Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrast Enhancement of Fundus Images by Employing Modified PSO for Improving the Performance of Deep Learning Models

Computer-Aided diagnosis (CAD) is a widely used technique to detect and diagnose diseases like tumors, cancers, edemas, etc. Several critical retinal diseases like diabetic retinopathy (DR), hypertensive retinopathy (HR), Macular degeneration, retinitis pigmentosa (RP) are mainly analyzed based on the observation of fundus images. The raw fundus images are of inferior quality to represent the minor changes directly. To detect and analyze minor changes in retinal vasculature or to apply advanced disease detection algorithms, the fundus image should be enhanced enough to visibly present vessel touristy. The performance of deep learning models for diagnosing these critical diseases is highly dependent on accurate segmentation of images. Specifically, for retinal vessels segmentation, accurate segmentation of fundus images is highly challenging due to low vessel contrast, varying widths, branching, and the crossing of vessels. For contrast enhancement, various retinal-vessel segmentation methods apply image-contrast enhancement as a pre-processing step, which can introduce noise in an image and affect vessel detection. Recently, numerous studies applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, but with the default values for the contextual region and clip limit. In this study, our aim is to improve the performance of both supervised and unsupervised machine learning models for retinal-vessel segmentation by applying modified particle swarm optimization (MPSO) for CLAHE parameter tuning, with a specific focus on optimizing the clip limit and contextual regions. We subsequently assessed the capabilities of the optimized version of CLAHE using standard evaluation metrics. We used the contrast enhanced images achieved using MPSO-based CLAHE for demonstrating its real impact on performance of deep learning model for semantic segmentation of retinal images. The achieved results proved positive impact on sensitivity of supervised machine learning models, which is highly important. By applying the proposed approach on the enhanced retinal images of the publicly available databases of {DRIVE and STARE}, we achieved a sensitivity, specificity and accuracy of {0.8315 and 0.8433}, {0.9750 and 0.9760} and {0.9620 and 0.9645}, respectively.
Design of Vital Sign Measurement Based on 24G FMCW Radar

In this paper a vital sign measurement system is presented based on 24G frequency modulation continuous wave (FMCW) radar. The radio frequency (RF) circuit is built with BGT24MTR11 operating 24-24.5G FMCW signal. However, to guarantee a sufficient frequency resolution for respiration rate (RR) and heart rate (HR) measurement most of the previous studies use long-period time windows more than 10s. This paper proposes a method based on Discrete Fourier Transform (DFT), with the sample time window less than 6s. Moreover an improved DFT is used to suppress the huge computation of DFT.
End-to-End Neural Network for Vehicle Dynamics Modeling

Autonomous vehicles have to meet high safety standards in order to be commercially viable. Before real-world testing of an autonomous vehicle, extensive simulation is required to verify software functionality and to detect unexpected behavior. This incites the need for accurate models to match real system behavior as closely as possible. During driving, planing and control algorithms also need an accurate estimation of the vehicle dynamics in order to handle the vehicle safely. Until now, vehicle dynamics estimation has mostly been performed with physics-based models. Whereas these models allow specific effects to be implemented, accurate models need a variety of parameters. Their identification requires costly resources, e.g., expensive test facilities. Machine learning models enable new approaches to perform these modeling tasks without the necessity of identifying parameters. Neural networks can be trained with recorded vehicle data to represent the vehicle's dynamic behavior. We present a neural network architecture that has advantages over a physics-based model in terms of accuracy. We compare both models to real-world test data from an autonomous racing vehicle, which was recorded on different race tracks with high- and low-grip conditions. The developed neural network architecture is able to replace a single-track model for vehicle dynamics modeling.
Graphene for next generation magnetic devices: A first-principles study

Graphene, an atomic thin sheet of monolayer carbon atoms, is deemed to replace silicon and revolutionize the electronics industry. It has massless Dirac fermions and exhibits ballistic charge transport, a prerequisite for upcoming futuristic nanodevices, such as field-effect transistors, sensors, etc. However, it lacks an intrinsic electronic bandgap and thus is obsolete for use in these applications. In this work, a first principles study is used to predict the opening of a bandgap in graphene by an engineered introduction of modulations in its lattice. Moreover, it is found that the atomic modulation and the addition of hydrogen atoms at sub-lattices induce magnetism in the graphene sheets. The hydrogen-induced itinerant magnetism (ferromagnetic or antiferromagnetic) depends on the type of defects and the structure of the sheet. This hydrogen-induced spin promotes the role of graphene as a potential material for magnetic memory device applications. Furthermore, with the creation of atomic vacancies and quasilocalized states, it is deemed to allow selective permeation (water/gas) and thus paves the way for its use in-filtration (membrane technology) and hydrogen storage applications.
Dual-Polarized Microstrip Yagi Antenna Based on Coaxial Back Feed

This paper proposes a dual-polarized microstrip Yagi antenna based on coaxial back feed. Two truncated microstrip dipoles are placed orthogonally on the same plane as the driving elements, and shortened probes are applied to eliminate the feed balun, which greatly simplifies the antenna structure and the manufacturing complexity. The simulated results show that the proposed antenna has an impedance bandwidth of 12.5% (S11<-10dB), radiates a tilted beam of 26° with a good polarization purity.
Cervical Cancer Diagnosis Using Very Deep Networks Over Different Activation Functions

Cancer prevention is mainly achieved by screening the transformation zones. Cervical pre-cancerous stages can be seen in three different types, and all can transform into cancer. Thus, it is crucial to intelligently screen cervical abnormality and have a robust system for detecting whether a cervix is in normal (healthy) or at a pre-cancerous stage. Deep learning showed great potentials when applied to biomedical problems, including medical image analysis, disease prediction, and image segmentation. Hence, in this paper, very deep residual learning based networks are designed in order to perform cervical cancer screening. Moreover, in this work, we highlight the importance of the activation functions on a residual network (ResNet)’s performance. Thus, three residual networks of the same structure are built with different activation functions. The employed models are trained and tested using a dataset of colposcopy cervical images, and the experimental results showed that designed residual networks with leaky and parametric rectified linear unit (Leaky-RELU and PRELU) activation functions performed almost equally in terms of accuracy where they reached accuracies of 90.2 and 100%, respectively. This achieved high accuracy was compared to other related works’ results, and it showed an outperformance in screening the pre-cancerous and healthy colposcopy cervical images. Such an earlier and accurate diagnosis may help in preventing cervical cancer transformation.
An FPGA-Based LDPC Decoder With Ultra-Long Codes for Continuous-Variable Quantum Key Distribution

In this paper, we propose a good decoding performance, low-complexity, and high-speed decoder architecture for ultra-long quasi-cyclic LDPC codes by using the layered sum-product decoding scheme. To reduce implementation complexity and hardware resource consumption, the messages in the iteration process are uniformly quantified and the function $\Psi (x)$ is approximated with second-order functions. The decoder architecture improves the decoding throughput by using partial parallel and pipeline structures. A modified construction method of parity check matrices was applied to prevent read&write conflicts and achieve high-speed pipeline structure. The simulation results show that our decoder architecture has good performance at signal-to-noise ratios (SNRs) as low as −0.6 dB. We have implemented our decoder architecture on a Virtex-7 XC7VX690T field programmable gate array (FPGA) device. The implementation results show that the FPGA-based LDPC decoder can achieve throughputs of 108.64 Mb/s and 70.32 Mb/s at SNR of 1.0 dB when the code length is 262,144 and 349,952, respectively. The decoder can find useful applications in those scenarios that require very low SNRs and high throughputs, such as the information reconciliation of continuous-variable quantum key distribution.
Automatic Defense Against Distributed Denial of Service Using Anomaly Based Method in Machine Learning

Security of the network is biggest challenge in modern world. Big companies like Google, Microsoft, Amazon etc. need to have their network secured all the time as they host public information on their servers. DDoS attacks poses a serious threat to many organizations. One need to be alert always to identify such attacks as they come from various distributed sources and every time hackers try to attack differently, so filtering by hand to avoid DDoS attack is a difficult task. Goal of this paper is to train a machine to classify the packets into malicious packet or legitimate packet. This is achieved by training our machine with KDD CUP dataset. This dataset is divided into two halves containing 20% of original dataset and 10% of original dataset which is used for training ANN and testing ANN, respectively. The dataset is chosen uniformly from the original dataset set so that normal and malicious instances are approximately same in number in each dataset.
Gain Improvement Methods of Millimeter-Wave Antennas

Millimeter-wave (MMW) frequency bands possessing of high date rate capability makes them strong candidates for future 5G networks. Due to the high path loss at millimeter-wave frequency bands, high-gain MMW antennas are indispensable for future wireless communications. How to improve antenna gain is still a challenging issue. This paper presents a few of methods to improve the gain and their antenna examples are given.
Formation of Fiber Tapers by Chemical Etching for Application in Chaotic Correlation Fiber Loop Ring Down Sensing

Fiber loop ring down spectroscopy has been developed into a high-sensitivity technique for sensing many important characteristics of samples, such as refractive index. In contrast to the previous demonstrations by using mode-locked fiber laser, this work is demonstrated to use chaotic laser combined with fiber loop ring down sensor. The formation and performance of fiber tapers was monitored by combining chaotic fiber ring down technology on line and in real time. Two sets of refractive index sensing experiments are performed and further analyzed by using the proposed setup. The results are shown that the detection limit of the chaotic correlation fiber loop ring down system is 3.45 × 10−4 RIU and the sensitivity is 0.0682 ns−1RIU−1 in 6.7 m, respectively. The performance of our system can be drastically improved by increasing the length of the etched fiber tapers to be tens of centimeters using the chaotic correlation fiber loop ring down system with the smaller fiber ring lengths.
Fat-IntraBody Communication at 5.8 GHz: Verification of Dynamic Body Movement Effects Using Computer Simulation and Experiments

This paper presents numerical modeling and experimental validation of the signal path loss at the 5.8 GHz Industrial, Scientific, and Medical (ISM) band, performed in the context of fat-intrabody communication (fat-IBC), a novel intrabody communication platform using the body-omnipresent fat tissue as the key wave-guiding medium. Such work extends our previous works at 2.0 and 2.4 GHz in the characterization of its performance in other useful frequency range. In addition, this paper also includes studies of both static and dynamic human body movements. In order to provide with a more comprehensive characterization of the communication performance at this frequency, this work focuses on investigating the path loss at different configurations of fat tissue thickness, antenna polarizations, and locations in the fat channel. We bring more realism to the experimental validation by using excised tissues from porcine cadaver as both their fat and muscle tissues have electromagnetic characteristics similar to those of human with respect to current state-of-art artificial phantom models. Moreover, for favorable signal excitation and reception in the fat-IBC model, we used topology optimized waveguide probes. These probes provide an almost flat response in the frequency range from 3.2 to 7.1 GHz which is higher than previous probes and improve the evaluation of the performance of the fat-IBC model. We also discuss various aspects of real-world scenarios by examining different models, particularly homogeneous multilayered skin, fat, and muscle tissue. To study the effect of dynamic body movements, we examine the impact of misalignment, both in space and in wave polarization, between implanted nodes. We show in particular that the use of fat-IBC techniques can be extended up in frequency to a broadband channel at 5.8 GHz.
Frequency Domain Optimization of FIR Filter for Pre-emphasis in Transmission Line Systems

In this paper, a coefficient optimization method for the finite impulse response (FIR) filter under least standard deviation criteria from frequency domain is proposed. In order to solve the inter symbol interference (ISI) problem and eliminate the impact of channel, the FIR filter is adopted for pre-emphasis here. The optimal tap coefficient is determined through the channel characteristics and hence one specific microstrip line is analyzed as the practical channel. Eventually, the performance of the combined effect of FIR filter pre-emphasis is evaluated and the compensation results demonstrate the validation of the optimization method. The proposed method can better highlight the frequency domain characteristics of compensation and the system structure is much simpler.
A Simplified Logical Method (SLM) Based Capacitor Balancing Technique for Modular Multilevel Converter

The modular and voltage scalable features of modular structure based power converter has attracted for wide power range applications. However, balancing the capacitor voltage becomes complex when the number of sub modules is increased. In a conventional system, for balancing a capacitor voltage, separate monitoring and control are required for each sub module, which further increases the cost and burden to the converter. This paper proposes a simplified logical method (SLM) for balancing the capacitor voltage of each sub module without any additional sensors and controllers. The capacitor voltage of N sub modules is balanced based on the simple logical function, which eventually balances the capacitor voltage and thus reduces the voltage ripple of each capacitor. In addition, this method suppresses the circulating current and common mode voltage, which increases the efficiency and cost effective of the converter. The level shifted carrier technique is used for providing desired pulse signals for the switching devices in the converter. The effective performances of the proposed method are validated using simulation and experimental setup.
Robot Collaboration Intelligence with AI

We present a current automation trend, robot collaboration intelligence, to control and manage individual industrial robots to collaborate intelligently with advanced AI technologies. To increase the level of flexibility in manufacturing lines and warehouse/distribution centers, flexible agent–type robots such as automated guided vehicles have been adopted in many industries. As information technologies advance, these individual agent robots become smart and the fleet size of agents becomes larger. Robot collaboration intelligence is a newly emerging technology that allows intelligent robots to work in a more effective and efficient way. We introduce this emerging technology with industry cases and provide researchers with new research directions in automation and simulation with AI.
Conical Beam Antenna With Dual Pointing Angles for Large Beam Coverage

A novel conical beam antenna with dual pointing angles for large beam coverage is proposed in this paper. The design consists of a three sections circular waveguide aperture antenna and a coaxial CTS (Coaxial Transverse Stub) array, which uses a single pole double throw (SPDT) switch to switch the two conical beams. The three sections circular waveguide aperture antenna structure is adopted to realize a conical beam with the beam-pointing angle of 40°, and the 3dB beam coverage in elevation plane is from 30° to 50°. The coaxial CTS array is composed of six coaxial CTS elements, that implements a beam pointing angle of 60° and the 3dB beam coverage in elevation plane of 50° to 70°. The gains of the circular waveguide aperture antenna and the CTS array are both 7.2dB. Hence, by using a SPDT switch to switch the two beams, a high gain wide beam coverage of 30° to 70° can be achieved when the two antennas are assembled together. The simulated results show that the two antennas could operate independently, and the interaction effects are insignificant.
Three-Dimensional Object Co-Localization From Mobile LiDAR Point Clouds

Recently, 3D deep learning technologies require a large amount of supervised 3D point-cloud data to learn statistical models for various ITS-related tasks, e.g. object classification, object detection, object segmentation, etc. However, manually annotating 3D point-cloud data is time-consuming and labor-intensive. Therefore, this paper aims at co-locating 3D objects from mobile LiDAR point clouds without any help of supervised training data. To realize it, we propose a new framework to implement 3D object co-localization for automatically extracting the objects of the same category from different point-cloud scenes. Specifically, to search and exploit the co-information from objects in different point-cloud scenes, we formulate a 3D object co-localization problem as a maximal subgraph matching problem. During the graph construction procedure, to handle the inconsistent representation of objects in different scenes, we propose a multi-scale clustering method to represent objects by a pyramid structure. In addition, because the maximal subgraph matching problem is NP-hard, we propose a stochastic search algorithm to generate the co-localization results. Extensive experiments on the point-cloud data collected by the Reigl VMX450 mobile LiDAR system demonstrate the promising performance of the proposed framework.
V2X in 3GPP Standardization: NR Sidelink in Release-16 and Beyond

The 5G mobile network brings several new features that can be applied to existing and new applications. High reliability, low latency, and high data rate are some of the features that fulfill the requirements of vehicular networks. Vehicular networks aim to provide safety for road users and several additional advantages such as enhanced traffic efficiency and in-vehicle infotainment services. This article summarizes the most important aspects of NR-V2X, which is standardized by 3GPP, focusing on sidelink communication. The main part of this work belongs to 3GPP Release 16, which is the first 3GPP release for NR-V2X, and the work/study items of the future Release 17.
Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.
BCI: Classifiers Optimization for EEG Signals Acquiring in Real-Time

The topic of this article is for the development of the new high-performance BCI systems, and in this regard we have integrated optimization algorithms in basic BCI system at the level of the classification algorithms to improve the performance of the system recognition as illustrates the section of results. The optimizes were also programmed to work collectively in order to reach the maximum value of accuracy in a short period of time. This position can also be extended to include improving the system rest, algorithms of the selection features, algorithms of frequency and spatial filters, algorithms of the best electrodes selection.
A Ka-Band Folded Transformer Based Quadrature Generation Scheme

This paper introduces a folded transformer based quadrature generation scheme. The quadrature generation network is implemented with multi-turn transformer configuration and compensating capacitors. The differential input signal is converted into differential quadrature outputs with low magnitude and phase error in one transformer footprint. A 30-40GHz quadrature generation design is realized in a standard 65 nm CMOS process with 107um in diameter. This scheme achieves 0.8 dB insert loss at 35 GHz. For maximum ±0.7dB magnitude mismatch and 2° phase error, the achieved bandwidth is 28.6% (30 GHz to 40GHz). Within the operation bandwidth, the input matching is below −13 dB.
Photonics-assisted Amplification for Baseband-over-Fiber Links

We propose and experimentally investigate the use of a photonics-assisted amplification technique for baseband-over-fiber (BBoF) links. The proposed approach focuses on increasing the received electrical baseband signal in fiber-wireless systems, by exploiting the four-wave mixing effect for proving baseband electrical gain of up 19 dB. An experimental digital performance analysis in terms of bit error rate (BER) demonstrate the feasibility of our strategy for improving the power efficiency in BBoF links. By using PBA we enhance the BER performance from 10-3 to 10-11, for the same electrical power.