the probability of detection and the likelihood of false alarm may vary as well. A sensitivity study of the proposed algorithm is provided, which identifies the parameters that drive the planner’s performance. A visual example is used to illustrate the parameters’ impact on the path-planning policy. Results from simulation and two-dimensional experimentation are presented for MCM missions in two different search areas with varying environmental conditions.
Impact Force Minimization Algorithm for Collaborative Robots Using Impact Force Prediction Model

Recently, the demand for collaborative robots is increasing in the industrial field. However, as the collaborative robots share the same workspace with human workers, there is a high possibility of collision between the robot and the worker. A possible method to ensure the safety of a human worker is to restrict the impact force that the robot exerts on the worker during a collision. That is, if the impact force can be predicted, the robot motion that causes excessive impact force can be detected and handled properly before the actual robot motion. To this end, an algorithm for predicting the impact force generated by a collision is proposed, and a method for ensuring the human safety, by modifying the trajectory of the robot when the excessive impact is predicted with current motion, is investigated. To establish the impact force prediction model, collision experiments were performed with a 6-DOF collaborative robot and a dummy. Moreover, an algorithm for minimizing the impact force, by reducing the end-effector velocity of the robot when excessive impact is predicted from the established model, is proposed to ensure the human safety. The performance of the algorithm was verified through various experiments.
Analytical Modelling of Doubly Salient Electric Machines using Conformal Mapping Method

This paper presents an analytical approach for the design of doubly salient electric machines based on the concept of conformal mapping. The main idea is to reduce the number of required conformal transformations used for doubly salient machines without losing the analytical side of the approach. As an example, we consider the wound salient pole rotor synchronous machine. However, this proposed analytical model is general and fits any other structure to consider the slotting or doubly salient effect. The analytical results obtained by this method are in very good agreement comparing to those of finite element simulations. Moreover, the calculation time is highly reduced. The accuracy of the proposed analytical model is checked out by analyzing the performance of the machine (torque, flux, Back EMF) for on-load and no-load conditions.
Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture

Building extraction with high accuracy using semantic segmentation from high-resolution remotely sensed imagery has a wide range of applications like urban planning, updating of geospatial database, and disaster management. However, automatic building extraction with non-noisy segmentation map and obtaining accurate boundary information is a big challenge for most of the popular deep learning methods due to the existence of some barriers like cars, vegetation cover and shadow of trees in the high-resolution remote sensing imagery. Thus, we introduce an end-to-end convolutional neural network called Generative Adversarial Network (GAN) in this study to tackle these issues. In the generative model, we utilized SegNet model with Bi-directional Convolutional LSTM (BConvLSTM) to generate the segmentation map from Massachusetts building dataset containing high-resolution aerial imagery. BConvLSTM combines encoded features (containing of more local information) and decoded features (containing of more semantic information) to improve the performance of the model even with the presence of complex backgrounds and barriers. The adversarial training method enforces long-range spatial label vicinity to tackle with the issue of covering building objects with the existing occlusions such as trees, cars and shadows and achieve high-quality building segmentation outcomes under the complex areas. The quantitative results obtained by the proposed technique with an average F1-score of 96.81% show that the suggested approach could achieve better results through detecting and adjusting the difference between the segmentation model output and the reference map compared to other state-of-the-art approaches such as autoencoder method with 91.36%, SegNet+BConvLSTM with 95.96%, FCN-CRFs with 95.36%% SegNet with 94.77%, and GAN-SCA model with 96.36% accuracy.
DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model.
Compact Probabilistic Poisson Neuron based on Back-Hopping Oscillation in STT-MRAM for All-Spin Deep Spiking Neural Network

A unique compact Poisson neuron that encodes information in the tunable duty cycle of probabilistic spike trains is presented as an enabling technology for cost-effective spiking neural network (SNN) hardware. The Poisson neuron exploits the back-hopping oscillation (BHO) in scalable spin-transfer torque (STT)-MRAM. The macrospin LLGS simulation confirms that the coupled local Joule heating and STT effects are responsible for the bias-dependent BHO. The complete neuron circuit design is at least $6\times$ smaller than the state-of-the-art integrate-and- fire (IF) CMOS neuron. Hardware-friendly all-spin deep SNNs achieve equivalent accuracy to deep neural networks (DNN), 98.4 % for MNIST, even when considering the probabilistic nature of neurons.
Effects of the technology scaling down to 28nm on Ultra-Low Voltage and Power OTA performance using TCAD simulations

In this paper, the effect on the performances of the technology scaling down to 28nm (bulk and planar) is studied on a specific Ultra-Low Voltage/Power (ULV/P) OTA [4] design. This study is carried out for 90nm and 28nm technology node OTA using specifically the shortest available channel length and benchmarked with the original OTA in 180nm. First, the TCAD simulation (Synopsys) of the design implemented with the corresponding iPDK’s are performed and the electrical parameters are extracted. We deduced that the short channel devices still conserve a good dc gain explained by physics considerations. Then, the performance analysis based on the power consumption and Figure-Of-Merit leads to conclude that alike the large signal performance, the small signal performance is maintained with a significant power dissipation reduction making the 28nm technology still an interesting choice for small-signal applications. However, a specific study of the variability (process variation) of the transistor performance shows that OTA implemented in 28nm node may provide a poor fabrication yield suggesting to consider ULV/P OTA design immune to the variability introduced by low technology node.
A Hierarchy MCTS Algorithm for The Automated PCB Routing

The PCB electronic components placement and routing are very importance for the Printed Circuit Board (PCB) design, which needs plenty of time and precision. Some algorithms have been proposed for automatic electronic components placement and routing. However, existing methods can’t meet the precision or efficiency demand for the PCB design. In this work, we propose a hierarchy MCTS algorithm for the PCB routing. The PCB is mapped to a grid map. Hence, the PCB routing can be converted to an optimal route searching game from the start pin to the end pin. The optimal goal is to find the shortest routes for the pins on the PCB by following the routing rules. Then, the hierarchy MCTS is employed to find the optimal routes for the PCB routing. The hierarchy MCTS consists of two MCTSs. The first MCTS will generate a sketchy route and a subgoal for the second MCTS. Hence, the second MCTS can obtain two optimal routes from the start pin to the subgoal and from the subgoal to the end pin. The two optimal routes will be concatenated as the final optimal route. The advantage of using hierarchy MCTS is that the second MCTS can search more efficiently according to the subgoal generated by the first MCTS and generate a better route. After experiments, our proposed algorithm can obtain the optimal routes and improve 15.38% computing efficiency, compared with existing methods.
High-Speed Synchronous Reluctance Motor for Electric-Spindle Application

In last years, high speed integral motor spindles are obtaining attentions due to their compact frame. The actual choice for raw plastic and wood materials processing are the induction motor and the interior permanent magnet machine. In particular, permanent magnet motors are suitable for wide constant power-speed range; they show high power density and high power factor. However, the increasing cost of rare-earth magnets leads the designer to investigate other solutions. For this sake, this paper deals with the design procedure of a high-speed free rare-earth motor for a electric-spindle applications. Hereafter, the synchronous reluctance machine is investigated. Such a machine represents a valid alternative thanks to the absence of rare-earth magnets and, consequently, the reduced cost. The goal of this study is to show the feasibility of such a high-speed motor. For this aim, a 4. 5kW motor at the maximum rotation speed of 20000 rpm is designed. A multi-objective optimization process is proved to be an useful tool for this sake. The best individual is capable to deliver the rated power at both base speed and maximum speed. Synchronous reluctance machine proves to be a good free rare-earth candidate for high speed integral motor spindle applications.
Application of Epoxy-Bonded FBG Temperature Sensors for High-Temperature Superconductor-Coated Conductor Quench Detection

Fast detection of quench in high-temperature superconductors (HTS) is essential for the application of HTS magnets due to the low normal zone propagation velocity (NZPV). In this work, quench measurement of single-HTS wires has been carried out at 80 K through gas cooling. We use both electrical and thermoelectric methods to verify the ability of fiber Bragg grating (FBG) sensors to be applied to quench detection and study the quench behavior of HTS tape in the view of NZPV. An FBG chain with five FBG sensors was attached to a 12-mm-width Superpower REBCO tape in order to monitor the temperature during a quench induced by a bonded heater. PT100 sensors were also attached to the HTS tape as reference sensor. The measured FBG wavelength data was compared with the voltage and PT100 temperature data. Our results show that commercially available FBGs can capture the temperature change as soon as the quench occurs; however, further work is needed to improve the stability and accuracy of the sensors.
Assessing Short Answers in Indonesian Using Semantic Text Similarity Method and Dynamic Corpus

Automatic assessment of short answers is one of the Computer Assisted Test works that can assess answers in natural language. Several methods have been used to create a system capable of assessing short answers that are close to human markings. In Indonesian, it might be easy to use string-based similarity methods by matching keywords, as has been done in previous studies. However, short answers have characteristics that focus on content, question type, and answer length, which cannot be accommodated only by string-based methods. This study aims to implement a hybrid method using corpus and string-based similarities. The Semantic Text Similarity (STS) method was used in this study to assess short answers in Indonesian. The STS method consists of three combinations of similarity methods, namely Normalized and Modified Longest Common Subsequence, Second Order Co-occurrence Pointwise Mutual Information, and Common Word Order Similarity. We also use a dynamic corpus with the advantage of being relatively small and adaptable to the learning domain. The Gensim Module is used to generate a dynamic corpus. The dynamic corpus uses the top five answers from students obtained from the Gensim module. The STS method is compared with the Cosine Similarity method since Cosine Similarity is the most commonly used method to assess answers in Indonesian. The results show that the STS method can outperform the Cosine Similarity method based on the Mean Absolute Error value, but still not outperformed in terms of correlation.