Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced FEC for 200 Gb/s Transceiver in 800 GbE and 1.6 TbE Standard

200 Gb/s transceiver specification is under development in IEEE 802.3 as an essential technology for 800 GbE and 1.6 TbE, including electrical and optical interconnection applications. Both IM-DD and coherent modulations are being considered for optical solutions. Due to the higher rate transmission and more stringent requirements, advanced FEC beyond RS (544, 514) code used in 100 Gb/s transceivers is most likely to be mandatory. To enable 200 Gb/s transceivers for 800 GbE and 1.6 TbE, this article investigates on FEC coding gain performance, latency, and area from logic layer perspective.
Implementation of Convolutional Neural Network for Artifact Removal from EEG Signals

Artifacts that occur during the registration of electroencephalographic signals (EEG) pose a significant problem, hindering the analysis of these signals for medical diagnosis or brain-computer interfaces (BCIs). While some artifacts can be relatively easily removed, others, such as those related to muscle activity, are more challenging to eliminate. Continuous research is being conducted to discover methods for removing physiological artifacts resulting from the natural activity of the individuals. In this article, the authors propose a method for removing electromyographic (EMG) artifacts from EEG signals using convolutional neural networks (CNNs). To accomplish this, a method for augmenting EMG and EEG signals is proposed. The authors performed artifact removal tests using a trained CNN for both real and simulated signals. The obtained results indicate that the proposed method shows promising outcomes and enables effective removal of demanding artifacts, such as jaw clenching.
Trustworthy AI: A Fuzzy-Multiple Method for Evaluating Ethical Principles in AI Regulations

In this study, we investigated the ethical principles of trustworthy AI and differentiated five prime factors essential for developing trust in AI and most widely presented in regulatory guidelines worldwide. By utilizing Fuzzy Logic Toolbox in MATLAB 9.4, we evaluated the impact of primary ethical principles on trustworthy AI systems in a systematic and structured manner. We discovered that the principle of Fairness and Non-discrimination is the most influential for the development of trustworthy AI, as it is the most represented in the regulatory guidelines. The proposed model offers two main benefits for developers and deployers of AI systems, including predicting the potential public trust in AI systems and assessment compliance with the regulatory frameworks. To ensure the continued trustworthiness of AI systems, the model should be used at all stages of the software life circle, including during development, before placing the system on the market, and at the stage of use to monitor compliance with the safeguards declared to users.
Deploying Virtual Environments for Defense in Control Systems

Facility control systems networks are highly vulnerable to malicious attacks, and these attacks have been on the rise in the past few years. Although there is a need to protect these environments, there is a lot of difficulty in testing and evaluating possible solutions on real environments. In order to help facilitate cybersecurity research for control systems networks, an ongoing effort in the U.S. Army seeks to virtualize these environments with high fidelity. This work details the efforts that have been in place in modeling most aspects of any facility's network, from its control systems network to the enterprise network, as well as providing a means for data generation and collecting, network traffic monitoring, and adversarial scenario automation. This work highlights a major evaluation effort for a redacted zero trust networking solution and shows the power that virtualization of facilities can bring to cybersecurity research.
Efficient Artificial Neural Networks Model for the Layout of Platform-based dual-Antennas Mutual Coupled

To expedite the design process of the platform-based dual-antenna mutually coupled layout, the inverse artificial neural networks(ANNs) model is proposed, which can directly generate the layout to match the expected electromagnetic response. In this ANNs model, the two antenna coupling performance S-parameter is regarded as the input, and the corresponding discrete points are the output. Once the training is completed, the proposed inverse model can provide the corresponding antennas located point directly without an optimization process. In the last, two examples are presented to demonstrate the accuracy and efficiency of the proposed method.
Compact Substrate Integrated Waveguide Bandpass Filters with Wide Stopband

This paper presents two compact wide stopband filters using Complementary Split Ring Resonators (CSRRs) and improved Defected Ground Structure (DGS) based on Substrate Integrated Waveguide (SIW) technology. By loading CSRRs on the top metal layer of the substrate, the resonant frequency can be decreased, thus the size of the filter can be reduced. In order to suppress the high-frequency interference outside the dominant mode, improved Defected Ground Structure is cascaded with the SIW resonator so that the stopband can be widened. Compared to conventional DGS, improved DGS can maintain excellent in-band and out-of-band performance simultaneously. Since the transmission characteristics of the Half Mode SIW are the same as those of the full mode SIW, the size of filters can be further reduced. To validate the method, two compact filters are designed and fabricated, the measured results are in good agreement with the simulated results.
Dual-Frequency Shared-Aperture Fabry-Perot Cavity-Integrated Monopole Antenna

In this paper, a dual-frequency shared-aperture antenna working at 3.15-3.85 GHz and 11-11.8 GHz is designed based on the structure reused technology. Fabry-Perot resonator antenna is adopted for high-frequency antenna, the low-frequency antenna is a monopole antenna loaded with partially reflective surface, covering the Sub-6 GHz band of 3.3-3.8 GHz. The design of the dual-frequency shared-aperture antenna is realized by the multiplexing of partially reflective surface (PRS) and the metal ground. The dimension parameters of partially reflective surface and the height of the resonator cavity can be adjusted to change the working band of the low-frequency antenna. The radiation beam of the high-frequency antenna is a high gain beam, and the radiation pattern of the low-frequency antenna is omnidirectional. The two patterns do not interfere with each other, which can effectively avoid the deterioration of the pattern. Finally, the high frequency Fabry-Perot resonator antenna achieved gains of 18.3 dBi at 11.2 GHz and the low frequency antenna achieved gains of 4.45 dBi at 3.3 GHz.
SRGCN: Social Relationship Graph Convolutional Network-Based Social Network User Geolocation Prediction

In location-based applications such as online shopping, recommendation system and disaster monitoring, the prediction of user geolocation plays an important role. Existing methods mainly utilize textual content and social network for location prediction, which fail to fully mine and fuse the two types of information on the one hand, and on the other hand, it is difficult to predict the location of isolated users in social network. Therefore, we propose a hybrid learning model Social Relationship Graph Convolutional Neural Network (SRGCN) to jointly model text features and network structure, in addition to constructing a social relationship graph for isolated users based on text topic similarity, and further improving the accuracy of location prediction through social relationships. The experiments on a real-world benchmark dataset validate the effectiveness of the method, showing that SRGCN outperforms existing methods in terms of average distance error, median distance error, and prediction accuracy.
Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data

In this study, using 104 years (1901–2004) of high resolution daily gridded rainfall data, variability and long‐term trends of extreme rainfall events over central India have been examined. Frequency of extreme rainfall events shows significant inter‐annual and inter‐decadal variations in addition to a statistically significant long term trend of 6% per decade. Detailed analysis shows that inter‐annual, inter‐decadal and long‐term trends of extreme rainfall events are modulated by the SST variations over the tropical Indian Ocean. The present study supports the hypothesis that the increasing trend of extreme rainfall events in the last five decades could be associated with the increasing trend of sea surface temperatures and surface latent heat flux over the tropical Indian Ocean. In the global warming scenario, the coherent relationship between Indian Ocean SST and extreme rainfall events suggests an increase in the risk of major floods over central India.
Enhanced Vgg-19 Model for Accurate Brain Tumor Prediction

One of the most serious diseases for both adults and children is a brain tumor. Through the scans, a substantial amount of image data is produced. The radiologist reviews these images to detect the abnormality. Due to the complexity of brain tumors and their features, a manual examination may be mistake-prone. To visualize the brain and spot any abnormal growths, many imaging techniques are used. Magnetic resonance imaging (MRI) is the method that works best to identify brain tumors. Predicting brain tumors from MRI images also contributes to medical research and the development of more sophisticated predictive models. This continuous improvement in technology can lead to better diagnostic tools and more effective treatment options for brain tumor patients. The existing models exhibit significant shortcomings concerning explanation, interpretability, computational complexity, and accuracy. In order to overcome the drawbacks available in the existing techniques, this research work proposes the Enhanced VGG-19 model by improving the convolutional neural network layers, fully connected layers, and weights of the layers and applying effective preprocessing techniques to the datasets to predict brain tumors from MRI images with high accuracy and less computation time. The proposed model produced an accuracy of 99.72% and performed better than the existing techniques.
Template Database Based ACARS Air-Ground Data Link Message Parsing Method and Application

With the continuous development of the aviation industry, air-ground data link communication equipment has become a piece of mandatory equipment for aircraft. According to the problem that aircraft send ACARS messages with different formats and complex message types, the message parsing logic is summarized by analyzing the ACARS communication system and messages to achieve fast and accurate parsing of message information by building a message template library. By implementing the data link ground system through this process, it is verified that the method effectively improves the readability of message data and can cope with the problems arising from the expansion of message formats such as new models and new parameters, which is of significance to the design work of aircraft democratic manufacturers.
Experimental Study of a 28GHz Low-Loss Reflection-type Phase Shifter

A low-loss phased shifter for millimeter-wave phase array antenna is presented. The microstrip line reflection-type phase shifter (RTPS) is designed to use for a 28 GHz phased array system. Based on this microstrip line reflection-type phase shifter structure, a varactor diode (MAVR000120) is installed and measured. Experimental results show that the phase shifter produce a phase change up to 158° with low inserted loss between -2.2 dB and -0.4 dB.
A Beam-Steerable Circular Polarized Antenna Based on Risley Prism With Two Different Tilt Angles LPP-PSSs

There is a high demand for flexible beam scanning of antennas in modern communication and radar technologies. Compared to other beam scanning techniques, mechanical control techniques have the advantage of simple principles and easy implementation. Mechanical beam scanning technology based on the Risley Prism (RP) principle has become a research hotspot, and most of them are realized by rotating a pair of transmitarray for beam scanning. However, a single layer of transmitarray can only achieve beam steering range of ±90° in azimuth. Hence, this article proposed a circularly polarized (CP) antenna that utilizes two linear phase progression phase shifting surfaces (LPP-PSSs) with different tilt angles to achieve 2-D beam scanning coverage of 0-360° in azimuth. The proposed antenna can achieve a maximum scanning range of elevation angle from 4-42° and azimuth angle from 0-360° . Furthermore, when operating at the center frequency of 11 GHz, it has a gain of 18.02 dBi and a 3 dB axial ratio (AR) bandwidth of 36.37%. The proposed antenna has significant application prospects in scenarios that require beam scanning and anti-interference, such as tracking radar and satellite communications.
An implantable wirelessly powered optoelectronic integrated chip for optogenetics

In order to solve the problem of size and power consumption of wired winding or wireless built-in power supply devices in optogenetic experiments, an implantable wireless powered optoelectronic integrated chip, with a RF-DC rectifier and a GaN-LED optrode, has been designed. The rectifier is realized with the integrated NMOS and capacitor based on SMIC 0.18um CMOS technology. The collected RF signal is converted to a stable and high-amplitude DC signal by quadruple voltage rectification, and then supplied to the GaN-LED optrode as its driver. Therefore, the proposed implantable wirelessly powered optoelectronic integrated chip does not require external wiring or built-in battery, which can effectively reduce the size and power consumption. The results from the post-simulation shows that when the antenna collects 5dBm RF signal of 1.5GHz, the RF-DC rectifier provides about 3.4V DC stable signal with about 20us delay and can make the GaN-LED optrode working normally. The PVT and Monte Carlo simulation show the circuit stability under process and temperature deviations, which can meet the requirements of optrode load.
Detection of fluvial landforms underneath forests using LiDAR data

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DETECTION OF FLUVIAL LANDFORMS UNDERNEATH FORESTS USING LIDAR DATA By Hyun-chong Cho May 2009 Chair: K. Clint Slatton Major: Electrical and Computer Engineering Airborne Laser Swath Mapping (ALSM) instrument technology and subsequent algorithm advances have made it possible over the last several years to map the earth’s surface and landcover at unprecedented resolution. The ability of ALSM technology to densely sample ground elevations beneath forest canopies is particularly important because forested watersheds have traditionally been difficult to study with remote sensing techniques. Extraction of stream networks from digital elevation models (DEMs) plays a fundamental role in modeling local and spatially distributed hydrological processes. To detect stream channels, we have developed two approaches. The first approach is based on an encoding of mathematical morphological operators and is shown to systematically and accurately extract stream channel locations, forms, and incipient incisions in a forested watershed. The accuracy of the method is verified using a set of error measures over simulated terrain and also over real terrain where the site was manually surveyed. The second approach represents an alternative to the first, and it consists of three steps. First, the composition of geodesic tophat and bothat operations of different sizes is used in order to build a differential morphological profile that records image structural information. The use of morphological operations at multiple scales can capture a wider variety of surface forms, but it also leads to a high-dimensional parameter space that often contains redundant information.
Design and Application of Deep Processing of Fish Products Based on Three-Dimensional Image Recognition Algorithm

With the continuous improvement of people's living standards, the demand for fish has shifted from direct purchases to convenient and delicious fish processing products. Therefore, how to carry out the deep processing of fish products to enhance their market value has gradually become the focus of attention. It is crucial to improve the mechanization of fish species identification and gill removal during the pre-treatment stage. The purpose of this article was to study the mechanized design and application of deep processing of fish products based on three-dimensional image recognition algorithms. The principle of imaging underwater objects was introduced. Taking fish products as an example, an improved Faster RCNN neural network was used to accurately locate the gill incision area of fish. The experimental results showed that compared with the Darknet53 model, the improved Faster RCNN improved the positioning accuracy of gill cutting points by 10%, laying a foundation for the precise positioning of subsequent gill cutting points.
Human Motion Recognition Method Using Millimeter-wave Radar Based on 3DTSNet

This paper proposes a 77 GHz millimeter wave radar-based human motion recognition method. Firstly, a 77 GHz millimeter wave radar data acquisition system is built. Secondly, the radar echoes of multiple human actions are data preprocessed to obtain a 3D Range-Doppler-time image dataset of human actions. Finally, the 3D convolutional neural network model is designed with the human action dataset as the network input sample to complete the simulation experiments of five different human action recognition. The experimental comparison results show that the detection accuracy of the proposed 3DTSNet network model can reach 98.8% and has good generalization.
A distributed transaction performance optimization method

With the wide application of distributed systems, complex transaction processing involving multiple nodes has become an important challenge. The difficulty lies in how to ensure the data consistency of each node and how to reduce the system delay to increase the transaction throughput. In order to solve the distributed transaction problem, the 2PC(two-phase commit) solution has been proposed by predecessors, but this solution brings some problems such as resource blocking, which has a great impact on the performance of distributed system. This paper proposes an asynchronous transaction processing scheme on the basis of 2PC protocol and BASE theory. It has been verified and evaluated that although 2PC scheme still performs well in solving distributed transaction problems, compared with 2PC scheme, the proposed scheme in this paper has slightly larger transaction throughput and less resource consumption, and the effectiveness of the solution has been verified in experiments.
Ultra-wideband High Power Space Pulsed Traveling-wave Tube Amplifier

With the development of microwave vacuum power technology and the needs of various satellite applications, a higher output power of space vacuum power amplifier is required. In order to meet the requirements of synthetic aperture radar (SAR) ,a 500W pulsed traveling-wave tube amplifier (TWTA) with the bandwidth of 6G in Ka band is presented in this paper. The main function of the pulsed TWAT is to receive the radio-frequency (RF) pulse signal of the radar transmitter and amplify its power to send to the antenna, and to receive both the pulse control signal and the telemetry signals from the data processor, and also to offer the radar system the corresponding telemetry signals. In this paper, a prototype is designed to verify the performance of the TWTA.
Comparative Analysis of Patient Care Processes with Process Mining and Graph Edit Distance in Patient Data on the Indonesia National Health Insurance

Process mining is designed to support business process management in various sectors, such as manufacturing, finance, healthcare, etc. In the healthcare sector, process mining has been widely used in various case studies. This study will take sample data from the Health Social Security Administration Agency in 2015-2018. There are four types of sample data, namely Membership, Primary Health Facilities, Advanced Referral Health Facilities, and Non-Capitation Primary Level Health Facilities. Based on the sample data, this study aims to analyze the sequence of activities for each patient diagnosis using an inductive miner, which is one of the algorithms in process mining. The treatment process for each diagnosis is compared to one another using the Graph Edit Distance (GED) to see the similarities and differences between the treatment processes. This will be done to find out how efficient a treatment process is for all disease diagnoses from patients based on existing sample data by looking at the values of trace fitness, precision, generalization, and simplicity from process models for each diagnosis. The purpose of this paper is to look for differences or similarities in the process of patient treatments from the Health Social Security Administration Agency based on the diagnosis of one disease with another. The result of this study enables us to identify treatment processes with similarities based on the weighted node graph. Additionally, it provides insight into the relative efficiencies of different treatment processes, highlighting areas that should be improved.
Bioimpedance Spectroscopy System for Glioblastoma Spheroid Growth and Dissociation Characterization

This work presents a novel impedance biological sensor technique for the characterization of spheroid growth and dissociation into single cells. The impedance platform was built on Analog Devices’ AD5933 and was configured to measure unknown impedances by optimizing the sweep parameters between a frequency range of 50 to 25,050 Hz. The device was calibrated with a 470 $\Omega$ resistor and set to a feedback resistance of 470 $\Omega$. Subsequently, the configured biosensor was tested in the characterization of glioblastoma cells. For the first time in the literature, distinct impedimetric differences between single cells and spheroids were observed. This serves as the foundation for a robust platform for use in cell culture, tissue engineering, and single-cell analysis applications.
An Improved Algorithm for Arterial Road Signal Coordination Control

In this paper, the traditional algorithm of arterial coordination control is analyzed in depth, and the shortcomings of the traditional algorithm are pointed out. A modified method for calculating the width of green wave bandwidth, a method for calculating the phase shift of intersection signals, and a method for matching the real intersection with the ideal intersection are proposed. The analysis shows that the improved algorithm can achieve the best match between the real intersection and the ideal intersection, and the calculated green bandwidth width and signal phase shift of each intersection are more accurate.