Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Loss and Wideband Quadrature Hybrid Coupler for Millimeter-Wave Attenuators

In this paper, a novel quadrature hybrid coupler, composed of parallel-stripline (PSL) and a 180-degree phase inverter, is employed for millimeter-wave attenuator design. For verification, a reflection-type attenuator using this hybrid coupler has been implemented in a 0.13-μm SiGe BiCMOS process. The different attenuation levels of 0 dB (reference state), 2 dB, 4 dB, and 8 dB are achieved by adjusting the reflective loads. Capacitive compensation is used to improve the phase variation at high attenuation levels of 8 dB. The proposed attenuator demonstrates a simulated insertion loss (in the reference state) of less than 1.5 dB, a phase shift of less than 3.2 degrees, and an input/output return loss better than 15 dB within the operating frequency range of 170 GHz to 200 GHz.
Design of Millimeter Wave Radar Broadband Microstrip Antenna for Blast Furnace

In this paper, a wideband microstrip array antenna based on millimeter-wave radar is proposed, to achieve strong anti-interference ability and high resolution for detecting the burden surface of blast furnaces. The antenna consists of a Chebyshev main radiation patch array and a button-shaped coplanar parasitic element array. The coplanar parasitic elements alter the coupling between antenna patches to widen the bandwidth. It improves the limitation of the angular resolution of the previous millimeter-wave array antenna due to the problem of insufficient bandwidth. Simulation results show that the −10 dB impedance bandwidth is 7.89 GHz and the antenna main gain reaches 13.92 dBi. The antenna is suitable for high-gain and high anti-interference millimeter-wave radar systems and has greatly reduced dimensions of approximately twenty millimeters, making it applicable for placement in the protective cabin of a blast furnace detection system. It has great potential for solving issues related to the impact of internal environments on blast furnace operations.
Application of Remote Sensing in Determining Particulate Matter Pollution

This research is aimed at modelling Particulate Matter (PM2.5) using satellite remote sensing data, ground measurements and weather data. Using the linear mixed effect model high PM2.5 concentrations were predicted near traffic routes, urban centers, industrial zones and informal urban settlements. Along Jogoo road concentrations ranged from 36 to 39 $\mu$g$m^{-3}$, along Thika road (36 to 43 $\mu$g$m^{-3}$), in Viwandani area (41 to 45 $\mu$g$m^{-3}$), in Mukuru area (38 to 43 $\mu$g$m^{-3}$) and near Kibera (38 to 42 $\mu$g$m^{-3}$). The low PM2.5 concentrations are predicted near forested areas such as near Karura Forest (24 to 31 $\mu$g$m^{-3}$) and the Nairobi National Park (31 to 35 $\mu$g$m^{-3}$).
Difficulties in Implementing a Heavy-tailed Distribution Using the Example of the Pareto Distribution in Modeling Network Traffic

The features of computer simulation of network traffic represented by the Pareto distribution are discussed. Difficulties in the software implementation of a Pareto-distributed random variable generator are presented. The first problem is the poor convergence of the first and second moments of the Pareto distribution obtained experimentally to their theoretical values. The second problem is to determine the number of experiments to obtain the convergence of empirical moments to theoretical ones. It is proposed to use an analytical-statistical approach in assessing small probability of losses in a network node. The essence of the approach is to substantiate and apply a suitable approximation of the dependence of the probability of losses on the volume of the buffer storage of the network node.
Network Attack Detection Based on Data Mining Methods

In article, the detection of above pounce upon supported on collections Mining undergrounds was studied, including the detection of pounce upon using a clandestine Markov model, detection of pounce upon using theorem networks, detection of pounce upon using bunch methods, detection of pounce upon using the facilitate agent method, detection of pounce upon using neuronal networks, detection of pounce upon using transmissible algorithms, detection of pounce upon using fleecy scientific reasoning rules. The pointers of tone-beginning detection in indefinite studies are precondition in the table.
Development of Intelligent Algorithms for Interpretation of Images of the Remote Image of the Earth

The paper considers the problems of developing intelligent algorithms for interpreting images of a remote image of the earth, since the automatic interpretation of these images becomes relevant. In this context, reference datasets serve as important prerequisites for developing and testing intelligent interpretation algorithms. The paper discusses the problem of how to efficiently prepare a suitable reference dataset for the interpretation of remote sensing images. The paper discusses the problem of classification of remote sensing images using the Convolutional Neural Network.
Combination of Spatial Multiplexing and Non-orthogonal Multiple Access NOMA

Large number of publications are devoted to non- orthogonal multiple access technology which is one of the main candidates for inclusion in the new international standards of wireless communication systems. The introduction of new multiple access technology brings with large number of unsolved problems in the field of signal processing. One of this is combining new multiple access technology with already well- established technologies, such as multiple antenna MIMO systems. The main goal of this work is to show possible combinations of MIMO and NOMA technologies. The results of computer simulations provide us to evaluate gain from the joint use of these both technologies.
Novel Approach to FDSOI Threshold Voltage Model Validated at Cryogenic Temperatures

The paper presents a novel approach to the modeling of the back-gate dependence of the threshold voltage of Fully Depleted Silicon-On-Insulator (FDSOI) MOSFETs down to cryogenic temperatures by using slope factors with a gate coupling effect. The FDSOI technology is well-known for its capability to modulate the threshold voltage efficiently by the back-gate voltage. The proposed model analytically demonstrates the threshold voltage as a function of the back-gate voltage without the pre-defined threshold condition, and it requires only a calibration point, i.e., a threshold voltage with the corresponding back-gate voltage, front- and back-gate slope factors, and work functions of front and back gates. The model has been validated over a wide range of the back-gate voltages at room temperature and down to 3 K. It is suitable for optimizing low-power circuits at cryogenic temperatures for quantum computing applications.
Deep perceptual similarity and Quality Assessment

Measuring the perceptual similarity between two images is a long-standing problem. This assessment should mimic human judgments. Considering the complexity of the human visual system, it is challenging to model human perception. On the other hand, the recent low-level vision task approaches, mostly based on supervised deep learning, require an appropriate loss for the backward pass. The per-pixel loss, such as MSE and MAE, between the output of the network and the ground-truth images were among the first choices. More complicated and common similarity measures in which the error is computed in a hand-designed feature space are also employed. Furthermore, Deep Perceptual Similarity (DPS) metrics, where the similarity is measured in the deep feature space, also have promising results. This feature can be selected from a pre-trained or optimized model for the task at hand. Recently many studies have been conducted to thoroughly investigate DPS. In this research, we provide an in-depth analysis of the pros and cons of DPS in assessing the full reference quality assessment. In addition, to compare different similarity measures, we propose a metric which aggregates various desired factors. Based on our experiment, it can be concluded that perceptual similarity is not directly related to classification accuracy. It is discovered that the outliers mostly contain high-frequency elements. The code and complete outcomes described in results, can be found on: https://github.com/Alireza-Khatami/PerceptualQuality
A Compact Four Elements Self-Isolated MIMO Antenna for C-Band Applications

This study introduces a four-element MIMO antenna that is self-isolated and appropriate for use in C-band applications for satellite communication. The antenna consists of a cross-shaped slot positioned in the center of the antenna along the chords and four T-shaped slots placed on the four main sides of the patch. The design process involved using a 3.2 mm thick FR4 substrate with a relative permittivity of 4.4 and optimizing the antenna dimensions before fabrication. The slots on the patch are responsible for creating the desired impedance bandwidth and generating remarkable isolation between the ports. Therefore, the antenna structure does not require additional elements for decoupling purposes. The experimental results show that the proposed MIMO antenna exhibits a broad impedance bandwidth ranging from 6.62 to 7.12 GHz, isolation better than 15.3 dB, ECC less than 0.008, TARC less than −5 dB, and realized peak gain of 5.77 dB.
Doppler Principle Based Situational Awareness Method for Autonomous Driving

Situational awareness is a critical connection in autonomous driving, which is now based on a combination of technologies. Camera, lidar, radar and ultrasonic sensors are the most commonly used modality for this task. Among them, lidar has the highest accuracy within 200 m and becomes the main input module for environmental perception. Nevertheless, lidar has defects of large size, high cost, high energy consumption, and accuracy is affected by the weather environment. To overcome the challenge, this paper proposes a situational awareness method for autonomous driving based on the Doppler principle, which is a help and supplement to the onboard radar. Specifically, five 24 GHz speed sensors are used to measure the Doppler speed of the vehicle in relative motion on the horizontal road surface, and the three-dimensional position coordinates of the vehicle and the two-dimensional speed of the horizontal plane can be calculated by a joint observation. Another 24 GHz speed sensor assists in distinguishing each target, judging the vehicle model, and predicting the possible movement of nearby vehicles. Experiment results illustrate that the measurement errors of the prototype system designed according to this paper are in line with experiment expectations, which proves the feasibility of Doppler radar for situational awareness.
Empirical Mode Decomposition Based Morphological Profile For Hyperspectral Image Classification

The empirical mode decomposition (EMD) based morphological profile (MP), called as EMDMP, is proposed for hyperspectral image classification in this work. The EMD algorithm can well decompose the nonlinear spectral feature vector to intrinsic components and the residual term. To extract the main spatial characteristics and shape structures, the closing operators are applied to the intrinsic components. In contrast, to extract details and more abstract contextual features, the opening operators are applied to the residual component. Finally, a multi-resolution morphological profile is provided with concatenation of the intrinsic components-based closing profile and residual component based opening profile. EMDMP achieves 96.54% overall accuracy compared to 95.15% obtained by convolutional neural network (CNN) on Indian dataset with 10% training samples. In University of Pavia with 1% training samples, EMDMP results in 97.66% overall accuracy compared to 95.90% obtained by CNN.
Analysis of Human Brain Signal Rhythms Through a Bitalino Device

this article discusses methods based on the EEG signal when creating brain-computer interface (BCI) applications and systems. In this, the stages of creating BCI systems with an EEG signal approach are explained in detail. The main aspect of the article is the creation of BCI systems by minimizing EEG signal recording electrodes.
Converge intra-class and Diverge inter-class features for CNN-based Event Detection in football videos

Football event detection in videos is very challenging, but challenges on the Penalty and the Free-kick, which have common visual elements, are severe and critical. The existence of common elements between two events causes the extraction of common and ineffective features in recognizing these two events. As a result, the error of recognizing and separating these two events is more than other events. In this paper, we present a new method for filtering the input data to converge the intra-class features and diverge the inter-class features to increase the classification accuracy. For this purpose, using the IAUFD Dataset, we have evaluated images for the Penalty and the Free-kick classes with the criterion of structural similarity. Based on the results, inappropriate images have been ignored according to the average value and standard deviation of each class of data. This filtration leads to ignore of ineffective and common features in the learning process. The results of the proposed method indicate an improvement in the accuracy of distinguishing between two Penalty and Free-kick events using a deep neural network and filtered training images compared to the deep neural network using all training images.
Edit-History Vis: An Interactive Visual Exploration and Analysis on Wikipedia Edit History

We propose Edit-History Vis, a visual analytics system designed to facilitate interactive exploration on Wikipedia edit history at a fine-grained level. The examination of detailed changes in Wikipedia articles is crucial for understanding how authors’ perspectives vary and conflict during the collaborative editing process. However, it is challenging to reveal the details while preserving the heterogeneous attributes of revisions, namely the time, content, and editor. The Edit-History Vis system integrates editor and textual changes of revisions by utilizing a force-directed revision graph that groups revisions based on standpoints. Through this revision graph, users can identify and analyze editing events such as edit wars, vandalism, repair, and normal updates. The effectiveness of the system in analyzing the edit history is validated through a qualitative comparison with prior work and a quantitative rating from a user study.
Dynamic overlapping community detection algorithm based on topic segmentation

The study of dynamic overlapping semantic communities is an important research content for people to analyze the development pattern of events, monitor the trend of public opinion and commercial personalized recommendation. However, existing dynamic community detection algorithms ignore the influence of attributes on community structure, which not only affects the accuracy of community structure but also fragments the relationship between community and topic, and the similarity of topics within the community is not high. Therefore, this paper proposes a dynamic community detection algorithm based on topic division (DOCTS), which uses node attribute features to make topics, combines the network structures of adjacent moments to get weighted subnetworks, then uses Louvain algorithm to detect weighted subnetworks to get topic sub-communities, and finally, merges and optimizes the communities that will have high similarity to get overlapping topic communities. It is verified in real dataset Facebook that the algorithm can effectively detect communities in dynamic networks and also analyze the community structure based on topic evolution.
Space Vector Pulsewidth Modulation Strategy for ANPC-5L Inverter Based on Model Predictive Control

Space vector pulsewidth modulation (SVPWM) strategies for active neutral-point-clamped five-level (ANPC-5L) inverters have multiple control degrees of freedom and control objectives. However, traditional SVPWM strategies are hard to realize optimal control of multiple objectives as the objectives are coupled with each other. To address this problem, an SVPWM strategy based on model predictive control (MPC) is proposed. In the pulse generation process of the proposed SVPWM, all control degrees of freedom are utilized to achieve optimal control of multiple objectives through MPC. First, voltage vector sequences that meet the requirements of common-mode voltage (CMV) are selected. Then, a cost function is constructed to realize optimal control of the neutral point potential (NPP) and three floating capacitor (FC) voltages. The proposed strategy has advantages such as high control accuracy and producing low CMV. Finally, the effectiveness of the proposed strategy is validated by a 150-kW three-phase ANPC-5L inverter prototype.
Approximation Algorithm Applications in Artificial Intelligence: A Review

The function of this study is devoted to aspects of approximation algorithms such as approximation problems and showing the relationship between approximation order, linear classification display, dimensionality reduction and nested matrices using comparison between models prepared in the search to reach the approximate model that helps in using activation that increases monotonously to provide an approximation similar to the wavelet technique. On how the approximation algorithm could permeate the computer science literature within the development of a network design that met a special need for communication with endpoints via Buy-at-Bulk technology, which is the most impressive class of application of network communication by approximation algorithm using artificial neural networks (ANNs), which are computer models that are a contour of the nervous system response to human and animal brains through the psychological theory known as cognitive consistency model.
Importance of Depolarization Current in the Diagnosis of Oil-Paper Insulation of Power Transformer

Recently, Polarization Depolarization current (PDC) measurement is widely accepted time domain spectroscopy-based method for assessing the insulation condition. Various performance parameters like Dissipation factor (%tan $\delta $ ), Paper Moisture (%pm), Dielectric Adsorption Ratio (DAR), Polarization index (PI) etc. can be estimated by analyzing the PDC data. During field measurement various factors influences the recorded PDC data. As per existing literature, presence of low frequency noise, effect of temperature variation and influence of residual charge are common during field measurement. These factors significantly affect recorded polarization current and hence estimated performance parameters. Hence, analysis using recorded polarization current data may provide misleading information regarding insulation condition. Under such practical situation where polarization current is affected by above mentioned factors that generally observed during field measurement, depolarization current should be used for analysis of insulation condition. The depolarization current does not influence by such external factors. The present work shows the importance of depolarization current where polarization current is influenced by external low frequency noise and residual charge. The analysis firstly applied on sample prepared in the laboratory and then on data collected from real life in-situ transformers. The results obtained from the analysis shows that the data obtained from depolarization current is more reliable.
Real-time Personalized Guest Experiences using Product Attribute Information on Vanilla Hardware

User experience in digital retail critically depends on high-quality personalized recommendations. A large pool of guests, a large catalog of items (millions of items) and real-time low latency requirements for recommendations make such systems difficult to design and implement. In this paper, we discuss our early experiences in building and deploying a real-time inferencing framework (with 95 percentile latency ≤50ms) by deploying microservices framework on vanilla hardware and serving thousands of recommendations per second. Our recommendation framework builds on the general knowledge that purchase of a product by a guest is ultimately driven by its attributes such as color, brand, flavor, size or price. While this information is highlighted in product pages and all personalization algorithms tend to use implicit attributes and attribute similarities to recommend products to the guests, this information is not directly utilized to empower personalized guest experiences. We built a Guest’s Attribute Preferences (GAP) model that scores a guest’s preferences to attributes to improve product recommendations in app and website. Several AB tests reveal that this results in significant improvement in incremental interaction and revenue (upto 3x improvements) over hadoop-based production system recommendations in different use cases. We also discuss system aspects of GAP, where a single pod can execute hundreds of recommendation requests per second with 50 millisecond latency. Today, systems backed by GAP contribute to significant user engagement with product recommendations in Target’s website and App.
Eco-friendly Green Biosynthesized Metallic Nanoparticles and Biotechnological Applications in Pharmaceuticals Sciences

The next years will prove the importance of greensynthesis methods for MNPs and MONPs production because they are not only easy to execute, fast, and cheap but also less toxic and environmentally ecofriendly. Nanoparticle synthesis using microorganisms and plants by green synthesis technology is biologically safe, cost-effective, and environment-friendly. Plants and microorganisms have established the power to devour and accumulate inorganic metal ions from their neighboring niche. The biological entities are known to synthesize nanoparticles bothextra and intracellularly. The capability of a living system to utilize its intrinsic organic chemistry processes in remodeling inorganic metal ions into nanoparticles has opened up an undiscovered area of biochemical analysis. Metal nanoparticles (MNPs) and metal oxidenanoparticles (MONPs) are used in numerous fields. The new nano-based entities are being strongly generated and incorporated into everyday personal care products, cosmetics, medicines, drug delivery, and clothing toimpact industrial and manufacturing sectors, which means that nanomaterials commercialization and nanoassisted device will continuously grow. They can be prepared by many methods such as green synthesis and the conventional chemical synthesis methods. The green synthesis of nanoparticles (NPs) using living cells is a promising and novelty tool in bionanotechnology. Chemical and physical methods are used to synthesize NPs; however, biological methods are preferred due to its eco-friendly, clean, safe, cost effective, easy, and effective sources for high productivity and purity. Greensynthesis includes infinite accession to produce MNPs and MONPs with demanding properties. The structure–function relationships between nanomaterials and key information for life cycle evaluation lead to the production of high execution nanoscale materials that are gentle and environmentally friendly. Majority of plants have features as sustainable and renewable suppliers compared with microbes and enzymes, as they have the ability to pick up almost 75% of the light energy and transform it into chemical energy, contain chemicals like antioxidants and sugars, and play fundamental roles in the manufacture of nanoparticles. Plants considered the main factory for the green synthesis of MNPs and MONPs, and until now, different plant species have been used to study this, but the determined conditions should be taken into consideration to execute this preparation.