Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasensitive Superconducting Cold-Electron Bolometer Coupled to Multi-frequency Phased Antenna Array for Polarization Detection of the Cosmic Microwave Background

We describe the design and simulation of a novel dual-polarized multi-frequency singlepixel for next-generation Cosmic Microwave Background (CMB) polarization detection. The pixel consists of a phased array of planar antennas known as slot antennas coupled to millimeter-wave cryogenic detector by superconducting transmission lines. The pixel separates the incoming radiation into two linear polarizations with high polarization purity and operates at two frequencies (75 GHz and 105 GHz) as well. This completely lithographed planar technology will enable us to squeeze the size of the focal plane to avoid aberration problems. This will also enable us to realize bolometer arrays with thousands of pixels needed to meet the sensitivity requirements of future CMB polarization experiments and to achieve better resolutions and higher scan speeds. Having multiple frequencies is important to differentiate the CMB from the galactic foreground contamination. Those goals are difficult to achieve with the current technology which uses feed horn for each pixel. The detector is a superconducting Cold Electron Bolometer (CEB), which have several advantages over current technologies. The main component of the CEB is a Normal- Insulator-Superconductor (NIS) tunnel junction, which acts as a thermometer. The CEB could implement an additional spectral filtering feature, which obviates the need for external filters. This technology is a candidate for use in the European Space Agency (ESA) Cosmic Vision Program (2015-2025) to detect the B-mode of the CMB, which will be considered as an indirect detection of the gravitational waves that followed the Big Bang.
Research on Optimization design of Hydraulic Brake Cylinder processing technology for Railway Vehicle

Hydraulic braking has the advantages of small volume, light weight, compact structure, smooth braking and fast response speed, so it can achieve short distance fast braking. Hydraulic braking device is widely used in railway locomotive and vehicle. The assembly of brake cylinder is the key part of hydraulic braking device, which plays the role of spring energy storage and continuous braking force. As the parent of the assembly, the brake cylinder carries the braking force and controls the whole process of braking action. In practical application, the brake cylinder and spring seat often produce stuck phenomenon, the reason is that the brake cylinder shape tolerance requirements are very high, there is a great difficulty in the processing process. In this paper, the processing technology of the brake cylinder is optimized to improve the processing accuracy of the brake cylinder, and the assembly accuracy of the brake cylinder assembly is improved.
Active 2D Sound Source Localization using a Sample DCNN Architecture & Encoding

This paper presents how to use raw audio data (multi-channel) to train a deep convolutional neural network in an end-to-end architecture to locate active auditory sources in space. It employs a revolutionary Sample DCNN architecture and encoding approach to encode the spatial coordinates of sources, allowing for end-to-end 2D source localisation. A Transfer Learning approach is employed to train the DCNN. Using clean voice data from the TIMIT database's dialect 8, we synthesise and produce anechoic and reverberant datasets. We map the model's predictions on a 2D plane to visualise them.
Security risks in MQTT-based Industrial IoT Applications

Internet of Things (IoT) plays a crucial role in improving the quality of life. In recent years, IoT systems have proliferated in almost every industry, including manufacturing, automobiles, agriculture, and energy. IoT is the key enabling technology for Industry 4.0. This growing reliance on IoT devices piqued the interest of several adversaries attempting to gain unauthorized access to IoT systems for illicit purposes. So, it is essential to identify any potential security risks in IoT systems. Numerous Industrial Internet of Things (IIoT) applications, including wind turbines, agriculture, and warehouses, deploy hundreds of IoT devices in remote locations. These IoT devices are not physically monitored since it requires extensive human effort. Instead, the IoT devices are monitored by web applications that collect sensor data from remote devices. IoT application-layer protocols are responsible for communication between web applications and IoT devices in such large-scale IoT systems. Any communication flaw could put the entire IoT system at risk. The publish/subscribe-based MQTT protocol is a widely used IoT messaging standard. In this paper, we present a threat model and demonstrate a specific weakness in unencrypted MQTT-based IoT systems that enables an attacker to gain unauthorized access to the entire system by launching a combination of man-in-the-middle (MITM) and cross-site scripting (XSS) attacks. We also discuss steps to be taken and future directions for research in the security of industrial IoT systems using the MQTT communication protocol to avoid the possibility of such attacks.
Super-resolution of infrared images based on hierarchical distillation network

Aiming at the problem of low image resolution that easily occurs in the process of infrared images acquisition, this paper proposes a novel hierarchical distillation network to achieve infrared images super-resolution. By designing a cascaded residual distillation module, the negative impact of the over-deep network model is reduced; meanwhile, a dual-path feature fusion module is constructed to further enhance the feature expression capability of the network model. Experiments were conducted on public datasets and evaluated using two evaluation metrics, Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM). The experimental results show that the method in this paper improves 1.97 and 0.033 in PSNR and SSIM, respectively, compared with RCAN, and generates images with high definition, strong structure and rich detail information.
Coordinated Variation of Precipitable Water Vapor and Rainfall during Typhoon

Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) has been applied in numerous meteorological researches due to its high accuracy and all-weather support. This paper explores the coordinated variation characteristics between the PWV and actual precipitation during typhoon "Chanthu. " The PWV series, from ten stations of Shanghai Continuously Operating Reference Stations (SHCORS) during Sep. 10th to 14th, 2021, were retrieved by real-time Precise Point Positioning (PPP) technology. We also collected the rainfall records from eleven meteorological stations during the same period. The results show that PWV content was significantly correlated with precipitation, i.e., PWV initially surged before rainfall, then remained stable during rain, and finally fell back to the normal level after rain. Additionally, the spatial changes of PWV could also reveal the water vapor (WV) transport path during the typhoon. It suggests that real-time PWV has the potential capacity for extreme precipitation warnings. The above findings indicate that the CORS system can equip the rainfall warning module to expand its further service field in the future.
Automatic Cephalometric Analysis using Machine Learning

Orthodontics is a specialized dental profession that specializes in diagnosing, preventing, and correcting teeth and jawbone, as well as biting patterns. The irregular shape of the teeth and jaws is very common. About 50% of the population of the developed world, according to the American Association of Orthodontics, has malocclusions heavy enough to benefit from orthopedic treatment. Cephalometries is often used by dentists as a tool for diagnosis and treatment planning and evaluation. Cephalometries aids in orthodontic diagnostics by empowering the study of skeletal structures, teeth, and soft tissues of the craniofacial region. Cephalometric analysis has many applications, including diagnostics, the definition of face measurement patterns, planning of orthodontic and orthognathic treatments, monitoring changes due to ageing or treatment and prediction of orthodontic and orthognathic treatment outcomes. Machine-based software is the only solution to reduce the dentist's work of planning and evaluation. Although some software are available for cephalometric analysis but, they are expensive and not easy to use as it requires heavy hardware-based tools such as laser guns and cephalostats. The proposed model uses a decision tree to develop a diagnostic program based on the data of previous patients assigned to it. This model is implemented using python language libraries such as Tkinter, Opencv, Sklearn, PIL and Pandas.
Trans-Border Trade, Minerals, and Civil War Impacts on Land Use and Land Cover Change in GOMA, Eastern Congo: An Integrated Geospatial Technologies and Political Economy Approach

This study analyzes land cover and land use change in the territory of Goma, Eastern Democratic Republic of Congo by leveraging the geospatial technologies capabilities (Remote sensing, GIS, and Cartography) and the analysis of existing literature on the political economy of war. The Normalized Difference Vegetation Index (NDVI) derived from Landsat Enhanced Thematic Mapper plus images were used to detect changes between 1987 and 2010. Results suggest that changes in the landscape of Goma are attributable to institutional factors such as the trans-border trade networks, the refugee influx of about 1.5 to 2 million due to ethnic conflicts in Rwanda, the Congolese civil wars, and the operations of international nongovernmental organizations and humanitarian agencies. An increase in non-vegetation areas is evident in the territory of Goma. Overall, economic opportunities, humanitarian and NGOs operations and mineral trade create more wealth in the region. This is evidenced by massive investments in real estate and unusual population growth. The study expands the applicability of both geospatial techniques and political economy by shedding new lights on the interplay among war, minerals, and land cover and land use change.
Automated Parsing of Credit Reports Integrating Banking Knowledge in Low-Resource Scenarios

Credit reports contain a large amount of unstructured and valuable information on risk. However, extracting and applying the information to the credit business is costly due to the professionalism of banking and the limitation of sample quantities. Combining natural language processing technology with the knowledge in the vertical field of bank credit, this paper aims to find an efficient way to extract these risk factors. Referring to the credit risk assessment system, this paper selects partial risk elements in credit reports, including the actual controller profile, industry and operation analysis, and parses them into structured data. We construct a Chinese NER (Named Entity Recognition) model based on Lexicon-Fusion by incorporating the lexicon information into the pre-trained language model, ALBERT. The experimental results show that Lexicon-Fusion can increase the F1 value of the NER model by 3.61 % and 1.86% in the two tasks of this paper, which reveals the feasibility of extracting key information from credit reports in low-resource scenarios. The Lexicon-Fusion method lessens the error-prone problem of key information boundaries. This work contributes to speeding up the automation of credit approval.
Policy Text Classification Algorithm Based on Bert

With the development of the Internet, the policy text classification model of deep learning is used to improve the effect of policy text classification, and to play and use the huge value contained in the policy text. In order to more accurately determine the policy field described by the text, a BERT-based policy text classification algorithm is proposed. First, the algorithm uses the BERT (Bidirectional Encoder Representations from Transformers) pre-trained language model to vectorize the sentence-level feature of the policy field text, and then the obtained feature vector is input into the classifier for classification, and finally the homology policy field is used. The text data set is verified. The experimental results show that the classification of the trained model on the test set recorded the highest F1 value of 93.25%. It is nearly 6% higher than the classification task of the BERT model for the MRPC task. Therefore, the proposed policy domain text classification algorithm can more accurately and efficiently judge the domain of the policy text, which is helpful for further analysis of the text data in the policy domain and extract more valuable information.
Review on Automatic Covid-19 Detection from Chest CT Images using Artificial Intelligence

The corona virus is one of the widespread diseases affected globally. The corona virus was first found in Wuhan, China in December 2019. The disease caused by the corona virus is named Covid-19. Covid-19 can be diagnosed by laboratory testing and chest radiography method. Due to limitations of laboratory testing methods like RT-PCR (Reverse Transcript polymerase Chain Reaction) for Covid-19 detection, medical image-based techniques have been proposed by various researchers. In this paper, various papers were reviewed that used machine learning and deep learning methods from Chest X-rays and chest CT for Covid-19 diagnosis. The overall performance of these methods is measured in terms of accuracy, sensitivity, specificity, and F1 score.
Heart Rate Prediction from Human Speech using Regression Models

The heart and the mind are intimately connected. Negative states of mind, including depression, anxiety, loneliness, anger, and chronic stress, may increase the risk for heart disease over time or worsen heart issues that already exist. Heart Rate (HR) monitor is a term used to describe a device that monitors and records the HR in real time. Electrode leads linked to the chest were utilized in the early types of HR monitors. We thought of whether and if possible, the way we speak can be used to predict HR. As we know, speech is affected by the emotional environment so we can not only rely on the basic classification of speech so, we need to consider other factors to analyze and predict. This is where the spectral features come into the picture, one of them being MFCC. So, we move away from a conventional model to prove that your voice does depict and give what your HR is. Voice-based detection of HR is attractive because it does not require any additional sensors. In contrast to previous studies, we use real-time speech data as input. Regression is used when we can have the output over a range of values of the class label. In our case, it is the heart rate that can vary over a range of 60 to 130 in ideal conditions. We decided to compare various algorithms under regression and find the one with the best accuracy Results using random forest show modest but significant effects on HR predictions.
Intelligent OpenFlow Switch for SDN Networks Based on COVID-19’s High Network Traffic using Heuristic GA-Fuzzification Control Approach

The COVID-19 outbreak has impacted network operators and data centers in terms of congestion and high traffic that lead to outages and significant pressure on the network. The overhead traffic is generated from web, voice calls, and Internet activity. In this paper, we are investigating data center congestion control for Software Defined Networks (SDN) network data centers. A Software-Defined (SDN) data center is an emerging networking paradigm that simplifies the network architecture by decentralizing plane functionality into a single with centralized decision capabilities. Along with the SDN paradigm, there is a crucial part that is responsible for forwarding packet called OpenFlow switching engine. In a typical SDN environment, the rules are initiated by the SDN controller and pushed to the OpenFlow switches. The traditional OpenFlow switch has no forwarding decision and depends on the incoming policies from the controller’s southbound interface. Additionally, the flow of traffic is initiated from different sources that are assigned to a specific route. However, this significant flow of traffic due to COVID-19 can lead to congestion and degradation of network performance in terms of delay and interruption. To be precise, a single OpenFlow switch could receive a capacity of traffic that floods its forwarding table and lead to link flaps and outages. In order to optimize the OpenFlow switch with regards to how much traffic it can host and to adjust routing capabilities for dynamic changes in the network, we propose an optimized OpenFlow congestion control and fault prediction framework for inbound traffic to overcome the inefficient route planning in the network. The proposed developed optimization algorithm is based on Genetic Evolutionary Algorithm criteria and adds intelligence to the OpenFlow switch by the adoption of Fuzzy Logic prediction capabilities. The experimental evaluation shows that the proposed optimization method adds significant intelligence and optimization to OpenFlow operation. The testbed was implemented experimentally using Raspberry Pi (RPI)cluster with customized SDN and OpenFlow deployment. The probability of the best fitness was 14.11% for Gen 999. The proposed approach adds intelligence and prediction into the OpenFlow switch to overcome the unstable flows of traffic and to predict faults to enhance the traffic capacity levels and manage flows into an entirely uninterrupted production environment.
Pulse Density Modulation Controlled WPT Charger for LEVs

Light Electric Vehicles (LEV) are increasingly used today to provide mobility in crowded cities. The charging process of LEVs has been one of the issues that should be paid attention to. For this purpose, LEVs owned or rented by users need to be charged quickly and reliably. By using Wireless Power Transfer (WPT), this needed quick and reliable charging can be achieved for LEVs. In this paper, Pulse Density Modulation (PDM) is used for charging in Constant Voltage (CV) mode during wireless charging of LEVs with WPT. The simulation study required for CV charging was carried out with MATLAB/Simulink. A WPT system has been proposed that produces a constant output voltage in response to changing equivalent load resistance in CV charging mode.
Fano Bounds for Compact Antennas. Phase I

Abstract : Impedance matching is a canonical problem in electrical engineering -- design a matching network that transfers maximum power from a generator to a load. Although the loads in this report are restricted to compact antennas, the mathematical exposition applies to any lumped load. Finding a good matching network is a diffcult numerical optimization problem. Therefore, techniques that compute bounds on the matching provide valuable information. For example, the Fano bounds discussed in this report bound the matching performance as a function of the frequency band. Therefore, the circuit design can see the matching performance against the bandwidth to make design decisions without having to solve countless matching problems. Moreover, the Fano bounds provide an excellent benchmark to assess the performance of actual matching circuits getting close enough to the best bounds is good enough. The Fano bounds are computed by maximizing the matching performance under inequality constraints. The inequality constraints are determined by analytic expansions of the load. Because symbolic manipulators compute these expansions and the numerical packages can solve the inequality constraints, the Fano bounds may be amenable to a hands-free computation. However, the sticking point for the Fano bounds is the requirement that the load be given in the Darlington representation. Therefore, this Phase I effort shows that the Fano bounds can automate and defers the Darlington computation to Phase II.
Vaccine Cold Storage Monitoring and Tracking using LoRaWAN

This paper proposes a methodology for a cold storage monitoring and tracking system built upon a LoRa network. The architecture comprises an end node with a monitoring system, a LoRaWAN Gateway, LORIOT network server and the user application. The end node has built-in temperature, pressure and humidity sensors. Force-Sensitive-Resistors and a GPS module are interfaced externally to the module. The monitoring system can thereby measure the parameters inside the chamber as well as track the chamber in real time. Further, the presence of vaccines is detected using the force-sensitive-sensors. A web application has been designed to provide the user with an interface to analyse the network parameters, receive data from the end node, issue signals to the end node and track the cold storage during transportation.
BERT-Based Mixed Question Answering Matching Model

Compared with the current question answering system, similarity matching methods are largely separated into two categories: deep learning methods and conventional ways. Conventional ways rely heavily on artificial features, have weak generalization ability, and insufficient accuracy. RNN and CNN also have text global feature extraction. Limitations. This paper proposes a BERT-based hybrid question answering matching model, which uses the BERT -base pre-training model to capture and represent the semantic information of the QAS sentence and the semantic relevance between the two. The feature vector generated by the BERT model is used as Bi-LSTM _ GCN for the input of the model, feature extraction is performed to further obtain the syntactic features of the sentence, and finally the attention mechanism is added to find the target answer, and the effectiveness of the proposed algorithm is verified on the two types of data sets.
An Airborne SAR Radiometric Calibration Method with Random Constrained Evolution

Due to the passive corner reflector widely used in airborne SAR calibration is greatly affected by the flight attitude of airborne platform and the process of manufacturing, processing and laying out the corner reflector. An airborne radiometric calibration method with random constrained evolution is proposed in this paper. Based on the principle of random sampling consistency, this method can iteratively extract the internal points of corner reflector samples for effectively avoiding the impact on the accuracy of conventional radiometric calibration methods when there are abnormal points in corner reflector samples. Finally, the experiment of X-band SAR data is applied to verify the effectiveness of the proposed method.
The Safety Status and Foreign Object Detection Model of Tobacco Cabinets Based on Deep Learning with IoT-Cloud

Virtual reality and Internet of Things (IoT) are widely used in various fields ranging from smart healthcare to smart factories. Among these systems, it is indispensable to obtain security, productivity, and other aspects of verification through the input data. We propose to detect the production status of tobacco cabinets in real-time and the safety detection of foreign objects in cigarette cabinets based on deep learning with the Internet of Things environment, and build foreign object detection and production state detection data sets and monitoring models. In the actual detection, the detection accuracy for different foreign objects reaches 97.75%; the accuracy for the feeding in materials status of the tobacco cabinet reaches 96.36%; the accuracy for the detection of the status of the tobacco cabinet conveyor belt is 91.76%; the overall detection time was less than 1 second. The proposed method has important practical significance for the safety, well-being, and efficient production of cigarette factories.