Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Driving Performance of Pneumatic Soft Finger

Pneumatic soft hand performs as end effector of soft robotic system, which has the adaptive envelope grasping ability to irregular shape object. The pneumatic soft finger which has good bending motion ability is hard to be fabricated accurately. In this paper, based on a common pneumatic soft finger structure, the research is done on modeling of driving performance for pneumatic soft finger, and stretch ratio and static stiffness models are obtained. At last, stretch ratio and static stiffness are used as evaluation index of driving performance for pneumatic soft finger. Under empty load and static constant load conditions, bending state of pneumatic soft finger can be described by image processing module under different inflation pressures. Modeling analysis and experimental results indicate that stretch ratio has negative correlation with silicone rubber layer thickness and material shear modulus, and static stiffness has positive correlation with stretch ratio. So, the main contribution of our work is to provide references for fabricating pneumatic soft fingers.
Increasing DDR4 SDRAM throughput in parallel workloads

DRAM chips and modules have been essential building blocks of electronic equipment ranging from embedded systems to supercomputers for decades. However, the chip technology trends have created a gap between processor and memory performance, aggravated by multi-core processor design. Not only memory bandwidth becomes insufficient in various applications, the actual throughput, or efficiency, of the memory is reduced in multi-threaded applications.In this work, we analyze the memory efficiency problem in parallel workloads, identify two approaches to solving it, survey and assess some of the possible solutions. In our experiments with DDR4 SDRAM controller, we achieve the average efficiency of 71 percent in 16-threaded workloads.
Grid Search Optimization (GSO) Based Future Sales Prediction for Big Mart

In retailer domain predicting sales before actual sales plays a vital role for any retailer company like Big Mart or Mall for maintaining a successful business. Traditional forecasting models such as statistical model is commonly used as methodology for future sales prediction, but these techniques takes much more time to estimate the sales, also they are not capable to handle the non-linear data. Therefore, Machine Learning(ML) techniques are employed to handle both non-linear and linear data. ML techniques can also efficiently large volume of data like Big Mart dataset, containing large number of customer data and individual data item's attribute. A retailer company wants a model that can predict accurate sales so that it can keep track of customers future demand and update in advance the sale inventory. In this work, we propose a Grid Search Optimization (GSO) technique to optimize the parameters and select the best tuning hyper parameters, further ensemble with Xgboost techniques for forecasting the future sales of a retailer company such as Big Mart and we found our model produces the better result.
Arbitrary Time Stabilization of a Coupled Tank System: A Contraction based Approach

In this paper, we discuss stabilization in arbitrary time. The class of free-will arbitrary time stable system is considered. Stability has been analyzed using contraction theory. It is shown that the trajectories contracts towards one another within a given arbitrary time, thus leading to free-will arbitrary time stabilization of the system. The given strategy performs satisfactorily on a benchmark example of the coupled tank system.
A Machine Learning based Human Activity Recognition during Physical Exercise using Wavelet Packet Transform of PPG and Inertial Sensors data

Nowadays, high availability of optical and inertial sensors in fitness trackers and mobile phone attracts researchers attention on human activity recognition(HAR) through photo-plethysmography and inertial sensor data. It is also more suitable than camera based HAR in different circumstances. In this paper, we propose a wavelet packet transform based feature extraction technique from PPG along with accelerometer and gyroscope data to differentiate various physical activities like high and low resistance biking, running and walking. Wavelet packet transform(WPT) is applied on acquired signals and a number of statistical features and entropy feature are extracted. Random forest(RF) classifier is employed in recognition. A 5-fold cross validation is done to examine the performance and an accuracy of 97.8% is achieved which surpasses existing algorithms worked on the same dataset.
Emotion Recognition In Videos For Low-Memory Systems Using Deep-Learning

This paper explores a deep learning model for emotion recognition in videos, suitable for systems with limited memory like robots and embedded-systems. The proposed model is a Mini-xception+LSTM architecure with around 80k parameters. This model got a classification accuracy of 93% in dinstinction between Anger and Amusement emotions using the BioVidEmo dataset, compared to 70% accuracy that a recent work got for the same two emotions, and got 86 % and 90 % classification accuracy using the CK+dataset for seven and six emotions, respectively.
Weighted product Taxonomy for Mobile-Commerce site in Recommendation of Product based on Heuristic Approach

As we know number of smart phone users grows rapidly and Indian government survey said that there are 10+ billion smart phone users by 2020. We know that in metro cities people does not have time to purchase products of their daily needs. For this reason Online shopping using mobile phone is now in trend and survey also says that number of online shopping users grows exponentially. There are various types of factor plays important role in recommendation of product to customer. Product taxonomy, Customer behavioral and navigational factor are among them which are analyzed in this paper. Importance of weighted product taxonomy in fast recommendation of product is highlighted in this paper by authors. Authors proposed a greedy heuristic algorithm to search product in weighted product taxonomy. To proof the efficiency of proposed algorithm they use one tail independent sample‘t’ test with 5% level of significance and found that results are significant.
Energy-Efficient Data Collection Scheme Based on Mobile Edge Computing in WSNs

Energy-efficient data collection plays an imperative role in maximizing the lifetime of wireless sensor networks. In data collection, mobile sink can balance the energy consumption between sensor nodes and extend the network lifetime, but the route between sensor node to mobile sink changes frequently when the sink moves, which consumes more energy and brings more transmission delay. Static sinks can achieve low transmission delay and do not change the routing frequently. Based on this consideration, this paper proposes an energy-efficient routing scheme based on the collaborative work of dual sink nodes (mobile sink and static sink). This scheme considers static sinks to collect data in the center of the networks. The mobile sink moves along the network edge to gather the data, i.e., achieve mobile edge computing based data gathering. Mathematical analysis and simulation results demonstrate that the energy consumption of a node depends to a large extent on the position of the node. Compared with the previous scheme, the proposed method can greatly enhance the network lifetime and decrease the corresponding transmission delay.
An Optimized Spectrum Sensing Implementation Based on SVM, KNN and TREE Algorithms

Cognitive radio (CR) network is an intelligent technology, widely used to solve the scarcity of the radio spectrum by allowing the unlicensed users to have access to the licensed spectrum. Spectrum sensing (SS) phase is of great importance to the workings of a cognitive radio network (CRN). It consists in detecting licensed signals in a particular frequency band to decide whether the unlicensed signals can transmit or not. In order to detect primary user (PU) presence, this paper proposes a low cost and low power consumption spectrum sensing implementation based on real signals. These signals are generated by an ARDUINO UNO card and a 433 MHz Wireless transmitter (ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type). The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection (spectrum sensing) is done by three methods: support vector machine (SVM), Decision Trees (TREE) and k-nearest neighbors (KNN). The main objective is to identify the best method for spectrum sensing between the three methods. The performance evaluation of our proposed model is the probability of detection (P_d) and the false alarm probability (P_fa). This Comparative work has shown that the SS operation by SVM and KNN can be more accurate than TREE and some other classical detectors.
Spectral Graph Theory Based Topology Analysis for Reconfigurable Data Center Networks

Emerging technological innovations introduce the possibility to reconfigure the data center topology at runtime. The development of reconfigurable architectures can adapt their topology to account for changing demands, e.g., using Flyways-like augmented links. However, there is no common notion established in this area of how to evaluate the topology, the underlying theoretical analysis is not yet well studied. In this paper, we present the upper bound of the network diameter using spectral graph theory, to the best of our knowledge, which is a first theoretical attempt on understanding the nature of the reconfigurable data center networks. We further prove the algebraic connectivity is insensitive to the weight changes. Finally, based the algebra-connectivity λ_2, we give a comprehensive link-augmentation validity, which can be implemented in current DCNs potentially.
Analysis and Optimal Design of Magnetic Components in Dual-Active-Bridge Converter for 1 MVA Solid-State Transformer

The high frequency (HF) isolation transformer is the core element of a dual-active-bridge (DAB) bi-directional high-power DC-DC converter. At high frequency high power conversion, providing compact and efficient transformer design solutions need to consider the magnetic, electric as well as thermal aspect thoroughly and carefully to ensure the reliability and efficiency of the whole system. Detailed design considerations for a HF isolation transformer in a DAB conversion system are presented in this paper taking into consideration the insulation, high frequency magnetics characteristics, skin-proximity effects at high switching frequency as well as the thermal-fluid interaction analysis. Special attention is paid to the leakage inductance since improper value leads to an undesirable overshoot on the device voltage which may cause the SiC failure. This paper also presents the a multi-objective optimization design methodology applied to a 25kVA/25kHz, 1500V/750V transformer intended for DC-DC stage in 1MVA SiC-based solid state transformer (SST) and the Pareto optimal sets under different cooling methods are derived and analyzed. Comparison between different magnetic materials and different operation conditions are also carried out to find the characteristics variation of the designed transformer. The merit of the optimal design are validated through finite element modeling (FEM) and computational fluid dynamics (CFD) simulations.
Image-Translation-Based Road Marking Extraction From Mobile Laser Point Clouds

Road markings are one of the most important safety elements in a road network, and they play a critical role in traffic safety. However, the automatic extraction of road markings remains a technical challenge in the fields of smart city construction and automatic driving. This paper presents an image-translation-based method of obtaining the 3D vectors of typical road markings from mobile laser point clouds. First, ground roughness is used as a criterion to extract ground points based on the topological relationship of adjacent scan lines, and the feature images of a road surface are generated using the adapted inverse distance weighted method. Second, by comparing objective functions based on the pix2pix framework, a finely adjusted image-to-image translation model named P2P_L1 is proposed for the segmentation of road markings. The proposed model outperforms the advanced DeepLab V3+ network in terms of precision, F1-score, and mean Intersection over Union indicators in the comparative segmentation results of ten types of road markings in the Shenzhen test area. Third, methods such as node averaging and optimized iterative closest point are developed for the 3D vectorization of road markings. This study presents a new approach for the automatic extraction of road markings to provide effective technical support for the construction of smart cities.
Fusion of Heterogeneous Range Sensors Dataset for High Fidelity Surface Generation

Due to the need for higher quality depth data than possible with an individual range sensing approach nowadays, there has been a growing interest to develop an integrated depth sensing technique by fusion of different $3D$ acquisition approaches that are more precise than the individual devices. In this paper, a new unsupervised range data fusion method using distinct range sensors has been presented for the extraction of an accurate surface model. In the fusion method, the analysis of Kinect's depth data based on Haar wavelets is used to identify regions requiring finer scan by the Laser range sensor. The fused data illustrate the more accurate descriptive characteristic of the surface. The experimental results show a high quality reconstructed $3D$ model which validates the correctness of the real surfaces.
An Overview About Detection of Cyber-Attacks on Power SCADA Systems

The power SCADA systems have been undergoing several upgrades over the years. At first, these systems were based only on isolated processes, without the need to connect in a global network, such as the Internet. In recent years, the access to such an open network has been critical for the SCADA systems, due to the need for data exchange across large areas, between geographically remote industrial plants, or even because of the demand for remote applications. Despite the SCADA systems have advantages by being now connected to a network such as the Internet, several problems can arise due to that. For example, such a connection makes it possible for an intruder to compromise the whole system. SCADA systems are essential in the new concept of smart grids and are widely used in the monitoring and control of Distributed Energy Resources (DER) based on photovoltaics. In that context, this paper presents an overview of the main attacks found and documented in the literature, as well as possible techniques to detect such attacks.
Modeling and Control of a Back-to-Back system for turboelectric propulsion

This paper studies the aspects of turboelectric system control regarding the power conversion topology. Both Modeling and control of a back-to-back system connected to synchronous machines in both ends are considered. An instantaneous power technique in the αβ-frame is used to control the active rectifier connected to the permanent magnet synchronous generator (PMSG) while the inverter associated in the motor speed control uses synchronous (dq) frame robust control technique. The system considers a synchronous motor of 42kW connected to a 220V PMSG by a 670V DC bus.Mathematical modeling and simulation are used to provide insights on the whole system.
Design of a Variable Stiffness Actuator Based on Beam Flexure and Bistable Mechanism

Variable stiffness actuator (VSA) is important for its contribution to improve the robot's performance and safety, especially in case of interactions between human and uncertain environments. In this paper, a VSA is designed based on beam flexure as well bistable compliant crank-slider mechanism. The beam flexure and the bistable mechanism are connected by a slider, offering a variable supporting force. The stiffness is not influenced by the joint position and the joint orientation, but associated with the distance from the slider to rotating center of variable stiffness mechanism. This paper illustrates the operating principle, with a mechanics model, and validates the characteristics by experiments.
Research on the Influencing Factors of User Continuance Intention in Mobile Video Live Broadcast Platforms: Based on the Perspective of User Experience

For the booming mobile video live broadcast industry, enhancing user stickiness and increasing user continuance intention are the key to their future development. From the perspective of user experience, combining the Technology Acceptance Model (TAM) and some related demand theory, this paper constructs a theoretical model of influencing factors of user continuance intention in mobile video live broadcast platforms. The descriptive statistical analysis of 411 valid questionnaire data shows that the current user experience level and the intention to continue using the mobile video live broadcast platform are not high. The results of structural equation model (SEM) analysis show that user experience and need fulfillment have a significant positive impact on the user continuance intention. Perceived ease of use and perceived hedonic quality indirectly influence the user continuance intention through the mediating role of user experience, while perceived usefulness has no significant impact on user experience and user continuance intention, need fulfillment also has no significant impact on user experience. Finally, through crosstab analysis, it is found that some users' demographic characteristics and some usage environment variables are correlated with the user experience level.
A 40 nW 32.7 kHz CMOS Relaxation Oscillator with Comparator Offset Cancellation for Ultra-Low Power applications

This paper presents a fully-integrated 32.7 kHz relaxation oscillator designed in TSMC 40 nm CMOS for IoT applications. Simulation results show that the proposed relaxation oscillator consumes 40 nW at room temperature from 0.6 V power supply. The architecture achieves a temperature coefficient (TC) as low as 12.5 ppm/°C (μ = 35.5 ppm/°C for 400 samples) from −40°C to 125°C. Also, more than 50% of samples have a TC less than 30 ppm/°C. The oscillator also has a fast start-up time, achieving a steady-state of the operating frequency within 3 cycles. The energy efficiency is 1.22 nW/kHz, and the entire circuit occupies an active area of 0.127 mm2.
Wireless Power Transfer System for an Embedded Energy-Storage System in a PV Microinverter

This paper explains the development of a wireless power transfer system, whereby inductive coils and power converters, charge an embedded energy-storage system in a microinverter, using a photovoltaic panel. A detailed analysis of the design, implementation and control of a station for battery charging, using a PV panel of 35[W] is developed. An inductive-coupling link is used, which consists of transmitting electrical power from a power source to a load using a magnetic field. Finally, the results obtained in simulations from PSIM software and the experimental implementation are contrasted, showing a 12[V] battery which emulates the embedded energy-storage system to prove the concept. Then, the battery is charged with a controlled current of 300[mA].
Machine learning based SRM control using FPGAs for torque ripple minimization

Switched Reluctance Motor (SRM) is an attractive candidate for electric vehicles and much research is ongoing on motor and its control particularly in the last decade due to advancement in power electronics. Non-linear characteristics of SRM have been a challenge for control engineers for SRM control implementation. In such cases, learning models such as Artificial Neural Network (ANN), fuzzy logic, genetic algorithms etc., are helpful which are easier in terms of the programming and computation. One of the challenge for SRM application is its inherent torque ripple, which can affect drivability, vehicle dynamics and acoustics. Existing approaches for minimizing the ripple are based on standard normal Torque Sharing Function (TSF). In this paper, a novel approach is proposed to bring further reduction in the torque ripple in base, medium and high speed of the motor and also the implementation of this control algorithm is discussed. Two pre-trained ANN models are implemented one for providing run-time torque feedback and another one for calculating instantaneous reference currents for each phase. The novelty of this proposed approach is in providing a dynamic limiter for instantaneous current, based on the speed and torque error, in real time. The machine-learning model is implemented as a sequential logic on Xilinx Zynq-Ultrascale SoC Field Programmable Control Arrays (FPGA). The complete motor control is realized on the Programmable Logic (PL) part of the FPGA. FPGA utilization, along with timing diagram is evaluated for the ANN implementation, which could be used as a basis for selection of FPGAs.
Computer-Aided WCE Diagnosis Using Convolutional Neural Network and Label Transfer

Automatic diagnosis of gastropathy disease plays an important role in supporting the clinician and speeding up the examination process. While it is hard for clinicians to accurately detect gastrointestinal disease due to its great dependence on doctors experiences. In this paper, we propose an algorithm for stomach disease detection using deep learning features. First, images are partitioned into patches using a superpixel segmentation and points on superpixel edges are randomly selected as the center of image patches. Then a Convolutional Neural Network (CNN) is utilized to get more generic features of Wireless Capsule Endoscopy (WCE) stomach images. Finally, the detected patch label is transferred to superpixel labels to get a more accurate lesion detection area. Experiments on our database show that the proposed method can get more accurate disease regions and it outperforms the previous state-of-the-art methods based on hand-crafted features.
Comparison among Two-Phase Three-Wire AC Off-Grid Power Systems

The update of the power system moves over to connection of small distributed energy resource through grid-interactive converters. The electrifying process of the distribution power system allows the implementation of different grid configurations, such as those based on two-phase three-wire systems that are appealing for residential zones, commercial areas and off-grid systems. The off-grid two-phase three-wire topology can be implemented considering voltages as an extension of the conventional three-phase system (abn), voltages in quadrature (αβn), or voltages with opposite phases (xyn). Then, this paper aims at comparing the three off-grid power system topologies in terms of power quality characteristics, sizing of DC capacitor of grid-connected inverters, sizing of neutral conductor, and integration with the conventional mains. To accomplish those goals, a mathematical approach considering the Conservative Power Theory and computational simulations are shown and discussed.
NS-3 Simulation of Poisson-Pareto Burst Process in Multi-Source FANET Scenario with Network Coding

this paper considers the Poisson Pareto Burst Process as a traffic model for describing data streaming process from the flying node to the ground station. The flying node is the gateway of flying ad hoc network (FANET) that consists of several unmanned aerial vehicles (UAVs). Two scenarios are considered in the paper: in the first scenario, the gateway node in the form of a UAV transmits data from several UAVs to the ground station directly; in the second scenario, between the flying gateway node and the ground station, there is a relay-node with the OLSR routing protocol enabled. To evaluate QoS was selected Goodput metric. According to the experiments’ results, it was concluded that an increase in the number of sources increases the requirements for QoS metric. The solution to the problems could be the use of the network coding method, which is described through a scenario of two oncoming streams.