Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Virtual Reality with Camera Data and a Wearable Sensor Jacket to Facilitate Robot Teleoperation

Unmanned ground vehicles (UGV) with differing degrees of autonomy are increasingly used for routine tasks, albeit still widely controlled through teleoperation in safety-critical contexts. We pro-pose to complement a traditional approach of showing camera views on a 2D screen with an immersive Virtual Environment (VE), displaying camera data with depth cues around a model of the robot. Combining both approaches with a wearable sensor jacket, the operator can choose the best one for the current task. Preliminary experiments showed that even untrained operators can successfully solve pick and place tasks with high accuracy with our system.
Image Based Design Patent Retrieval with Classification and Indexing

As a new image retrieval technology, Content Based Image Retrieval (CBIR) has a broad application prospect. In a normal CBIR system, the mature technology is usually as follows.Firstly, the underlying features of the image in the database are extracted in the preprocessing process. Then, during the retrieval process, the feature is matched with the feature of the Query Image linearly [1], [2], [3]. As the existence of semantic gap, this technique does not perform well enough to satisfy users’ need.Compared with the general image on the network, the design patent images have some special properties. For example, the object in one image is singular, the images are fine categorized and little blurred, etc (e.g. Fig. 1). Based on these good properties, this paper presents an image retrieval method of design patent based on classification technology. When the retrieved image is correctly classified with a high probability, it is retrieved in the category; otherwise, it is retrieved in all images by using indexing technology to speed up the retrieval.
A Recurrent Neural Network Approach to Model Failure Rate Considering Random and Deteriorating Failures

Recurrent neural networks (RNNs) utilize their internal state to handle variable length sequences, as time series; namely here as uncertain failure rates of the systems. Failure rate model of the components are required to improve systems reliability. Although the failure rate model has undeniable importance systems reliability assessment, an acceptable failure rate model has not been proposed to consider all causes of failures particularly random failures. Therefore, planners and decision makers are susceptible to a high financial risk for their decisions in the system. An approach is addressed to consider random failure rate along with deteriorating failure rate, to ameliorate this risks, in this paper. Therefore, the complexity of failure behavior is considered, while modeling considering the failure data as a time series. Moreover, the results of failure rate estimation are tested on a reliability-centered maintenance (RCM) implementation to prove the importance of random failure rate consideration. The results express that a more effective strategy can be regarded for preventive maintenance (PM) scheduling in RCM problem, when the proposed approach is utilized for failure rate modeling.
Quadri-Cluster Broadband Circularly-Polarized Sequentially-Rotated Metasurface-Based Antenna Array for C-Band Satellite Communications

This research proposes a compact quadri-cluster broadband circularly polarized (CP) sequentially-rotated metasurface-based (MTS) antenna array for the C-band frequency spectrum. One cluster of the quadri-cluster MTS-based antenna array consisted of 4 $\times $ 4 S-shaped periodically-arranged MTS elements. The sequentially-rotated feed network was utilized to realize circular polarization and improve the impedance bandwidth (IBW), 3-dB axial ratio bandwidth (ARBW) and 3-dB boresight gain bandwidth of the quadri-cluster MTS-based antenna array. Simulations were performed and results were compared with experiments. The measured IBW and ARBW were 84.74% (4.0–9.0 GHz) and 57.6% (4.2–7.6 GHz) at the center frequency of 5.9 GHz, rendering the proposed quadri-cluster MTS-based antenna array suitable for satellite communication applications. In addition, the quadri-cluster MTS-based antenna array achieved the measured 3-dB boresight gain bandwidth of 81.3% (3.9–8.7 GHz), the maximum gain of 10.04 dBic at 5.6 GHz, and low radar cross-section. Specifically, the novelty of this research lies in the use of the sequentially-rotated feed network with the S-shaped MTS elements to effectively enhance ARBW of the quadri-cluster MTS-based antenna array for the C-band frequency spectrum.
Preserving Family Album Photos with the HoloLens 2

Following the leap set by the introduction of digital photography and spread of social media, the custom of creating and nourishing a family photo album is steadily declining. The feeling of waving goodbye to an old technology may hence explain why collectors and museums are increasingly searching for such type of photographs. In such scenario, a barrier set to the preservation and analysis of the enormous source of information is the unavailability of a fine-grained digitization and cataloguing infrastructure. An opportunity to resolve such issue is offered by augmented reality (AR): we envision an AR-application capable of individuating and segmenting photos, while browsing through a family album. In addition to this digitization step, we also include a second one where the digitized images are analyzed, tagging them with meta-data pertaining their presumed socio-historical context and their date. The addition of such meta-data is key not only from a cataloguing point of view, but also from a conservation perspective. In fact, analog photos are more often preserved when some information pertaining their subject and their date is known. To this aim, we experiment the use of the HoloLens 2 along with artificial intelligence paradigms.
Tradeoff between Model Accuracy and Cost for Federated Learning in the Mobile Edge Computing Systems

The combination of MEC and federated learning is a promising research direction in network intelligence. However, the performance and efficiency of federated learning cannot be guaranteed in MEC systems. In this paper, in order to balance the model accuracy and resource consumption of federated learning in MEC systems, a framework for deploying federated learning in MEC systems is proposed. Based on this framework, the model accuracy performance of federated learning is analyzed, and a tractable upper bound of accuracy loss is given. In addition, an optimization problem is proposed to balance the accuracy loss and resource consumption of the training model, and a joint optimization algorithm with high computational efficiency is designed to approach the optimal solution. Finally, numerical simulation and experimental results show that the joint optimization algorithm can not only improve the model accuracy of federated learning, but also significantly reduce the resource consumption.
Investigation and Evaluation of Solid-State Marx Pulse Generator Based on 3-D Busbar

SiC-MOSFET has been widely used in nanosecond pulse power generators due to its excellent switching characteristics. The solid-state Marx generator using SiC-MOSFET has the advantages of modularity, flexible adjustment, and economic reliability. However, due to the limitation of the busbar loop’s parasitic inductance, the excellent switching characteristics of SiC-MOSFETs have not yet been fully utilized in solid-state Marx. This article discusses the PCB wiring structure of the single-module of solid-state Marx and the influence of its superimposed spatial distribution parameters on the output pulse waveform. The principle of 3-D busbar structure and mutual inductance cancellation method to reduce the parasitic inductance of pulse busbar loop is proposed. And verify the above principle through electromagnetic simulation and four-stage superposition experiment. The parasitic inductance of the busbar loop is less than 4 nH. The voltage edge of the output pulse is further reduced, the current rise/fall speed is increased by about 2.3 times, and the switching voltage overshoot is reduced by 80%. And the proposed structure effectively suppresses the gate bounce and improves the solid-state Marx pulse characteristics. Finally, the stability and reliability of the system are improved too.
Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks

Network information security pipeline based on the grey relational cluster and neural networks is designed and implemented in this paper. This method is based on the principle that the optimal selected feature set must contain the feature with the highest information entropy gain to the data set category. First, the feature with the largest information gain is selected from all features as the search starting point, and then the sample data set classification mark is fully considered. For the better performance, the neural networks are considered. The network learning ability is directly determined by its complexity. The learning of general complex problems and large sample data will bring about a core dramatic increase in network scale. The proposed model is validated through the simulation.
A Multi-Classifier System for Rock Mass Crack Segmentation Based on Convolutional Neural Networks

In rock masses, presence of cracks greatly affects the behavior of it. Obtaining the cracks is very important in specialized analysis of rock mechanics. In computer vision applications, crack segmentation task in an intricate texture such as rock mass, is difficult. Crack segmentation problem can consider as an edge detection task so we can use edge detection methods to achieve it. In this paper, we propose a multi-classifier system based on deep convolutional neural network (CNN) to predict pixel-wise cracks in rock mass images. We provide a dataset consists of 489 RGB rock mass images with manual ground truths. For training classifiers, we create two sub-datasets obtained by mentioned dataset. Also we introduce a new approach of image labeling to improve general methods. Based on the results, our method achieves F-score of 84.0, which has a best performance compared to different methods.
From TensorFlow Graphs to LUTs and Wires: Automated Sparse and Physically Aware CNN Hardware Generation

We present algorithms and an architectural methodology to enable zero skipping while increasing frequency in per-layer customized data flow Convolutional Neural Network (CNN) inference accelerators for FPGAs. Data flow architectures leverage the static configurability of FPGAs to increase processing efficiency, reduce dynamic muxing, and save routing wires. While this holds out the promise of high efficiency, these architectures require a different circuit to implement every CNN, making automated exploration and implementation of the accelerator essential. Each accelerator has layer-specific subcircuits with CNN-specific parallelization parameters and CNN graph topology-based interconnection that impact fanout and routing congestion, which lower the hardware operating frequency with naive implementation strategies. To address this, we designed latency insensitive hardware templates that build a model of signal fanout and span and instantiate different structures within each compute unit to ensure a high operating frequency regardless of CNN topology and parallelism settings. We also leverage the hardware efficiency of data flow architectures to add support for zero-weight-skipping at a normalized area cost less than one half of prior work. The overall optimization tool chooses parallelism settings for each layer-specific hardware unit to balance throughput across all layers of the CNN, while respecting the FPGA device limits on available buffering space and DSP blocks. Together these optimizations enable throughput on a sparse Resnet-50 model at a batch size of 1 of 4550 images/s, which is nearly 4x the throughput of NVIDIA's fastest machine learning targeted GPU, the V100, and outperforms all prior work on FPGAs.
Static Detection of Ransomware Using LSTM Network and PE Header

Ransomware is a type of malware from cryptovirology that perpetually blocks access to a victim’s data unless a ransom is paid. Today, this type of malware has grown dramatically and has targeted the computer systems of some important organizations such as hospitals, banks, and Water Organization. Therefore, early detection of this type of malware is very important. This paper describes a solution to ransomware detection based on executable file headers. Header of the executable file expresses important information about the structure of the program. In other words, the header’s information is a sequence of bytes, and changing it changes the structure of the program file. In the proposed method, using LSTM network, the sequence of bytes that constructs the header is processed and the ransomware samples are separated from the benign samples. The proposed method can detect a ransomware sample with 93.25 accuracy without running the program and using a raw header, so it is suitable for quick detection of suspicious samples.
Application of Features Based on Auditory Signal Model in Online English Stress Detection

Application of features based on auditory signal model in online English stress detection is studied in this paper. For the signal processing model, more the RNN layers, the stronger the ability to recognize the speaker ID. But, more the layers means that the training cost is high, and it is easier to produce over-fitting. The proposed research work has designed a new algorithm by obtaining different structures through different combinations of these two parameters. Representative features mainly include the formant, cepstrum, LPC coefficient and its derivative parameters, etc., the features are further utilized to finalize the system. The experiment has proven the overall performance.
Better Application of Bayesian Deep Learning to Diagnose Disease

A significant, but challenging, phase in healthcare is detecting and predicting status for a complex human disease. By the year 2050, the global population over 60 years of age will be 2 billion, according to the World Health Organization. For most health conditions, age is the principal cause. For the analysis of multi-dimensional patient’s data, machine learning provides a principled approach to develop and compare sophisticated, automated and objective algorithms. Massive public health problems and economic burdens have been generated by complex human diseases such as cancers, Alzheimer, Parkinson, Motor Neuron, cardiovascular, and respiratory diseases. Technically, deep learning concept is derived machine learning under AI (Artificial Intelligence) that follows the brain structure and function in processing data and decision making. Deep learning uses many layers of nonlinear processing units to work with complex data sets and can transform them to analyze the outcome. It has inspired a lot of interest in its use for medical imaging problems. In order to ensure patient safety by clinical diagnostics, Bayesian Deep Learning makes more precise and intelligent predictions.The goal of this research was to optimize and implement a better algorithm for deep learning. A research field for growth is to complement deep learning with Bayesian thought. The performance of the method proposed is dependent on the sensitivity, specificity, accuracy and precision of the algorithms. The current implementation supports showing that a mixture of deep learning algorithms can be found in the best model. Machine Learning tools such as Tensorflow 2.0, Scikit-Learn, Pandas, Matplotlib & numpy are used for execution with powerful Python in this proposed application. Bayesian Deep Learning's proposed approach has outperformed current methods of diagnosis & prediction and demonstrates high precision and great potential to develop clinical tools. With more than 98 percent accuracy, one prototype and 2 data sets are used for clinical diagnosis and predictions for cancer and diabetes.
A Survey on Video Streaming in Multipath and Multihomed Overlay Networks

The focus of this survey is to study the protocols, mechanisms, and the latest standards proposed in the literature for improving the performance and quality of video content in multipath and multihomed overlay networks. Multipath is a broader term, but in the context of this survey, we define multipath as enhancing network routing technique by using various paths that are not necessarily completely disjoint. Multipath can furnish a variety of advantages such as reliability,connection persistence, increased perceived throughput and load balancing. On the other hand, multihoming is the ability to use multiple network interfaces when connecting to the Internet to increase reliability, resilience, and performance. Most existing surveys are specialized in one specific domain area related to multipath or multihoming. This study covers the research proposals at the different layers/sublayers of an overlay network from transport to the application and extends to cover the latest technologies like machine learning, Fog and Mobile Edge computing, VR 360 video, and the Internet of Multimedia Things (IoMT). As such, our work tries to be as comprehensive as possible to relate multipath and multihoming research solutions for video streaming to the current and emerging video streaming technologies.
MODELING EDUCATION TO DEVELOP RESISTIVE ECONOMICS IN AGRICULTURAL ECONOMY IN IRAN

The disruption of the economy has many problems for society . As a result all countries in the world are seeking to achieve improvements in productivity, So that they can get more productive by consuming lesser resources. This article is intended to explain modeling Agricultural Extension, in the Resistive Economics of the Islamic Republic of Iran and the presentation of the paradigm model has been done with the help of the Grounded theory, the factors influencing the formation of agricultural modeling in the resistive economics, have been identified and the paradigm model of resistance economy in agriculture. The overall approach to this research is qualitative and for the analysis of qualitative data Grounded theory, has been used in which 21 experts and pundits in the field of agriculture and economics sampling, through theoretical sampling method. Data were collected by semi-structured interview and was coded for data analysis using MAXQDA12 software. In this study, a total of 122 codes, 34 categories and 4 main categories, were extracted. The main category include: the weaknesses and Strengths points, the opportunities and threats of the agricultural sector, the promotion of agriculture, and the meaning of resilient economy, according to experts. The results showed that the most important factor of economic deterrent in agriculture: (High cost of production) and the most important factors leading the economy are agriculture (planning the right policies for macroeconomics), and for factor of resistive economics and the concept that has the greatest impact on creating (this factor domestic economic), To influence role of resistance in agriculture (provision of food was achieved through agriculture).
Traffic Sign Recognition using Deeplearning for Autonomous Driverless Vehicles

Recently, the smart world, smart cars, and so on plays a major role. To ensure Traffic Safety, the development of smart cars requires the detection and recognition of traffic signs. The algorithm is the extended work on the classical LeNet-5 CNN model. The proposed technique makes use of Gabor based kernel followed by a normal convolutional kernel after the pooling layer. The optimizer technique used here is the Adams method. Hue, Saturation Value color space features have a speed of detection is faster and low suffering from illumination. The proposed technique for traffic sign recognition is evaluated using the German Traffic Sign Recognition Benchmark. The proposed technique gives an accuracy of nearly 99%.
A V-Band Low-Power Compact LNA in 130-nm SiGe BiCMOS Technology

This letter presents the design of a <inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>-band low-power compact low-noise amplifier (LNA) in a 130-nm SiGe BiCMOS technology. For the low-power and low-noise requirements, transistors need to operate with low-voltage supply and low current density, which comes at the cost of lower gain per BJT stage. We use a technique to cancel the Miller capacitance in a single-stage differential amplifier and achieve high-gain, low-power, and low-noise simultaneously. The circuit topology is analyzed, and the transistor core layout and the matching network design considerations are discussed. The measured circuit shows a peak gain of 14.1 dB in a 3-dB bandwidth from 44 to 67 GHz while consuming 5.1 mW. Experimental results show an output power of 7.1 dBm at 1-dB compression with an associated power-added efficiency of 30%. The simulated noise figure is 3.3 dB at the center frequency. To the best of the authors’ knowledge, the highest figure of merit among <inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>-band LNAs based on silicon is reported.
Research on Cloud Parking System Based on Big Data

With the development of social networking, the focus of people’s production and life has gradually shifted to the network. At the same time, the development of social economy is also showing high speed, people’s living standards have also changed significantly. As an important means of transportation for people’s daily travel, vehicles are more and more popular, and the number of vehicles in the city is also increasing. This phenomenon is particularly prominent in the first and second tier cities. The contradiction between the limited parking space and the sharp increase of vehicles is gradually highlighted. Based on the application research of big data technology in the parking system, the parking information is collected through a large number of parking data in the cloud, and then the data is classified and analyzed by using data mining algorithm. It can realize the reservation of parking and reasonable planning of routes, and provide scientific services for car owners according to the habits of car owners, so as to improve people’s travel satisfaction to a greater extent.
Boarding Sensation Presentation of the Biped Walking Robot with a Low-cost Two-axis Motion Platform

We render a boarding sensation of a biped robot at low cost and high immersive by approximate the 6-DOF motion such as the impact, vibration, and steep slope experienced on boarding a biped robot to a 2-DOF rolling motion at max ± 25 degrees in both at pitch and roll directions with our low-cost two-axis motion platform.
An RL Approach to Radio Resource Management in Heterogeneous Virtual RANs

5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process.