Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation of Skin Lesion Using Harris Corner Detection and Region Growing

Nowadays skin diseases are considered as one of the common cause of human illness, and if not detected or diagnosed at early stage, it can be more harmful. With advancement in computer vision and image processing various techniques have been developed for diagnoses of skin diseases. This work mainly focused on simple yet effective technique for segmentation of skin lesions. It is also robust for extracting the region of interest whose background and foreground color is bit similar. It automatically segments skin lesion of arbitrary shapes and sizes. The proposed technique is mainly based on three major modules namely preprocessing, segmentation and post-processing. In pre-processing different color models are used for extraction of single channel followed by filtering to remove noise. In the second module, segmentation is preformed using Harris corner detector, seed selection and region growing segmentation. Finally morphological operation is performed as post-processing comprising of region filling and dilation. The experimentation results shows the performance of proposed model by achieving accuracy of 95%.
A Generalized Parity-Check Transformation for Iterative Soft-Decision Decoding of Binary Cyclic Codes

The conventional parity-check matrix transformation algorithm (PTA) requires matrix inversion to transform matrices of maximum distance separable (MDS) codes. However, such matrix transformation is not always guaranteed for the class of non-MDS codes. Hence, the PTA fails for binary cyclic (BC) codes. To overcome this limitation, we developed a generalized parity-check matrix transformation (GPT) algorithm for binary cyclic codes. The GPT avoids the matrix inversion step of the PTA. It permutes the columns of the parity-check matrix based on the reliability information from the channel. Results show a significant bit error rate (BER) performance gain as compared to the existing PTA. It also presents a reasonable BER performance as compared to the other soft-decision (SD) decoding algorithms. In addition, the decoder functions within a practical decoding time complexity, particularly at the message passing stage.
A Study on Virtual and Augmented Reality in Real-Time Surgery

The development in the healthcare industry is the best happening of this decade and it is a boon for both patients and doctors. As Virtual reality (VR) being used in many applications, this could also be extended to the surgical world. This study is about the potential use of VR and Augmented Reality (AR) in the field of medicine, and how it will train the young surgeons while performing a complicated surgery.
Minimization of Secrecy Outage Probability With Artificial-Noise-Aided Beamforming for MISO Wiretap Channels

The problem of optimal power allocation between the information-bearing signal and the artificial noise (AN) signal is addressed in this letter to enhance the physical layer security of the multiple-input-single-output wiretap channel. Differing from the existing works concerning the secrecy rate maximization, this letter studies the problem of minimizing the secrecy outage probability given the secrecy rate, where the channel state information concerning the eavesdropper is modeled as quasi-static Rayleigh fading and only known statistically. After formulating the problem as an optimization problem of power allocation, we prove that the optimal solution can be conveniently obtained by using the method of bi-section search. Simulation results show that the proposed power allocation method is efficient and can enhance the physical layer security by reducing the secrecy outage probability.
Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model

Interpretation of 3-D scene through LiDAR point clouds has been a hot research topic for decades. To utilize measured points in the scene, assigning unique tags to the points of the scene with labels linking to individual objects plays a crucial role in the analysis process. In this article, we present a supervised classification approach for the semantic labeling of laser scanning points. A novel method for extracting geometric features is proposed, removing redundant and insignificant information in the local neighborhood of the supervoxels. The proposed feature extraction method uses the supervoxel-based local neighborhood instead of points as basic elements, encapsulating the geometric features of local points. Based on the initial classification results, the graph-based optimization is used to spatially smooth the labeling results, based on the graphical model using the perception weighted edges. Benefiting from the graph-based optimization process, our supervised classification method required only a few training datasets. Experiments were carried out by comparing the semantic labeling results with manually generated ground truth datasets. The performance of the proposed methods with different characteristics was analyzed. By using our testing datasets, we have achieved an overall accuracy of better than 0.8 for assigning the measured points to eight semantic classes.
SPICE compact model of IGZO memristor based on non-quasi statically updated Schottky barrier height

A SPICE compact model based on non-quasi statically updated Schottky barrier is proposed and demonstrated in an IGZO memristors with Pd/IGZO/Mo structure. Our model combines the thermionic emission-based conduction and the potentiation/depression based on the Schottky barrier height modulation resulting from the oxygen ion migration or the electron trapping/detrapping at separate interface between the metal electrode and the switching layer. A stretched exponential function is used for non-quasi static update of the Schottky barrier. Proposed model reproduces the time-varying current, the DC I-V, and potentiation/depression characteristics very well with concrete parameter-extracting procedure. Our result is potentially useful in the design of devices and circuits for the oxide memristor-based neuromorphic computing.
Design Approach of Single-/Dual-Band Wilkinson Power Dividers with Single/Two Section Transmission Line Transformers

A design approach of single-/dual-band Wilkinson power dividers with single/two section transmission line transformers is summarized in this letter. Normally, two section transmission line transformer is well-known topology, which is not only used for single-band design application with compact circuit size and harmonics suppression, but also for dual-band circuit in other circuit topologies, such as Wilkinson power divider application. In this paper, several kinds of topologies could be summarized to a general topology with the same design equations and mathematical constraints. It is quite useful to understand the relationship among these topologies, meanwhile, it is also helpful for further circuit improvement.
Vision-Based State Estimation for Non-Cooperative Targets in Space

With the increasing frequency of space activities, non-cooperative targets such as space debris have brought great hidden dangers to spacecraft operation. How to estimate their state has gradually become the main demand. This paper introduces a new method for estimating the angular velocity value as well as the axis of rotation of non-cooperative targets based on deep neural networks. The proposed algorithm has three parts: (1) use of the convolutional neural network (CNN) YOLO model, which is trained to identify the non-cooperative targets in the images taken by the camera, (2) extracting ORB features in the detected pixel regions of the non-cooperative target and using simultaneous localization and mapping (SLAM) to calculate the rotation matrix and translation matrix of the non-cooperative target relative to the camera, and (3) using the angular velocity value calculated by the Rodrigues equation to estimate the time of loop closure in SLAM. In order to calculate the axis of rotation, the plane fitting and spatial arc fitting are used. Combined with ground test, the experimental results indicate the error of the measured speed value of this algorithm is 0.0081rad/s, and the maximum error of the rotation axis is 5.12° which show the correctness of the algorithm and the potential of online application.
Research and Application of Industrial Equipment Management Service System Based on Cloud-Edge Collaboration

With the integration development of Internet of Things, edge computing, cloud computing and artificial intelligence, the computing paradigm centered on traditional cloud computing has begun to integrate the edge computing paradigm, forming an industrial Internet of Things revolution based on Cloud-Edge collaboration. It not only can improve the response speed of industrial field system and reduce the load pressure of network bandwidth caused by data transmission, but also can realize the refined management of industrial devices and the deep mining of industrial data by on the cloud platform. In short, the cloud platform and the edge platform can play their respective advantages and continue to promote the industrial Internet of Things to the intellectualized development. In this paper, it started from the system architecture, and an industrial equipment management service system based on Cloud-Edge collaboration was discussed, including the design of equipment management service cloud platform and edge intelligent gateway in detail. The central airconditioning energy consumption monitoring and optimization application based on INDICS platform and SMART IOT edge intelligent gateway were introduced, to verify system availability.
Group Formation Control For Nonlinear Second-Order Multi-Agent Systems Using Neural Networks

Group formation control problems for nonlinear second-order multi-agent systems are considered using neural networks. Multiple agents are divided into subgroups to form specified sub-formation and construct interactions among subgroups based on graph theory. First of all, an adaptive protocol is designed for nonlinear second-order multi-agent systems based on neural networks. Then sufficient conditions for the multi-agent systems to achieve group formation are given. Finally, a numerical example is provided to verify the proposed conditions.
Study of Passenger Expectations on Public Transportation Service in the City of Martapura

In order to improve the public transportation service in the city of Martapura, the expectations from the side of the users are required to know. These factors are essential because a good public transportation service according to the passenger expectations will encourage the users to utilize more public transportation. Therefore, this study has goals to understand the factors of choosing public transportation (minibus) from the user expectations, to investigate the lack of the service and performance of the existing public transportation and to find a solution of improving public transportation service in the city of Martapura. Partial Least Square (PLS) method was employed to perform data analysis of a questionnaire survey from the passengers. The study demonstrates that the most important factors expected by the passengers are schedule and waiting time, fare system, safety, environmental impact, mode’s integration and connection, driver attitude, vehicle performance and capacity, access and performance of the bus stops, passenger comfort and information.
Cinnamomum Camphora Classification Based on Leaf Image Using Transfer Learning

Plants play a very important role in our daily life providing us with food and oxygen. It is necessary to build an aut omatic system for recognizing plant Convolutional neural netw orks (CNNs) have become effective identification method especial ly in computer vision in recent years because they achieved outst anding performance on different tasks, such as image classificatio ns. In this paper, we have shown performance comparison of usin g the seven pretrained deep convolutional neural network(Alexne t, Googlenet, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv 2, Squeezenet) that employed fine-tuning technique to retain the l ast three full connection layers and get the classication model. We applied our method to Cinnamomum Camphora leaf dataset clas sification,. the experimental results showed that the proposed met hod is quite effective and feasible.
Dual-band tunable mid-infrared polarization converter based on U-shaped graphene metamaterial

A dual-band tunable mid-infrared reflective polarization converter using graphene metamaterial is proposed. The converter is composed of a periodically U-shaped graphene sheet, a dielectric layer, and a metal layer from top to bottom. It is able to effectively reflect and convert a linear polarized incidence mid-infrared wave to its cross polarization reflected wave at the two resonant frequencies with the polarization conversion ratio (PCR) approaching 100%. The proposed polarization converter possesses great potential applications in mid-infrared, antenna, spectroscopy, radiometer, thermal testing instrument, and remote sensor.
Association and Reliability in 802.11ad Networks: An Experimental Study

With the exponential increase in wireless data traffic risking to overwhelm the current networking infrastructures, mmWave networking is seen as on of the most promising paths out of the logjam. IEEE introduced a new standard recently, 802.11ad, that uses mmWave frequencies for delivering multi-Gigabit capacities in a WLAN setting. Such capacities, however, do not come for free: mmWave frequencies have poor propagation characteristics and suffer from blockage, whose consequences on the networking protocols are not fully understood.In this paper, we aim at in-depth understanding of two key primitives in 802.11ad networks in environments rich with blockage: user association and reliability. Using careful measurements on COTS hardware, we find that user association under blockage takes an unreasonably long time, and, after analyzing the causes, provide practical insights on how to reduce this time by an order of magnitude. Relying on a multi-layer tracing tool allowing unprecedented granularity in the analysis of 802.11ad networks, we find that the part of the new standard regarding reliability is underspecified leading to different interpretations in practice. Their potential impact on performance is then evaluated using trace-based simulations, which reveal an interesting throughput-reliability tradeoff. Finally, we find that TCP is poorly prepared for sudden changes in round-trip timeouts, as those induced by link blockage in mmWave networks, leading to an unnecessary increase in timeouts.
Modeling of Self-organization of Swarm Agents by Markov Chain-based Methods

Aiming at two essence characteristics of swarm agents—"self-organization behavior" and "emergence effect", this paper introduces a kind of probabilistic methods -Markov Chain-based methods, to analyze the internal driving force and the dynamics of the swarm agents. The main idea is to transform the problem of self-organization into driving the swarm distribution to a given probabilistic distribution, not just controlling the precise trajectories of each agent. First, with the assumption that the system state is fully observable and autonomous, a group of Markov matrices are designed for a given swarm distribution. Next, a KL-distance feedback algorithm is designed to simulate the self-organization behavior, during which the overall issues such as complexity of the communication network and the computational burden are considered, while some local limitations such as the internal collisions are not considered. Then, the convergence and stability of the algorithm is proved. Finally, some numerical simulations are provided.
Cluster-Based Anonymization of Directed Graphs

Social network providers anonymize graphs storing users' relationships to protect users from being re-identified. Despite the fact that most of the relationships are directed (e.g., follows), few works (e.g., the Paired-degree [1] and K-In&Out-Degree Anonymity [2]) have been designed to work with directed graphs. In this paper, we show that given a graph, DGA [1]and DSNDG-KIODA [2] are not always able to generate its anonymized version. We overcome this limitation by presenting the Cluster-based Directed Graph Anonymization Algorithm(CDGA) and prove that, by choosing the appropriate parameters, CDGA can generate an anonymized graph satisfying both the Paired k-degree [1] and K-In&Out-Degree Anonymity [2]. Also, we present the Out-and In-Degree Information Loss Metric to minimize the number of changes made to anonymize the graph. We conduct extensive experiments on three real-life data sets to evaluate the effectiveness of CDGA and compare the quality of the graphs anonymized by CDGA, DGA, and DSNDG-KIODA. The experimental results show that we can generate anonymized graphs, by modifying less than 0.007% of edges in the original graph.
Spatially Coupled LDPC Codes With Small Constraint Length and Low Error Floor

In this letter, we design time-invariant spatially coupled low-density parity-check (SC-LDPC) codes with small constraint length and low error floor. For this, we modify the codes in the literature that have (close to) minimal constraint length for a given degree distribution and girth, to improve their error floor. This is performed by eliminating (or minimizing the multiplicity of) some of the dominant trapping sets (TSs) of the codes and/or increasing the minimum distance. To reduce the multiplicity of the TSs effectively and efficiently, we devise a technique based on the parent/child relationship between TSs that aims at successively minimizing the multiplicity of TSs depending on their harmfulness.
An Efficient Semantic Segmentation Method using Pyramid ShuffleNet V2 with Vortex Pooling

Efficient and accurate semantic segmentation is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance. Many works try to compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. As a result, real-time segmentation task for embedded devices is still an open problem. In this paper, we focus on building a network with better performance possible while still achieve real-time inference speed. We first use a pyramid kernel size to capture more spatial information instead of using just a 3×3 kernel size for DWConvolution in ShuffleNet v2. Meanwhile, an efficient Vortex Pooling module is employed to aggregate the contextual information and generate high-resolution features. Compared with other state-of-the-art real-time semantic segmentation networks, the proposed network achieves similar inference speed and better performance on embedded device. Specifically, we achieve state-of-the-art 73.46% mean IoU on Cityscapes test dataset, for a 768×1024 input, a speed of 46.1 frames per second on NVIDIA Jetson AGX Xavier embedded development board is achieved.
Exploiting Bias for Cooperative Planning in Multi-Agent Tree Search

Graph search over states and actions is a valuable tool for robotic planning and navigation. However, the required computation is sensitive to the size of the state and action spaces, a fact which is further exacerbated in multi-agent planning by the number of agents and the presence of sparse reward signals dependent on the cooperation of agents. To tackle these problems, we introduce an algorithm that is pre-trained in a centralized fashion but implemented on robots in a distributed way at runtime. The centralized portion uses imitation learning to iteratively construct policies that help guide an individual agent‘s own runtime search as well as predict other agents’ future actions by exploiting previously discovered joint actions. Our algorithm includes a novel method of tree search based on a mixture of the individual and joint action space, which can be interpreted as a cascading effect where agents are biased by exploration of new actions, exploitation of previously profitable ones, and recommendation provided by deep neural nets. Simulations show the efficacy of the proposed method in cooperative scenarios with sparse rewards.
Performance Study of a MIMO Mobile Terminal With Upto 18 Elements Operating in the Sub-6 GHz 5G Band With User Hand

This paper investigates the performance variation when a different number of antenna elements (AEs) is integrated onto a single MIMO mobile terminal, both in free space and when held in a user hand in data mode. Starting with a minimum of two AEs, this investigation assessed the performance of the MIMO terminal with every additional two AEs (up to 18 AEs) in terms of envelope correlation coefficient (ECC), efficiency, multiplexing efficiency, capacity and maximal ratio combining (MRC). The integrated MIMO antennas are identical and operate between 5 and 6 GHz for 5G applications. Results indicated that ECC increased with the number of AEs. However, ECC remains less than 0.32 for the case of 18 AEs in both free space and with a user hand. Meanwhile, the free space efficiency of about 90 % for the two AEs is observed to decrease with the increasing number of AEs to about 50 % with 18 AEs. However, the efficiency of elements changes with user hand depending on the level of interaction between each element and the hand. Direct blockage of AEs by the hand resulted in efficiencies of as low as 5 %, while at the same time, other AEs retained an efficiency of up to 75 %. At the center frequency of 5.5 GHz, the free space capacity is 11.1, 49.5 and 83.2 bit/s/Hz with two, ten and 18 AEs, respectively. However, the use of the mobile terminal in the proximity of the user hand degraded these levels by 11 % (two AEs), 35 % (10 AEs) and 31 % (18 AEs). Finally, multiplexing efficiency showed that capacity degradation is caused mainly by the degradation of AEs efficiency, whereas the impact of low correlation between AEs is found to be an insignificant factor. In addition to the capacity analysis, gain and diversity gain of the maximal ratio combining technique was also investigated.
Automated Detection of Microaneurysm using Textural Analysis

Diabetic retinopathy (DR), a common medical condition in diabetes patients, is a major cause of blindness. DR affects those who have diabetes for 20 years or more. By proper monitoring, major cases of DR could be rectified. The longer a person has diabetes, the higher his or her chances of developing diabetic retinopathy. Each year, DR accounts for 12% of all new cases of blindness. The algorithm proposed in this paper focuses on detection of DR using cross sectional and local binary pattern (LBP) features. Proposed system was tested on a database of 150 fundus images with mild to moderate signs of microaneurysms (MAs). Healthy and DR images were distinguished using three popular classifiers, among artificial neural network (ANN) shows highest accuracy of 92% and sensitivity of 100%.
A Machine Learning-Based Comparative Study for the Classification of Septic Shock Using Microarray Gene Expression Data

Septic shock is metabolic deviations at the cellular and cardiovascular level that causes enhanced risk of mortality in patients being treated in ICU and is prevalent all over the globe. Microarray gene profiling technology allows professionals to monitor the gene expression of the whole cell is diseased as well as a control group. In this paper, we have retrieved microarray gene expression data on septic shock and have applied different machine learning models to classify it against healthy subjects. All machine learning models classified reasonably between septic shock patients and healthy subjects with relatively good accuracy. By using this methodology, we have selected sets of best features that provide probable biomarkers of septic shock. The features elimination algorithms including chi-square, RFE and correlation-based classified helped in increasing the accuracy and selection of best discriminator genes.