Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Posterior Class Probabilities in the Problem of Differential Diagnostics of Diseases of the Mucous Membranes of the Oral Cavity

The approximation of the discriminant Anderson function at a point by a linear function in the feature space used in supervised learning allows us to estimate the posterior probabilities of classes at a point without using conditional probability distributions of feature classes and without using add-ons such as the Platt calibrator. We used the method of evaluating the posterior probability of a class to develop a computer program for differential diagnosis of oral lichen planus with oral leukoplakia and oral squamous cell carcinoma. Diagnosis by the posterior probabilities of diseases in the examined patient increases the reliability of the diagnosis and greatly facilitates the choice of treatment tactics.
CNN based Smart Surveillance System: A Smart IoT Application Post Covid-19 Era

There are many applications available in the face detection algorithm but very limited applications are identified for further processing. When it comes to identifying faces in the crowd and that too in all-weather conditions then it's too difficult a task to be conducted. Considering this challenge, most of the surveillance systems are not automated. In the sense the CCTV deployed are used only for bufferingpurposes. Very rarely an event is brought to the notice and later CCTV footage is used as a tool for legal issues. There are a lot of concerns about deploying CCTV's in Public places as well. Selecting this system, our work have made sure that the CCTV's will be used to process the video being taken, and whenever an event triggers, notice(s) has to be provided for officials. Thus, the proposed system is for automatic detection and recognition of human faces for finding criminals/suspects/missing persons for surveillance. The intended method first detects a face in the video using a face detection algorithm and then it will search whether the face is present in the data centre. The method provides the ability to detect, extract features, and recognize a face from inputs taken by camera or video automatically. Recognizing faces under different natural conditions can be done by training the system on a limited number of facial images. Also, this system is tested for Covid-19 Post situations, wherein wearing masks in public places is compulsory thus face recognition in these aspects is also tested, and encouraging results have been achieved.
Hot Plug-in: Test Method and HV Active Clamps

Hot-plug -in (HPI) compatibility of HV rail-based ESD protection networks is addressed by establishing express wafer level test method followed by upgrading HV active clamps. The clamp level solution is validated experimentally by cross -comparing the novel HPI I-V characteristics and TLP I-V characteristics of the original and improved HPI tolerant clamps
Grid-Tied Three-Phase Inverter Current Control Considering Low Voltage Ride Through Capability: A Comparison between Stationary and Synchronous Reference Frames

This work presents a study and comparison of current control for three-phase grid connected voltage inverters to cope with low voltage ride through requirements. A comparison between current control loops implemented employing synchronous and stationary frames is presented and their performances are analyzed during voltage sags. Current reference generation method is discussed; under which it is possible to minimize active power oscillations during a voltage sag or inject balanced currents even in asymmetric sags. The control system developed in this work is implemented in a DSC and demonstrated operating in a 5-kW three-phase voltage inverter with an LCL filter. Experimental results are presented to validate the analyzes.
Collaborative Anomaly Detection for Internet of Things based on Federated Learning

In this paper, we propose a federated learning(FL)-based collaborative anomaly detection system. This system consists of multiple edge nodes and a server node. The edge nodes are in charge of not only monitoring and collecting data, but also to train an anomaly detection neural network classification model based on the local data. On the other hand, the server aggregates the parameters from the edges and generates a new model for the next round. This system structure achieves light weight transmission between the server and the edge nodes, and user privacy can be well protected since raw data are not communicated directly. We implement the proposed scheme in the practical system and present experimental results that demonstrate results competitive with those of state-of-the-art models.
Anthropomorphic flexible joint design and simulation

In this paper, a two-degree-of-freedom (DOF) anthropomorphic pneumatic flexible joint is designed by using a silicone based hyperelastic material. The flexible joint consists of three chambers, which are distributed at 120 degrees, and they are not connected with each other. The pneumatic flexible joint is controlled by inflating high pressure gas to its chambers. Bending properties and motion space of the pneumatic flexible joint is analysed. And the influence of the chambers’ sizes and intervals on the joint’s curvature and motion space is analyzed through a series of finite element models (FEMs). Finally, the design of the geometrical sizes of the pneumatic flexible joint is studied qualitatively. Finite element analysis (FEA), under gas pressure of 10 kPa, 15 kPa, 20 kPa and 25 kPa, respectively, showes that the designed structure could realize two-degree-offreedom motion of the pneumatic flexible joint, which makes up for the deficiency of traditional one-degree-of-freedom pneumatic flexible joints.
A Wideband Millimeter Wave Substrate Integrated Waveguide Antenna Based on Coupling Feed

In this paper, a wideband millimeter wave substrate integrated waveguide antenna based on coupling feed is presented. The antenna consists of two parts: the bottom cavity-backed coupling feed and the upper antenna patch. In order to suppress the gap backward radiation and improve the antenna gain, a backward grooved SIW cavity is designed. The width slot on the upper rectangular antenna patch expands the relative bandwidth of the antenna for 17 % to 27.7 %. The antenna works at 46.7 - 61.7 GHz. The maximum simulated radiation gain ranges from 6.89 dBi to 7.52 dBi, with good radiation characteristics.
IoT Protocol Selection for Smart Grid Applications: Merging Qualitative and Quantitative Metrics

The concept of Internet of Things (IoT) implies that objects are connected to share information with each other and/or with a platform based on standardized communication protocols such as MQTT and HTTP. Recently, their performance has received much attention due to more widespread utilization. But, the vital aspect of the adoption of these IoT protocols is not just performance per se but also their feature set. However, comparative analysis that takes into account both the features and performance aspects are rarely presented. This paper investigates a combination of a qualitative comparison model (based on communication, operational and security attributes) with quantitative metrics in terms of CPU/memory utilization and time consumption. Quantitative metrics are collected for two of the four IoT protocols. Our analysis shows that there is no onesize-fits-all solution in terms of application layer IoT protocols. Additionally we emphasize that, on a higher level, both qualitative and quantitative measures are necessary for IoT stakeholders to consider before selecting the most appropriate protocol for the specific application to be deployed. We illustrate the results using a smart grid example simulating a collection of 1000 smart meters connected to the grid.
Wavelet Based Deep Recursive Pyramid Convolution Residual Network for Single Image Rain Removal

Image rain removal aims to separate the background image from the rainy image. During the past three years, the image rain removal with deep convolutional neural networks has achieved impressive performance. However, how to reach tradeoff between high de-raining performance and low model parameters is still a challenge. To address the issue, the paper is devoted to exploring a novel method based on wavelet deep recursive pyramid convolution residual network (WDRPRN), in which discrete wavelet transform is embedded to decompose the rainy image in different frequency domains, and the deep recursive pyramid convolution residual network (DRPRN) can well predict the residual coefficients between rainy image and clean image. In addition, compared with other neural networks, the DRPRN adopts recursive model that can cost fewer parameters. Plentiful of experiments on synthetic and real-world datasets show that the proposed method is significantly superior to the recent state-of-the-art algorithms.
A hardware solution to overcome the bandwidth limitation of drone jamming platforms

Drone jamming platforms are faced with a significant constraint on bandwidth limitation compared to the broadband spectrum used by remote control radio links in view of implementing the advanced techniques of spread spectrum on these devices. In this work, we propose a hardware solution to overcome the bandwidth limitation of drone jamming platforms by using the dual AD9361 transceiver integrated on the AD-FMCOMMS5-EBZ radio prototyping platform. The hyperchaotic generator of random digital signals and the custom control architecture have been implemented on the ZC706 FPGA development board. A versatile real time chaotic signals generation IP have been developed and integrated on the FMCOMMS5-EBZ HDL reference design considering timing constraints related to the AD9361 data exchange and the AXI-LITE bus interface. The experimental results showed a coverage capacity of more than 100 MHz of bandwidth, which made it possible to guarantee an integral coverage of the WIFI band and consequently to neutralize any potential threat evolving on this band.
Mask Classification and Head Temperature Detection Combined with Deep Learning Networks

Due to the COVID-19 pandemic, wearing a mask is mandatory in public spaces, as properly wearing a mask offers a maximum preventive effect against viral transmission. Body temperature has also become an important consideration in determining whether an individual is healthy. In this work, we design a real-time deep learning model to meet current demand to detect the mask-wearing position and head temperature of a person before he or she enters a public space. In this experiment, we use a deep learning object detection method to create a mask position and head temperature detector using a popular one-stage object detection, RetinaNet. We build two modules for the RetinaNet model to detect three categories of mask-wearing positions and the temperature of the head. We implement an RGB camera and thermal camera to generate input images and capture a person’s temperature respectively. The output of these experiments is a live video that carries accurate information about whether a person is wearing a mask properly and what his or her head temperature is. Our model is light and fast, achieving a confidence score of 81.31% for the prediction object and a prediction speed below 0. 1s/image.
On Coverage Probability of Uplink NOMA in Millimeter Wave Cellular Networks

In this paper, using the stochastic geometry, we analyze the uplink performance of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) cellular networks. Our model takes account of the line-of-sight (LOS)/ non-line-of-sight (NLOS) propagation and directional beamforming. The expressions of coverage probability and network throughput are obtained. The numerical results highlight the importance of choosing network parameters to guarantee the NOMA performance. A comparison of conventional ultra-high-frequency (UHF) networks indicates that the NOMA in mmWave networks show the superiority.
Compact Sub-6-GHz MIMO Slot Antenna System

A sub-6-GHz multi-input multi-output (MIMO) slot antenna system was proposed in this study, where each antenna element was constructed using a slot structure. To effectively use the available space in a tablet and maximize the screen, we have deployed the MIMO antenna system on the left side-edge frame of the tablet. The MIMO antenna system consists of two identical dual-slot-antenna units that are separated by 10 mm. The two closely spaced slot antennas in the same dual-slot-antenna unit are arranged asymmetrically mirrored of each other with respect to the system ground plane. The isolation between its two slot antennas can be enhanced by 5 dB to meet the design requirement by using an ungrounded full-wavelength microstrip resonator.
Risk Analysis for Automotive EMC: Scope, Approaches and Challenges

Current trends in the automotive industry are towards increased electrification, automation, light-weighting, and wireless connectivity, as well as greater use of software. Software modifications could present opportunities for through-life maintenance (e.g. for cybersecurity) or for the enhancement of vehicle functionality (enabling a "software-defined vehicle" paradigm). In addition, non-deterministic artificial intelligence and machine learning techniques are increasingly used to enable automation of driving. One consequence of these developments that the value and effectiveness of traditional pre-production conformance tests are becoming increasingly questionable. Hence new vehicle EMC engineering methods will be required to accommodate these developments. It is considered that risk analysis will become an important tool in the future, but there are still significant challenges to overcome to achieve this.
Snapshot compressive spectral video via a monocular optical system

This work introduces an imaging device that efficiently captures high-speed spectral videos along with a mathematical model that allows reconstructs them from far fewer measurements than those required by conventional scanning devices. This imaging architecture modulates and multiplexes the spectral-temporal information into a single compressed measurement by introducing a Dynamic Vision Sensor (SCAMP5) as a detector in a conventional compressive snapshot spectral image (CASSI) system. SCAMP5 sensor embeds processing and data storage capability into the pixels, which allows developed a high-speed temporal codification. The results of the numerical experiments through high-speed spectral videos shows reliable performance reconstructing spectral videos for a different amount of reconstructed frames. Comparing this proposal approach of snapshot spectral video with the conventional capture of spectral videos with multishot systems, our work arises very close results additionally our system outperforme the temporal spectral compression, more fully, the proposal approach captures a 8 times less samples obtaining a difference of 2.86 in SAM, 0.08 in SSIM, 2.9 in PSNR and 0.03 for RMSE. Therefore, the proposed architecture is an efficient and alternative high-speed spectral video acquisition system.
Optimization and Design of Planar Transformer for the High Frequency Link Converter

In this paper, the analysis and experimental verification of a planar transformer for a high-frequency link converter is presented. The known problem of such topologies is strict limitations related to the transformer leakage inductance and parasitic capacitance. This problem is more pronounced in the systems with high transformer turns ratio. The current research aims to find the low-cost solution, at the same time to ensuring that the values of the parasitic parameters are in the acceptable range. The verification of different 1:10 transformer design approaches was experimentally verified with a 1 kW converter prototype.
RUL prediction of lithium batteries based on DLUKF algorithm

Lithium batteries have been widely used in various fields, but there are still some safety problems. Once an explosion occurs, people's lives and property will be damaged seriously. In order to strengthen the safety of lithium batteries, it is necessary to master the maintenance of lithium batteries technology. Accurate prediction of the remaining useful life (RUL) of lithium batteries is beneficial to the maintenance of the battery, so that its safety can be improved. The traditional unscented Kalman filtering (UKF) method has been used in RUL prediction of lithium batteries, but there are still some problems such as low prediction accuracy because of the system’s high non-linearity. In this paper, an improved unscented Kalman filtering method for predicting the RUL of lithium batteries is used. The formula for calculating the weights by particle filter (PF) is used to change the weights in the UKF, and then in order to get the state and covariance at the next moment, the changed weights are additionally used in the measurement mechanism. The new method is verified more accurately by the open source battery capacity decay data from Center for Advanced Life Cycle Engineering.
Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model’s sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.
A Cross-Layer Time Slotted Cognitive MAC Protocol

In this paper, using time slotted multiple channels scenario, a cross-layer cognitive MAC protocol is proposed. The proposed scheme senses the radio spectrum and communicates over unused frequency bands. We found that the throughput improvement depends on the transition probabilities. However, considering the past information state of the spectrum can dramatically increases the secondary user's throughput. Moreover, the impact of sensing errors on the MAC layer performance is studied and a sensing strategy, considering the false-alarm and missed-detection probabilities, is investigated. In particular, a joint sensing and scheduling scheme is presented in this work. The objective is to achieve an optimal cross-layer design, spectrum sensing at physical layer and spectrum access at MAC layer, which can enhance the performance of cognitive radio networks. The proposed scheme makes efficient decisions on which channels to sense and access to ensure maximization of the throughput of cognitive radio users. The simulation results have shown that the aggregate throughput can be improved under the constraint that the false-alarm probability does not exceed a specified level.
Clinical utility of suicide behavior and ideation measures: Implications for military suicide risk assessment.

To date, the field has been unable to provide a definitive answer on which suicide risk assessment measure or set of measures is most useful in applied clinical settings. This study pits several psychometrically sound suicide risk assessment measures against one another, to determine which tool optimally assesses the likelihood of future suicide-related outcomes over a 3-month period, in a large sample of military personnel seeking and/or referred to services due to suicide risk concerns. The risk assessment measures were (a) Columbia-Suicide Severity Rating Scale; (b) Self-Harm Behavior Questionnaire; (c) Suicidal Behaviors Questionnaire-Revised; and (d) Beck Scale for Suicide Ideation. A total of 1,044 suicidal military service members completed baseline assessments, of whom 758 (72.6%) completed 3-month follow-up assessments. The data indicate that there is no best measure for suicide risk assessment and clinicians are therefore encouraged to select the measure, from among the four studied, for use in regular practice that best suits the demands of their setting. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Membrane separation technology

Membrane separations of interest here are not to be confused with barrier separations of the type employed in the uranium enrichment plant at Oak Ridge, Tennessee. There isotopes of uranium hexafluoride were separated by the free-molecular or Knudsen flow of the gas mixture through the pores and orifices created within a porous nickel media which was sometimes referred to as a membrane. In barrier separation, an enrichment of gases of differing molecular weights is accomplished by the differing gaseous diffusion rates within the porous media. By contrast the membranes of interest here are thin, dense, continuous films, typically formed from polymers. The separation of species is accomplished by the dissolution of the gases in the polymer and their diffusion across the solid film thickness under a concentration gradient according to Fick's law. This process is commonly referred to as membrane permeation.
A comparative analysis of Machine Learning approaches for Movie Success Prediction

The success of a movie is very critical not only for the producers of the movie but also for the thousands of workers who work behind the scenes. Their livelihood depends on the income produced from the movie. The accurate prediction of whether a movie will be a hit, or a flop is a non-trivial task since it involves many unknown parameters. In this regard, the application of machine learning (ML) in predicting whether a movie will be successful, or flop can go a long way in reducing the financial burden of all the stakeholders. In this paper, the focus is on developing an application which will help in predicting the success of the movie at early stages which will help investors to invest in movie and analysis is performed on some of the patterns from the IMDb dataset. By collecting data from a lot of sources and applying various machine learning techniques, the system predicts an approximate success rate of a movie based on its success by examining past statistics from sources such as IMDb, Rotten Tomato. Experimental results demonstrate that the score in the testing phase is very impressive. The further paper also concludes predicting which set of actors or actresses are best to have the maximum profit of the movie. This exploration emphasizes the significance of prediction in the business industry. As all investments are based on these types of prediction only.