基于可听化技术实现对遥操作机器人传感器数据流的监视

为了验证听觉反馈在移动机器人遥操作中应用的可行性,作者应用可听化技术设计了一种传感器信息-声音映射方案,并在此基础上进行了心理物理学实验。初步的实验结果表明,通过听觉反馈遥操作者完全可以对传感器数据流进行有效地监视。     

A Data Download Method from RSUs Using Fog Computing in Connected Vehicles

Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and an RSU has poor connection quality, the efficiency and speed of the data download from the RSU is decreased. This problem affects the quality of the user experience. Therefore, it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed. The proposed method maximizes download speed from an RSU using a machine learning algorithm. To collect and learn from network data, fog computing is used. A fog server is integrated with the RSU to perform computing. If the algorithm recognizes that conditions are not good for mass data download, it will not attempt to download at high speed. Thus, the proposed method can improve the efficiency of high speed downloads. This conclusion was validated using extensive computer simulations.     

A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019. It affects the whole world through person-to-person communication. This virus spreads by the droplets of coughs and sneezing, which are quickly falling over the surface. Therefore, anyone can get easily affected by breathing in the vicinity of the COVID-19 patient. Currently, vaccine for the disease is under clinical investigation in different pharmaceutical companies. Until now, multiple medical companies have delivered health monitoring kits. However, a wireless body area network (WBAN) is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient. The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure. If COVID-19 affected patient is monitored through WBAN sensors and network, a physician or a doctor can guide the patient at the right time with the correct possible decision. This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein, a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output. Security cipher helps to avoid wireless network issues like packet loss, network attacks, network interference, and routing problems. Monte Carlo based covid-19 detection technique gives 90% better results in terms of time complexity, performance, and efficiency. Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity, performance, and efficiency and thus, is advocated as a significant application for lessening hospital expenses.     

Drone-based AI/IoT Framework for Monitoring,Tracking and Fighting Pandemics

Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two main systems; AI/IoT for COVID-19 monitoring and drone-based IoT system for sterilizing. The two systems are integrated with the IoT paradigm and the developed algorithms are implemented on distributed fog units connected to the IoT network and controlled by software-defined networking (SDN). The proposed work is built based on a thermal camera mounted in a face-shield, or on a helmet that can be used by people during pandemics. The detected images, thermal images, are processed by the developed AI algorithm that is built based on the convolutional neural network (CNN). The drone system can be called, by the IoT system connected to the helmet, once infected cases are detected. The drone is used for sterilizing the area that contains multiple infected people. The proposed framework employs a single centralized SDN controller to control the network operations. The developed system is experimentally evaluated, and the results are introduced. Results indicate that the developed framework provides a novel, efficient scheme for monitoring and fighting COVID-19 and other future pandemics.     

Nanoscale Sensor Technologies for Disease Detection via Volatolomics

The detection of many diseases is missed because of delayed diagnoses or the low efficacy of some treatments. This emphasizes the urgent need for inexpensive and minimally invasive technologies that would allow efficient early detection, stratifying the population for personalized therapy, and improving the efficacy of rapid bed‐side assessment of treatment. An emerging approach that has a high potential to fulfill these needs is based on so‐called “volatolomics”, namely, chemical processes involving profiles of highly volatile organic compounds (VOCs) emitted from body fluids, including breath, skin, urine and blood. This article presents a didactic review of some of the main advances related to the use of nanomaterial‐based solid‐state and flexible sensors, and related artificially intelligent sensing arrays for the detection and monitoring of disease with volatolomics. The article attempts to review the technological gaps and confounding factors related to VOC testing. Different ways to choose nanomaterial‐based sensors are discussed, while considering the profiles of targeted volatile markers and possible limitations of applying the sensing approach. Perspectives for taking volatolomics to a new level in the field of diagnostics are highlighted.     

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.     

FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps identify the service rates of the devices, the availability of the system, and the number of jobs in the Industry 4.0 systems, which helps applications process data with a reasonable response time. An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application, which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time. After implementing the proposed model with different sizes of fog services in Industry 4.0 applications, FogQSYM provides a lower response time than the existing optimized response time model. It should also be noted that the average response time increases when the arrival rate increases.     

A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams

With the rapid advancement of sensor technology, a huge amount of data is generated in various applications, which poses new and unique challenges for statistical process control (SPC). In this article, we propose a nonparametric adaptive sampling (NAS) strategy to online monitor nonnormal big data streams in the context of limited resources, where only a subset of observations are available at each acquisition time. In particular, this proposed method integrates a rank-based CUSUM scheme and an innovative idea that corrects the anti-rank statistics with partial observations, which can effectively detect a wide range of possible mean shifts when data streams are exchangeable and follow arbitrary distributions. Two theoretical properties on the sampling layout of the proposed NAS algorithm are investigated when the process is in control and out of control. Both simulations and case studies are conducted under different scenarios to illustrate and evaluate the performance of the proposed method. Supplementary materials for this article are available online.     

A Novel Approach to Data Encryption Based on Matrix Computations

In this paper, we provide a new approach to data encryption using generalized inverses. Encryption is based on the implementation of weighted Moore–Penrose inverse A^†_MN(nxm) over the nx8 constant matrix. The square Hermitian positive definite matrix N_8x8 p is the key. The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge. We have provided NIST (National Institute of Standards and Technology) quality assurance tests for a random generated Hermitian matrix (a total of 10 different tests and additional analysis with approximate entropy and random digression). In the additional testing of the quality of the random matrix generated, we can conclude that the results of our analysis satisfy the defined strict requirements. This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications. In the experimental part of this paper, we give a comparison of encryption methods between machine learning methods. Machine learning algorithms could be compared by achieved results of classification concentrating on classes. In a comparative analysis, we give results of classifying of advanced encryption standard (AES) algorithm and proposed encryption method based on Moore–Penrose inverse.     

A Statistical Control Chart for Monitoring High‐dimensional Poisson Data Streams

Multivariate count data are popular in the quality monitoring of manufacturing and service industries. However, seldom effort has been paid on high‐dimensional Poisson data and two‐sided mean shift situation. In this article, a hybrid control chart for independent multivariate Poisson data is proposed. The new chart was constructed based on the test of goodness of fit, and the monitoring procedure of the chart was shown. The performance of the proposed chart was evaluated using Monte Carlo simulation. Numerical experiments show that the new chart is very powerful and sensitive at detecting both positive and negative mean shifts. Meanwhile, it is more robust than other existing multiple Poisson charts for both independent and correlated variables. Besides, a new standardization method for Poisson data was developed in this article. A real example was also shown to illustrate the detailed steps of the new chart. Copyright © 2016 John Wiley & Sons, Ltd.     

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification (MDC) refers to the application of classification methods on medical datasets. This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis. To gain experts’ trust, the prediction and the reasoning behind it are equally important. Accordingly, we confine our research to learn rule-based models because they are transparent and comprehensible. One approach to MDC involves the use of metaheuristic (MH) algorithms. Here we report on the development and testing of a novel MH algorithm: IWD-Miner. This algorithm can be viewed as a fusion of Intelligent Water Drops (IWDs) and AntMiner+. It was subjected to a four-stage sensitivity analysis to optimize its performance. For this purpose, 21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine. Interestingly, there were only limited differences in performance between IWD-Miner variants which is suggestive of its robustness. Finally, using the same 21 datasets, we compared the performance of the optimized IWD-Miner against two extant algorithms, AntMiner+ and J48. The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner, as confirmed by the Wilcoxon nonparametric statistical test. Results suggest that IWD-Miner is more efficient than AntMiner+ as measured by the average number of fitness evaluations to a solution (1,386,621.30 vs. 2,827,283.88 fitness evaluations, respectively). J48 exhibited higher accuracy on average than IWD-Miner (79.58 vs. 73.65, respectively) but produced larger models (32.82 leaves vs. 8.38 terms, respectively).     

Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient’s health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.     

Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using decision-based multimodal fusion process. In this view, this research article presents a new multimodal fusion-based IDS to secure the healthcare data using Spark. The proposed model involves decision-based fusion model which has different processes such as initialization, pre-processing, Feature Selection (FS) and multimodal classification for effective detection of intrusions. In FS process, a chaotic Butterfly Optimization (BO) algorithm called CBOA is introduced. Though the classic BO algorithm offers effective exploration, it fails in achieving faster convergence. In order to overcome this, i.e., to improve the convergence rate, this research work modifies the required parameters of BO algorithm using chaos theory. Finally, to detect intrusions, multimodal classifier is applied by incorporating three Deep Learning (DL)-based classification models. Besides, the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation platform. To validate the outcome of the presented model, a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset repository. The proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%, precision of 98.93% and detection rate of 99.59%. The results assured the betterment of the proposed model.     

Novel Framework for Generating Criminals Images Based on Textual Data Using Identity GANs

Text-to-image generation is a vital task in different fields, such as combating crime and terrorism and quickly arresting lawbreakers. For several years, due to a lack of deep learning and machine learning resources, police officials required artists to draw the face of a criminal. Traditional methods of identifying criminals are inefficient and time-consuming. This paper presented a new proposed hybrid model for converting the text into the nearest images, then ranking the produced images according to the available data. The framework contains two main steps: generation of the image using an Identity Generative Adversarial Network (IGAN) and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic theory. The IGAN has the same architecture as the classical Generative Adversarial Networks (GANs), but with different modifications, such as adding a non-linear identity block, smoothing the standard GAN loss function by using a modified loss function and label smoothing, and using mini-batch training. The model achieves efficient results in Inception Distance (FID) and inception score (IS) when compared with other architectures of GANs for generating images from text. The IGAN achieves 42.16 as FID and 14.96 as IS. When it comes to ranking the generated images using Neutrosophic, the framework also performs well in the case of missing information and missing data.     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy. In order to evaluate the proposed framework, we conducted a comprehensive set of experiments to validate the applicability of DL-HAR. Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.     

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.     

A Self‐Assembled Biocompatible Nanoplatform for Multimodal MR/Fluorescence Imaging Assisted Photothermal Therapy and Prognosis Analysis

The need for better imaging assisted cancer therapy calls for new biocompatible agents with excellent imaging and therapeutic capabilities. This study successfully fabricates albumin‐cooperated human serum albumin (HSA)‐GGD‐ICG nanoparticles (NPs), which are comprised of a magnetic resonance (MR) contrast agent, glycyrrhetinic‐acid‐modified gadolinium (III)‐1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetraacetate (GGD), and a fluorescence (FL) dye, indocyanine green (ICG), for multimodal MR/FL imaging assisted cancer therapy. These HSA‐GGD‐ICG NPs with excellent biocompatibility are stable under physiological conditions, and exhibit enhanced T₁ contrast capability and improved fluorescence imaging capacity. In vitro experiments reveal an apparent effect of the NPs in killing tumor cells under low laser irradiation, due to the enhanced photothermal conversion efficiency (≈85.1%). Importantly, multimodal MR/FL imaging clearly shows the in vivo behaviors and the efficiency of tumor accumulation of HSA‐GGD‐ICG NPs, as confirmed by a pharmacokinetic study. With the guidance of multimodal imaging, photothermal therapy is subsequently conducted, which demonstrates again high photothermal conversion capability for eliminating tumors without relapse. Notably, real‐time monitoring of tumor ablation for prognosis and therapy evaluation is also achieved by MR imaging. This strategy of constructing nanoplatforms through albumin‐mediated methods is both convenient and efficient, which would enlighten the design of multimodal imaging assisted cancer therapy for potential clinical translation.     

A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

Vendor lock-in can occur at any layer of the cloud stack-Infrastructure, Platform, and Software-as-a-service. This paper covers the vendor lock-in issue at Platform as a Service (PaaS) level where applications can be created, deployed, and managed without worrying about the underlying infrastructure. These applications and their persisted data on one PaaS provider are not easy to port to another provider. To overcome this issue, we propose a middleware to abstract and make the database services as cloud-agnostic. The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers. It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases. NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance, availability, and scalability. The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system. A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database (move existing data from a database on one cloud to a new database even on a different cloud). Although the middleware adds some overhead compared to the native code for the cloud services being used, the experimental evaluation on Twitter (a Big Data application) data set, proves this overhead is negligible.     

基于情绪认知的家用儿童慢性病医疗器具设计

目的 探究情绪认知视角下,家用儿童慢性病医疗器具的设计方法。方法 通过理论研究,发现将情绪认知引入家用儿童慢性病医疗器具产品设计的意义和机会。对儿童的认知及情绪发展规律进行研究,并结合对慢性病患儿负性情绪应激源及诊疗特点的研究,总结出慢性病患儿的情绪认知特性,综合前期研究结果,明确基于情绪认知理论的家用儿童慢性病医疗器具产品的设计方法。结论 情绪对慢性病患儿的康复具有重要意义。设计师在家用儿童慢性病医疗器具的设计上应充分考虑慢性病患儿在物质环境、生理状况、社会认知等方面的情绪认知特性,从物质层面影响患儿对周围事物的情绪感知,在行为层面对患儿的情绪进行适当引导,在认知层面对患儿的情绪进行有效调节,帮助患儿以积极的心态面对家庭治疗,使其早日恢复健康。