Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

On increasing the sensitivity of moving average control chart using auxiliary variable

In the statistical process control, the most useful tool to monitor the manufacturing processes in the industries is the control chart. Quality practitioners always desire the charting structure that identifies sustainable changes in the monitoring processes. The sensitivity of the control chart is improved when additional correlated auxiliary information about the study variable is introduced. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. In this study, auxiliary information-based moving average (AB-MA) control chart is designed for efficient monitoring of shifts in the process location parameter. The performance of the AB-MA control chart is evaluated and compared with existing charts using average run length and other run length characteristics. The comparison reveals that the AB-MA control chart outperforms the competitors in detecting the small and medium changes in the process location parameter. The application of the proposal is also provided to implement it in real situation.     

Control of cone-jet geometry during electrospray by an electric current

A new electrospray system has been successfully developed by employing a proportional–integral–derivative control action to maintain an electric current at a certain value. A polymer precursor solution containing polyethylene glycol was used to examine the performance of this system. The result showed that cone-jet geometry could be controlled easily by adjusting the electric current. The length of the cone-jet decreased as the electric current was increased, in a correlation that followed power law. We also found that the cone-jet observed during electrospraying was stable and robust with no disturbance during long periods of use (up to 4000 s). The present study is very useful for further development of high precision aerosol generators and particle synthesis.     

AC Potential-Dependent Concentration Variation and Domain Wall Pinning in Co1−x Zn x (x=0.4−0.5) Nanorods

The Co_1?x Zn _x (x=0.4?0.5) nanorods were synthesized via an AC electrochemical deposition method into anodized aluminum oxide (AAO) templates at different voltages ranging from 10 to 18 V, and nanorods of varying concentrations of Co and Zn were obtained. The characterization tools were used to examine different aspects of nanorods, e.g., shape, size, morphology, chemical composition, and magnetic behavior. Scanning electron microscope (SEM) images show that CoZn nanorods have length L=1μm and diameter d=50 nm. The grain size was calculated to be 25.4 nm using an X-ray diffraction (XRD) technique. The XRD also shows some other phases of ZnCoO. The M?H loops measured by a vibrating sample magnetometer (VSM) at room temperature show pure ferromagnetic behavior at all AC potentials. The nanorods show magnetic isotropic behavior due to strong magnetic interactions and presence of random nanorods. The potential-dependent coercivity H _c and saturation magnetization M _s show a non-linear curve which is explained on the basis of magnetic islands and domain wall pinning. This study is useful to tune the magnetic properties of nanorods by a simple and low-cost technique.     

Coverless Steganography for Digital Images Based on a Generative Model

In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information steganographic scheme based on generative model, compared with the traditional image steganography. The transmitted image is not embedded with any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our scheme has high capacity, security and reliability.     

基于团簇式的GH2132高温合金成分协同变化关系

GH2132(A286)是析出强化型铁基高温合金,其含有多种合金化元素,为避免不合适的成分选择导致的综合性能失配,通过重点分析东北特钢提供的产品成分,解析国标成分区间的合理性,本文提出了一个更加合适的新成分标准形式。为此,引入“团簇加连接原子”结构模型,该模型将合金成分的结构载体表述为［中心-第一近邻］(连接原子)的团簇成分式形式。首先将合金化元素分为基体Fe、稳定奥氏体的(Ni,Mn)、稳定铁素体的(Cr,Mo,V,Si,Ti,Al)、以及不进入团簇式的(C,P,S,B)。通过分析国标规定的成分区间和实际合金成分,指出合金的实际成分区间远小于国标范围,并由16原子的成分式限定:Fe(8.5~9.0)±0.25(Ni,Mn)4±0.25(Cr,Mo,V,Si,Ti,Al)3~3.5。进而揭示了同类元素内部的质量百分比协同变化关系,即24.6≤Ni+Mn ≤28.0和17.4≤Cr+0.6Mo+V+1.7Si+1.1Ti+1.8Al≤20.4。由此更合理地限定Mn、Si元素成分区间,并对东北特钢的合金成分提供了改进建议。     

Structure Preserving Algorithm for Fractional Order Mathematical Model of COVID-19

In this article, a brief biological structure and some basic properties of COVID-19 are described. A classical integer order model is modified and converted into a fractional order model with as order of the fractional derivative. Moreover, a valued structure preserving the numerical design, coined as Grunwald–Letnikov non-standard finite difference scheme, is developed for the fractional COVID-19 model. Taking into account the importance of the positivity and boundedness of the state variables, some productive results have been proved to ensure these essential features. Stability of the model at a corona free and a corona existing equilibrium points is investigated on the basis of Eigen values. The Routh–Hurwitz criterion is applied for the local stability analysis. An appropriate example with fitted and estimated set of parametric values is presented for the simulations. Graphical solutions are displayed for the chosen values of (fractional order of the derivatives). The role of quarantined policy is also determined gradually to highlight its significance and relevancy in controlling infectious diseases. In the end, outcomes of the study are presented.     

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’ requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.