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In order to improve the multicast transmissions, a full-duplex (FD) user cooperation scheme is proposed. In this paper, the transmitter sends the common messages to two FD users, and each user forwards its received signals to its counterpart by amplify-and-forward (AF) scheme. Considering the imperfect self-interference (SI) cancellation at the users and the AF scheme, the residual self-interference and the additive noise (RIAN) is shown to be accumulated over time. This paper first analyzes the equivalent channel model of the considered system, as well as the asymptotic statistics of the accumulated RIAN. Then, with the forward and backward decoding schemes, the corresponding achievable rates are derived, and the optimal power allocation is obtained via solving a max-min problem. In particular, one-side cooperation scheme (i.e., only one user forwards its received signal to its counterpart) is shown to be optimal to achieve the best system performance.

In heterogeneous wireless sensor networks (HWSN), sensor nodes may fail due to running out of battery power or sudden damage. This causes HWSN being partitioned to a number of separate networks. Relay node placement is to add new relay nodes to partitioned HWSN such that the network recovers to wireless communication. Energy efficient routing in HWSN is useful for prolonging network lifetime and energy conservation. This paper attempts to solve relay node placement and energy efficient routing problems for HWSN. Both problems have seldom been studied together in the literature. This paper first constructs a mathematical model for both problems. For relay node placement problem, it is assumed that HWSN contains unreachable area, where sensor nodes could not be placed. For energy efficient routing, it is transformed to path length of wireless communication. As the problem is non-deterministic polynomial (NP) hard, a heuristic method called whale optimizer is used. The paper studies the effect of whale optimizer method with three adaptive schemes. Numerical simulations are done to test the proposed method for HWSN. The analysis and discussion show that the proposed method is useful to address NP hard relay node placement and energy saving problems for HWSN.

The intensive research in the fifth generation (5G) technology is a clear indication of technological revolution to meet the ever-increasing demand and needs for high speed communication as well as Internet of Thing (IoT) based applications. The timely upgradation in 5G technology standards is released by third generation partnership project (3GPP) which enables the researchers to refine the research objectives and contribute towards the development. The 5G technology will be supported by not only smartphones but also different IoT devices to provide different services like smart building, smart city, and many more which will require a 5G antenna with low latency, low path loss, and stable radiation pattern. This paper provides a comprehensive study of different antenna designs considering various 5G antenna design aspects like compactness, efficiency, isolation, etc. This review paper elaborates the state-of-the-art research on the different types of antennas with their performance enhancement techniques for 5G technology in recent years. Also, this paper precisely covers 5G specifications and categorization of antennas followed by a comparative analysis of different antenna designs. Till now, many 5G antenna designs have been proposed by the different researchers, but an exhaustive review of different types of 5G antenna with their performance enhancement method is not yet done. So, in this paper, we have attempted to explore the different types of 5G antenna designs, their performance enhancement techniques, comparison, and future breakthroughs in a holistic way.

Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle’s operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.

Most conventional fine-grained image recognitions are based on a two-stream model of object-level and part-level CNNs, where the part-level CNN is responsible for learning the object-parts and their spatial relationships. To train the part-level CNN, we first need to separate parts from an object. However, there exist sub-level objects with no distinctive and separable parts. In this paper, a multi-scale CNN with a baseline Object-level and multiple Part-level CNNs is proposed for the fine-grained image recognition with no separable object-parts. The basic idea to train different CNNs of the multi-scale CNNs is to adopt different scales in resizing the training images. That is, the training images are resized such that the entire object appears as much as possible for the Object-level CNN, while only a local part of the object is to be included for the Part-level CNN. This scale-specific image resizing approach requires a scale-controllable parameter in the image resizing process. In this paper, a scale-controllable parameter is introduced for the linear-scaling and random-cropping method. Also, a line-based image resizing method with a scale-controllable parameter is employed for the part-level CNNs. The proposed multi-scale CNN is applied to a food image classification, which belongs to a fine-grained classification problem with no separable object-parts. Experimental results on the public food image datasets show that the classification accuracy improves substantially when the predicted scores of the multi-scale CNN are fused together. This reveals that the object-level and part-level CNNs work harmoniously in differentiating subtle differences of the sub-level objects.

Orthogonal frequency division multiplexing (OFDM) is an important technology that has been widely used in broadband wireless communications. However OFDM has some drawbacks, one of which is the high peak-to-average power ratio (PAPR) problem. Many techniques for reducing PAPR thus have been proposed in the literatures, where clipping and companding are effective techniques and have gained a lot of research interest. Based on the study of clipping and companding, this paper proposes a hybrid approach to reduce the PAPR of OFDM signals. An iterative clipping and filtering (ICF) technique and an enhanced nonlinear companding (ENC) scheme are used as the typical clipping and companding techniques in this paper. The results of the analysis show that the computational complexity of the proposed method is slightly higher than that of ENC, but much lower than that of ICF. And the simulation results show that the proposed method has better performance on PAPR reduction and bit error rate (BER) in contrast to ICF. On the other hand, compared to ENC with equal PAPR reduction, the proposed method still achieves better performance on BER.

Electromagnetic interference issues are associated with high-speed switching of power converters. EMI modeling is an essential tool to study and control the EMI emission, enabling more efficient solutions. A comprehensive review and comparison of different modeling approaches for conducted emissions are provided in this paper, which can be used as a design guideline for engineers. For a motor drive application, common mode and differential mode conducted emissions are studied, and dominant noise production mechanisms are identified. Moreover, a review of various modeling techniques is presented for the main parasitic components of the system. Finally, time domain and frequency domain analysis approaches are explored along with the equivalent circuits which enable fast prediction of EMI emissions. This paper intends to help the reader develop an organized understanding of conducted emission modeling to assist them with a more efficient and electromagnetically-compatible design.

This paper reviews the background and related studies in the areas of cloud systems, intrusion detection and blockchain applications against cyber attacks. This work aims to discuss collaborative anomaly detection systems for discovering insider and outsider attacks from cloud centres, including the technologies of virtualisation and containerisation, along with trusting intrusion detection and cloud systems using blockchain. Moreover, the ability to detect such malicious attacks is critical for conducting necessary mitigation, at an early stage, to minimise the impact of disruption and restore cloud operations and their live migration processes. This paper presents an overview of cloud architecture and categorises potential state-of-the-art security events based on their occurrence at different cloud deployment models. Network Intrusion Detection Systems (NIDS) in the cloud, involving types of classification and common detection approaches, are also described. Collaborative NIDSs for cloud-based blockchain applications are also explained to demonstrate how blockchain can address challenges related to data privacy and trust management. A summary of the research challenges and future research directions in these fields is also explained.

This paper explains how agents in a social network can learn the arbitrary time-varying true state of the network. This is practical in social networks where information is released and updated without any coordination. Most existing literature for learning the true state using the non-Bayesian learning approach, assumes that this true state is fixed, which is impractical. To address this problem, the social network is modeled as a graph network, and the time-varying true state is treated as a multi-armed bandit problem. The few works that have applied multi-armed bandit to a social network did not take into consideration the adversarial effects. Therefore, this paper proposes two non-stochastic multi-armed bandit algorithms that can handle the time-varying true state, even in the presence of an oblivious adversary. Regret bounds on the algorithms are obtained, and the simulation performance shows that all agents can converge to the most stable state. The sublinearity of the proposed algorithms is also compared with two well-known non-stochastic multi-armed bandit algorithms.

In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as -45.3dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as -46.1dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

Channel estimation with received signal-to-noise ratio (SNR) feedback is promising and effective for practical wireless multiple-input multiple-output (MIMO) systems. In this paper, we investigate the channel estimation problem for the MIMO system with received SNR feedback, of which goal is to estimate the MIMO channel coefficients at a transmitter based on the received SNR feedback information from a receiver in the sense of minimizing the mean square error (MSE) of the channel estimation. For analysis, we consider two very common and widely adopted scenarios of fading: (i) quasi-static block fading and (ii) time-varying fading. In both fading scenarios, it is generally challenging to analytically tackle the channel estimation problem due to its nonlinearity and nonconvexity. To intelligently and effectively address this issue, deep learning is exploited in this paper. First, in the quasi-static block fading scenario, we propose a novel learning scheme for joint channel estimation and pilot signal design by constructing a deep autoencoder via a convolutional neural network (CNN). Also, in the time-varying fading scenario, a novel channel estimation scheme is developed by connecting a recurrent neural network (RNN) to a CNN. Moreover, in both fading scenarios, we present new and effective ways to train the proposed schemes using generative adversarial networks (GANs) to address the practical issue of a limited number of actual channel samples (i.e., real-world data) required for training. Through extensive numerical simulations, we demonstrate effectiveness and superior performance of the proposed schemes.

In discrete multi-tone (DMT) transmission based digital subscriber line (DSL) systems, a cyclic prefix (CP) is added to each symbol before transmission, where the length of the CP is larger than the estimated channel impulse response (CIR) length. This ensures the elimination of inter-symbol interference (ISI) and inter-carrier interference (ICI) between the carriers of the same symbol, and allows for single tap frequency domain equalizers and crosstalk cancellation at the receiver. Recently, long reach xDSL (LR-xDSL) has been proposed to extend the reach of conventional DSL systems. With the extended loop lengths, the required CP length increases, in order to match the larger CIR length. The longer CP adds a large overhead and results in overall throughput loss. A more efficient way to deal with extended loop lengths is to use a channel shortening filter - commonly referred as a time domain equalizer (TEQ), to reduce the length of the CIR to the length of CP. This paper focuses on minimum mean square error (MMSE) based multiple input multiple output (MIMO) TEQ design for LR-xDSL MIMO channel shortening. Constraints are applied to the minimization problem to eliminate the trivial solution. This paper proposes two new constraints for the MMSE based MIMO TEQ design for upstream scenarios, which result in a lower complexity and provide better (or similar) performance compared to existing MMSE based MIMO TEQ design methods. Furthermore, a diagonal MIMO TEQ with lower memory requirement and lower computational complexity is presented based on the proposed constraints, which can be applied in upstream as well as downstream scenarios.

One of the key goals of future 5G networks is to incorporate many different services into a single physical network, where each service has its logical network isolated from other networks. Besides, Network Slicing (NS) is considered as the key technology for meeting the service requirements of diverse application domains. Recently, NS faces several algorithmic challenges for 5G networks. This paper provides a review related to NS architecture with a focus on relevant Management and Orchestration (MANO) architecture across multiple domains. In addition, this survey paper delivers a deep analysis and a taxonomy of NS algorithmic aspects. Finally, this paper highlights some of the open issues and future directions.

In this paper, a new method for reducing the ripples of torque and flux is proposed for the predictive torque control (PTC) of permanent magnet synchronous motors. The conventional PTC uses equations to analyze the relationship between the electrical torque and voltage with a fixed magnitude of reference voltage. Consequently, flux and torque ripples increase significantly at low-speed operation. This paper proposes an improved PTC algorithm using a simple duty-ratio regulator. The proposed method will minimize the torque error by calculating an appropriate torque minimization function to mitigate the induced torque and flux ripple at low speed operation. This torque minimization function is synthesized into the space vector pulse-width modulation block to reduce the torque ripple induced owing to the fixed time duration. The proposed scheme considerably reduces the torque and flux ripples and establishes a fast-dynamic response of the torque in the transient state. Simulation and experimental results show that the proposed method achieves a more efficient steady-state performance and a faster step response compared with the conventional PTC.

A $(t,r)$ -function secret sharing ( $(t,r)$ -FSS) scheme allows a dealer to secret-share a function $f$ among $r$ parties as $r$ secret keys $k_{1},\ldots,k_{r}$ such that for any input $x$ the parties can compute $r$ output shares that allow the reconstruction of $f(x)$ but any $\leq t$ of the parties cannot learn any information about $f$ . FSS schemes for point functions have been constructed under the name of distributed point functions (DPFs). The existing DPFs are computationally secure and based on the existence of PRGs or OWFs. As a result, the protocols where DPFs work as building blocks are computationally secure as well. In this paper, we study information-theoretically secure $(t,r)$ -FSS (called $(t,r)$ -itFSS) and propose a generic transformation from information-theoretic private information retrieval (PIR) schemes to itFSS schemes for point functions. We measure the efficiency of itFSS with its communication complexity, which can be defined as the total length of the secret keys and the output shares, maximized over the choices of $f$ and $x$ . By instantiating the generic transformation, we obtain $(t,r)$ -itFSS schemes for a variety of choices of $(t,r)$ , which have sublinear (in the functions’ domain size) communication complexity. How to make sure that the parties’ shares of $f(x)$ do not reveal more information than what needed to compute $f(x)$ is an interesting problem. An itFSS with this property is called function-private. In this paper, we also define a parameter called the mutual rate of itFSS in order to measure the amount of information that will be leaked by the parties’ output shares. We calculate the mutual rates for several specific itFSS schemes. We also define computational function privacy and propose a 2-party itFSS scheme with computational function privacy.

Integrating cyber, physical, and social spaces together, cyber-physical-social systems (CPSS) bring more conveniences to humans. For practical applications and user convenience, it is essential that the Big Data produced in CPSS be stored in the distributed storage systems of CPSS. In this paper, we study the fault tolerance scheme for distributed storage systems of CPSS, and propose a framework that can recover multiple failed nodes simultaneously. Considering the reliability of storage nodes in distributed storage systems, the research on locally repairable codes has mostly focused on repairing failed nodes within each repair group. However, when entire repair groups have failed, existing locally repairable codes cannot repair more than one failed group. In this paper, local codes with cooperative repair that can recover more than one failed group are proposed. Specifically, the proposed local codes are constructed based on minimum storage regenerating (MSR) codes, and have an interleaving structure among the local codes, so that the parity symbols of any local code can be generated from the MSR codes in its two adjacent local codes. Taking advantage of this property, more than one failed local group can be repaired cooperatively by their adjacent local groups with lower repair locality. Furthermore, the key parameters of local codes with cooperative repair are derived. Theoretical analysis and simulation results show that, compared with previous codes with local regeneration, our codes have higher bandwidth overhead when repairing failed nodes, but advantages in storage overhead and repair locality either for repair of a single failed node or one failed local group. Moreover for a single failed local group, local codes with cooperative repair achieve almost the same tradeoff curve of storage overhead and bandwidth overhead as MSR-local codes and minimum bandwidth regenerating local (MBR-local) codes.

This paper proposes Single Carrier-FDMA radar, which is not only a solution to the shortcomings of OFDM radars but also offers improved Ambiguity Function and autocorrelation properties. Unlike OFDM, the proposed SC-FDMA radar (with interleaved subcarrier mapping) does not suffer from high peak to average power ratio (PAPR) issue and hence does not affect the efficiency of the transmitter in OFDM signals. Levanon and Mozeson's power amplifier. SC-FDMA utilizes all the benefits of single-carrier modulation, orthogonal frequency division multiplexing and frequency domain equalization. The wideband characteristics of SC-FDMA are beneficial in achieving high range resolution for radar applications. The paper presents a complete architecture of SC-FDMA radar with interleaved subcarrier mapping. The proposed radar system is analyzed for its ambiguity function, auto-correlation properties, and PAPR. The performance of the proposed radar waveform is compared with those of the notable existing radar waveforms including a standard OFDM radar, CAN, Hadamard, and PWCIA. It is observed from the comparative analysis, that the proposed scheme outperforms OFDM and the other notable waveforms. The proposed work can be extended to develop multiple tasking radars as the different source signals can correspond to independent tasks to be performed simultaneously.

In recent years, enterprise service bus (ESB) has become a favorable adoption as a technology category in the IT industry as it provides secure and guaranteed delivery of services. The elasticity of Enterprise Service Bus (ESB) enables numerous applications to exchange information makes it a significant middleware layer responsible for transferring information in a Service-Oriented Architecture (SOA). ESB is presently the utmost promising tactic for the integration of business applications in distributed and diverse environments. It also offers essential infrastructure support for transforming messages or data, intelligent routing, and protocol transformation. The idea of ESBs emerged from the requirements to move out from traditional integration patterns, that becomes difficult to manage with the passage of time. Our study aim is to understand and provide ongoing research topics, challenges and future directions concerning ESB applications. A systematic mapping study (SMS) is therefore implemented to categorize the selected papers into the following classification: contribution type, ESB applications, research type and their approaches. We have extracted a total of twenty-two papers for this systematic study and they are classified according to defined criteria. The findings of this SMS are discussed and researchers were provided with suggestions on possible directions for future research.

Permission delegation in access control provides the subject with a second method to obtain object permissions in addition to permission granting. It is especially applicable when the owner and manager of the object are inconsistent. With the development of the Internet of Things, there are more and more scenes where object owners and managers are inconsistent, but the research on permission delegation in access control based on blockchain is not perfect. Therefore, once implemented in these blockchain-based access control algorithms, the permission delegation tends to have an unauthorized access. Based on the analysis of the causes for the unauthorized access vulnerability, this paper proposes a token-constrained permission delegation algorithm (TCPDA), which converts the access control policy corresponding to permissions into constraints for permission use, embeds the constraints in the permission token, and forms constraints on the transfer of tokens. Only subjects that meet the constraint conditions can receive tokens, thereby solving unauthorized access vulnerability caused by permission delegation. Since not all access control models can transform strategies into constraints and integrate them into blockchain tokens, this paper also proposes a permission delegation algorithm for decision-making entities to make desirable decisions. Finally, the security analysis shows that the two proposed schemes can overcome the unauthorized access vulnerability caused by permission delegation, and the algorithm performance is analyzed through experiments.

Wireless technology and the latest developments in a mobile object, has led to a Mobile Ad Hoc network (MANET), which is a collection of mobile nodes that are communicating with each other without requiring any fixed infrastructure. Due to the dynamic nature with a decentralized system, these networks are susceptible to different attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), Sink Hole Attack (SHA) and many more. Several researchers have worked for the detection and mitigation of individual attacks, either GHA or BHA nodes. But the protection of MANET against a dual-threat is scarce. In this paper, the protection against dual attacks has been presented for BHA and GHA by using the concept of Artificial Neural Network (ANN) as a deep learning algorithm along with the swarm-based Artificial Bee Colony (ABC) optimization technique. The performance of the system has been increased by the selection of appropriate and best nodes for data packets transmission which is explained in the result section of this paper. For the network designing and simulation purposes, MATLAB software is used with communication and neural network toolboxes. The examined results show that the presented protocol performs better in contrast to the existing work under black hole as well as gray hole attack condition. A mobile ad hoc network (MANET) is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure.

In this paper we study the effect of user mobility on the performance of wireless digital receivers operating in the presence of fading, shadowing, and path loss. Recently, it was shown in (Lopez-Fernandez et al., 2018) that the distribution of the received power of a mobile receiver is given by the incomplete moment of the underlying static fading distribution. In this paper, we consider the random waypoint mobility model and show that, in fact, other performance metrics of the mobile receiver can be expressed in terms of statistical characterizations of the fading distribution in the static case. We consider the moment generating function of the received signal-to-noise ratio (SNR), the average bit error rate and the average channel capacity and present analytical results for these performance metrics in different generalized fading environments. Asymptotic expressions are also derived that converge to the exact performance at high values of the SNR. Numerical results are provided to quantify the effects of network dimension, path loss coefficient and fading model parameters on the performance metrics of the mobile receiver.

Android is one of the most essential and highly used operating systems. Android permissions system is a core security component that offers an access-control mechanism to protect system resources and users’ privacy. As such, it has experienced continuous change over each Android release. However, previous research on the permissions system has employed static analysis techniques. Furthermore, most of these studies are outdated, covering older versions of Android. This paper aims to discuss the permissions system intensively to provide a nutshell overview of the Android platform’s access-control mechanism. The paper presents a comprehensive analysis of the Android permissions system since it was introduced in 2008 until now, accompanied by a formal model of its components. The results of the analysis reveal a continuous growth in the number of permissions since the original release—a growth of seven times in some permission categories. A case study has been conducted for the last five years’ versions of the top Android apps to examine the permissions system’s evolution and its attendant security issues from the applications’ perspective. Some apps showed an increase in permissions usage of 73.33% by the 2020 release. Additionally, the results of the case study contribute to the understanding of permissions deployment by both vendors and developers. Finally, a discussion of the permission-based security enhancements discloses that the Android permissions system faces various security issues. In general, this paper provides researchers and academics an up-to-date, comprehensive, self-contained reference study of the Android permissions system.

Tactile Internet (TI) is one of the main service types in 5G, which requires tight delay preservation for each end-to-end (E2E) transmission link of TI pair of users. Consequently, it is required a highly sophisticated resource allocation (RA) algorithm over cloud radio access networks (C-RANs). In this paper, we aim to address this problem based on admission control (AC) and power allocation in orthogonal frequency division multiple access (OFDMA) mode with considering joint fronthaul and access delay in C-RAN. The objective of our proposed RA problem is to minimize a total transmit power subject to E2E delays and C-RAN limitations, e.g., a capacity limitation of fronthaul and power transmission links. Due to tight E2E constraints, the RA problems suffers from an infeasibility issue, i.e., the requirements of all TI users are not guaranteed, which leads to high degradation of services of TI users. The proposed AC of this paper can overcome this practical challenge while RA can help to use the resources in the best possible extent. Through simulation results, we investigate diverse service satisfaction parameters such as service acceptance ratio (SAR) and transmit power. Simulation results reveal that by dynamic adjustment of the access and fronthaul delays, transmit power can be saved compared to the case in which the delay is fixed over access and fronthaul links. Moreover, the number of rejected users in the network is significantly reduced, and more users are accepted, i.e., the SAR increases. Simulation results demonstrate that our dynamic delay adjustment and RA along with AC can outperform the benchmark algorithm in this context up to 3 dB.

In recent years, electric vehicles with low cost and zero emission have been accepted and chosen by more and more people. In order to further shorten the charging time of electric vehicles, improve charging efficiency of electric vehicles and promote the development of green smart transportation, a new promising Energy Harvesting-Dynamic Wireless Charging (EH-DWC) system is currently much attracting extensive attention and research. EH-DWC system, on the one hand, can convert nature energy such as wind or solar nature energy into electricity energy by using energy harvesting technology, on the other hand, the system can directly provide charging service for the moving electric vehicle through wireless electromagnetic induction technology. However, during the charging process, since the electric vehicle and the EH-DWC need to communicate over an open wireless channel, it is inevitable to be attacked by some malicious, which can influence the charging process, privacy leakage or even property losses between the system and the electric vehicle. In this paper, we propose a lightweight secure management scheme for energy harvesting dynamic wireless charging system. The proposed scheme can achieve effective authentication, secure communication, privacy protection and reliable payment for the system. Moreover, considering that there are some weather that are not conducive to harvesting energy, the scheme divides the system into three different working states to ensure the safe operation. Finally, the security analysis and performance evolution prove our scheme is secure and efficient in the end of the paper.

As speed of communication data path is drastically improved in this decade due to the high data rate, evolutional technology is demanded to address the fast communication implementation. In this paper, we focus on data compression technology to speed up the communication data path. We have proposed a stream-based data compression called ASE coding. It compresses data stream based on the instantaneous data entropy without buffering and stalling for the compression processes. It is also suitable for hardware implementation. However, the stream-based data compression works heuristically with sensitive parameters that affect to the data compression ratio. If the parameters are statically configured, it does not follow the dynamic data entropy, and thus, the data compression performance becomes unstable. In this paper, we will disseminate the parameters, discuss the behaviors of those parameters and propose its autonomous adjustment methods. We will also propose adjustment algorithms for those parameters that follow the data entropy of the input data stream autonomously. Through experimental evaluations applying the algorithms, we will confirm the parameters are adjusted with depending on the data entropy in the data stream. And then, the compression ratio becomes stable as the compressor exploits the minimal entropy adaptively.

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations and dimension reductions. However, when it applied to real-world datasets, it shows shortcomings for the shortest path between all pairs of data points, which are based on the nearest neighborhood $G$ graph via the Dijkstra algorithm, which makes it a very time-consuming step. The other critical problem is the classical Isomap has a lack of topological stability on the nearest neighborhood $G$ graph. In this paper, we propose a novel technique called the FastIsomapVis for the above problems of the classical Isomap. The FastIsomapVis uses hierarchal divide, conquer, and combine approach through two algorithms, which are randomized division tree (KD-tree) and Dijkstra Buckets Double (DKD). The primary aim of the FastIsomapVis is to increase the efficiency and accuracy of the graph. This research paper focuses on transforming the high dimensional datasets into a low dimensional Isomap visualization. The FastIsomapVis makes it easy to construct an accurate $K$ nearest neighborhood $G$ graph and scale high dimensional data points into low dimensional space. Our proposed method is compared to the classical Isomap to verify its effectiveness and provide highly authentic results of the high dimensional datasets. The finding of the current study shows that our proposed method is much fastened than classical Isomap.

Orbital Angular Momentum(OAM) of Electromagnetic(EM) wave can be seen as a new degree of freedom(DoF) for wireless communication besides time and frequency. Uniform Circular Antenna Array(UCA) fed with signal of equal amplitude and equal phase difference on each antenna element is a good OAM generation method on radio frequency(RF) band. Previous researches have shown that UCA based OAM communication system is essentially a special case of Multiple-input Multiple-output(MIMO) system and elaborated the viewpoint from different perspectives. In this paper, we demonstrate the above viewpoint from a new perspective. Specifically, for UCA based OAM communication in ideal(line of sight(Los) and coaxial) channel, we systematically analyze the variations of radiation pattern and spatial correlation of UCA under different radius and distance settings. To extend our demonstration, we first simulate the channel capacity variation with the adjustment of UCA radius and distance, and then simulate the channel capacity of both UCA and uniform linear antenna array(ULA) based communication systems, and find that ULA can also achieve similar capacity performance as UCA when spatial distance scale is similar. As is known that MIMO communication system provides spatial DoF besides time and frequency, we can conclude that mode DoF of UCA based OAM essentially originates spatial DoF of UCA based on the analysis in the paper.

Conventional centralized methods use entire data to estimate the projection matrix of dimensionality reduction problem, which are not suitable for the network environment where the sensitive or private data are stored or there is no fusion center. In this paper, we develop a decentralized principal component analysis (DPCA) method to deal with the distributed data without sharing or collecting them together. The main contributions of this paper are as follows: i) The proposed DPCA method only needs the projection vector information communications among neighboring nodes other than the communications of the distributed data; ii) The decentralized projection vector determination problem is replaced by a set of subproblems with consensus constraints and the excellent processing capability of alternating direction method of multipliers (ADMM) is used to obtain the consistent projection vectors; iii) Especially, the integrating Lagrange programming neural networks (LPNN) is introduced to solve the projection vectors determination problem with the complex unitary and orthogonal constraints, and iv) the converge analysis of the proposed optimization problem is provided to ensure that the obtained projection vectors of the distributed method converge to those of the centralized one. Some simulations and experiments are given to show that the proposed algorithm is an alternative decentralized principal component analysis approach, and is suitable for the network environment.

A method to weaken the interference of antennas working in different frequencies is proposed in this paper. It uses a Frequency Selective Surface (FSS) placed between two different antennas. For one antenna, the FSS acts as an electromagnetically transparent structure, so the electromagnetic wave will penetrate the FSS and radiates into the free space. For the other one, the FSS acts as an artificial magnetic conductor, reflecting the incoming energy. With this design, two adjacent antennas could keep their individual performance especially radiation patterns normal. It is worth noting that although the FSS has been used in many aspects, such as reducing antenna's radar cross section or reducing the coupling between the identical antennas, its application in the field of `different antennas decoupling' is the first time. An embodiment is displayed in this paper for verification. Two antennas worked at different bands of 5G are placed very close with an FSS structure inserted. It turns out that the whole antenna has good electrical and radiation properties over the working band. This decoupling strategy can be very useful in 5G Massive MIMO application.

Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested model. Most recently, the evolutionary algorithms(EA), including genetic algorithm(GA), are exploited to calculate the parameters of variogram model, which can obtain a more accurate solution. These methods have some hyper-parameters to set and suffer from the well-recognized premature convergence and slow global convergence problem of EA. In this paper, a double elite co-evolutionary genetic algorithm(DECGA) and deep reinforcement learning(dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance. The DECGA can get the global optimal solution faster than GA with the help of dueling DQN, which can set the hyper-parameters according to the state of DECGA. To verify the effectiveness of the proposed method(DDQNGA), we conduct experiments on the agricultural heavy metal database. Experimental results demonstrate that our method can obtain parameter estimation more accurately. The method proposed in this paper have a certain practical value in the field of geostatistics and environmental engineering.

As more vehicles connect to the internet, they become an important and growing segment of Internet of Things (IoT). To enhance the performance of vehicle-to-network (V2N) communication with green objectives, satellite and unmanned aerial vehicle (UAV) are jointly applied to aid ground communication facility by leveraging the coordinated multi-point transmission technique. This paper investigates a novel architecture of V2N communication in the space-air-ground integrated IoT network consisted of ground base stations (BSs), a UAV as well as one satellite. In the V2N communication model, the UAV, receiving the requested data from the satellite, and BS cooperatively serve the ground vehicle. The goal of this paper is to maximize the achievable rate of the ground vehicle by jointly optimizing the transmit power allocations (i.e., UAV and ground BS) and UAV trajectory, subjecting to the UAV energy constraint, UAV transmission constraint as well as UAV mobility constraint. However, there exists an intractable issue of the formulated problem in a complicated non-convex form. To this end, we decompose it into two sub-problems by fixing UAV trajectory and the power allocations alternately. Specifically, the closed-form expressions are derived to solve the sub-problem with the given UAV trajectory. For the sub-problem with the given power allocations, the UAV trajectory is calculated by using successive convex approximation (SCA) technique. Through the alternation of two methods and iterative operation, the joint optimal solution to the problem is achieved. Finally, numerical results verify that UAV plays a pivotal role in overcoming the tradeoff between higher performance and green communication.

This paper focus on the two-tier downlink heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA). The aim is to jointly optimize user scheduling and power allocation to solve the problem of maximizing the energy efficiency (EE) of the NOMA HetNets. We formulate the basic principle of NOMA and develop the EE model of joint user scheduling, optimal power allocation factor among users on the same subchannel and power allocation across subchannels. Firstly, considering that the problem of formulation is a multi-objective optimization (MOO) problem, which is non-convex and NP-hard, we decouple the MOO problem into two single-objective optimization (SOO) problems: 1) joint the optimal power allocation factor of maximizing the EE among users on the same subchannel to solves the subchannel matching, and 2) the power allocation across subchannels. For subchannel matching, non-cooperative game and global optimal search (GOS) are respectively used to solve the power allocation factor among users on the same subchannel and user scheduling. For the power allocation factor among users on the same subchannel, the existence of Nash equilibrium (NE) is proved by introducing super-modular game. Besides, the Nash equilibrium point (NEP) of the game can be obtained by using the proposed algorithm. Then, for the correlation between users-subchannels, this paper proposes a non-cooperative game based user-subchannel global optimal search matching algorithm (NCG-US-GOSMA) with low-complexity. Finally, according to the obtained subchannel matching, the non-convex problem of power allocation across subchannels is converted into convex problem by successive convex approximation (SCA), and then solve it by iteration. Simulation results verify the effectiveness of the proposed scheme; in addition, the EE performance is better than the existing resource allocation algorithm and OFDMA scheme.

This paper is concerned with a multi-cell downlink (DL) massive multiple-input multiple-output (MIMO) system operating over spatially correlated Rician fading channels. We not only consider estimating channel state information (CSI) at the base station (BS), but also adopt beamforming training (BT) to obtain CSI at the users. With maximum-ratio transmission (MRT) or zero-forcing (ZF) employed at the BS to process the transmit signals, the paper derives closed-form expressions of the sum spectral efficiency for both cases: with and without BT. Based on the obtained closed-form expressions, we investigate the effect of the DL pilot length on the system performance for MRT and ZF precoding and with or without BT. Moreover, when the DL pilot length falls within different ranges, we find out whether using BT leads to better system performance. To address the energy efficiency (EE) maximization problem under the constraints of a given sum spectral efficiency and a maximum total DL transmit power, we transform the problem into a geometric program (GP), which can be solved more efficiently. In particular, we develop one iterative power allocation algorithm for the system with BT scheme. Simulation and numerical results demonstrate that the proposed power allocation algorithm can improve the system EE. Numerical results also show that when the BS uses MRT precoding, the sum spectral efficiency in the high signal-to-noise ratio (SNR) region is much improved with BT.

Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs.

© 2013 IEEE. In the last decades, baggage inspection based on X-ray imaging has been established to protect environments in which access control is of vital significance. In several public entrances, like airports, government buildings, stadiums and large event venues, security checks are carried out on all baggage to detect suspicious objects (e.g., handguns and explosives). Although improvements in X-ray technology and computer vision have made many X-ray detection tasks that were previously unfeasible a reality, the progress that has been made in automated baggage inspection is very limited compared to what is needed. For this reason, X-ray screening systems are usually being manipulated by human inspectors. Research and development experts who focus on X-ray testing are moving towards new approaches that can be used to aid human operators. This paper reports the state of the art in baggage inspection identifying three research fields that have been used to deal with this problem: i) X-ray energies, because there is enough research evidence to show that multi-energy X-ray testing must be used when the material characterization is required; ii) X-ray multi-views, because they can be an effective option for examining complex objects where the uncertainty of only one view can lead to misinterpretation; and iii) X-ray computer vision algorithms, because there are a plethora of computer vision approaches that can address many 3D object recognition problems. Besides, this paper presents useful public datasets that can be used for training and testing, and also summarizes the reported experimental results in this field. Finally, this paper addresses the general limitations and show new avenues for future research.

Reinforcement learning is a technique for power control in wireless communications. However, most research has focused on the deep Q-network (DQN) scheme, which outputs the Q-value for each discrete action, and does not match the continuous power control problem. Hence, this paper provides a deep deterministic policy gradient (DDPG) scheme for power control. A power selection policy designated an actor is approximated by a convolutional neural network (CNN), and an evaluation of a policy designated a critic is approximated by a fully connected network. These deep neural networks enable fast decision-making for large-scale power control problems. Moreover, to speed up the training process, this paper proposes a prioritized sampling technique, which samples the experiences that need to be learned with a higher probability. This paper simulates the proposed algorithm in a multiple sweep interference (MSI) scenario. The simulation results show that the DDPG scheme is more likely to achieve optimal policy than the DQN scheme. In addition, the DDPG scheme with prioritized sampling (DDPG-PS) converges faster than the DDPG scheme with uniform sampling.

Multiple phases sectionalized modulation (MPSM) jamming is a kind of blanket jamming method against linear frequency modulation (LFM) radar, which can produce range-controllable noise-like jamming effect by dividing the signal into multiple subsections in the time domain and modulating different phases on each subsection. However, the partial relationship between the jamming effect and jamming parameters is complicated, which decrease the controllability of MPSM jamming effect. By transforming the problem of the controllability of MPSM jamming effect into the problem of the optimization of MPSM jamming effect, an optimization algorithm based on genetic algorithm (GA) is proposed to solve the problem in this paper. In the optimization based on GA, first, the objective function is constructed according to the jamming evaluation, the parameters are coded, the population is initialized, and the fitness value of the initial population is calculated. Then, the selection operation, crossover operation and mutation operation are performed, the next population is generated and its fitness value is calculated. Finally, judge if reach the termination, continue or output the optimization result and its corresponding parameters. The optimization method proposed in this paper can not only effectively increase the controllability of MPSM jamming effect, but also optimize the jamming effect. The simulation results show that the optimization method is feasible and has strong stability.

In this paper, a chamfer distribution antenna array is designed to generate a deflecting circular-polarized vortex beam carrying orbital angular momentum (OAM) mode. When the vortex beam is designed with a deflection angle, the sidelobe of the vortex beam will deteriorate. In order to solve this problem, an optimized array design is presented in this paper. Inspired by the Chebyshev distribution array synthesis method, the antenna elements in the four corners of the antenna array are cut out to realize the chamfer distribution array. Therefore, the sidelobe level of the deflected vortex beam can be effectively suppressed. Moreover, a simplified feeding network is designed to reduce the feeding network complexity by using circularly polarized structural center antenna element. The designed antenna array is simulated, fabricated, and measured at center frequency of 4.25 GHz in C-band. The far-field radiation pattern of the deflected vortex beam and the near E-field OAM vortex beam distribution are simulated and measured to verify the effectiveness of the proposed design method for sidelobe suppression array.

Speed control of permanent magnet synchronous motors (PMSMs) without position sensors is preferable in some applications due to cost and mounting space concerns. For applications in which the operating speed is relatively stable, the dynamic performance and efficiency demands of PMSMs are not severe during the startup process. Under such circumstances, a sensorless controller based on back electromotive force (EMF) estimation combined with a simple startup strategy is a suitable solution. In this paper, an I-f (current-frequency) startup method and a sliding mode observer are adopted to develop a sensorless control system, and the startup process which employs the I-f method is analysed in detail. Based on the analysis, this paper proposes a simple and robust startup strategy with an adaptive compensator to realize the smooth transition from I-f control to EMF-based control. The proposed strategy allows quick startup from standstill under different load conditions without initial position estimation. Furthermore, the strategy does not introduce any additional control coefficients, and none of the original control coefficients need to be adjusted. The strategy is suitable for both surface and interior PMSMs, and a test bench based on an interior PMSM is built to validate its properties. The experimental results show that the motor can smoothly accelerate from standstill to its rated speed in less than 2 s in both loaded and non-loaded situations, and the transition between the two sensorless control methods does not cause any large speed or current fluctuations.

The contribution of this paper is two fold. It first analyses the flooding strategies for the Wireless Multihop Networks (WMNs) then it enhances the reactive and proactive routing protocols. For analysis purpose, we select four widely used flooding techniques for routing: i. traditional flooding, ii. Time-To-Live based Expanding Ring Search (TTL-based ERS) flooding scheme, iii. TTL-based Scope Routing (SR) flooding and iv. Multi-Point Relays (MPR) flooding. These techniques play a vital role and act as a backbone for routing protocols. Therefore, we compare efficiency of these techniques for six widely used routing protocols: Ad-hoc On-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), DYnamic MANET On-demand (DYMO), Fish-eye Scope Routing (FSR) and Optimized Link State Routing (OLSR). DSDV uses traditional flooding, AODV, DSR and DYMO use TTL-based ERS flooding, FSR uses TTL-based SR flooding and OLSR uses MPR flooding. This paper also presents mathematical models for flooding techniques and studies the affects of these techniques on their respective protocols in terms of energy and time consumption. This is done to measure the cost incurred by the routing protocols in the form of routing overhead and latencies. A novel contribution of this work is the enhancement in search set values and intervals of routing algorithms to improve the efficiency of selected existing protocols. A detailed comparison analysis of selected protocols with their default and enhanced routing algorithms in NS-2 is also a part of this work.

In this paper, the optimal linear filtering problem for linear discrete-time stochastic systems with random matrices, correlated noises and packet dropouts is studied where the random matrices are real and appear both in the the state and measurement equations. Using an equivalent transformation for random matrices and some results presented in this paper, an optimal linear filter for the system under consideration is developed. The developed optimal filter has a recursive pattern, and its computational complexity does not change with time. Number examples with simulation results are given to examine the performance of the developed optimal linear filter.

Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms’ memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms’ exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches $1500~urad$ before compensation. Consequently, the proposed method can effectively improve target tracking precision.

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

Handwriting was since the start of the history, a higher expression of human skills, and was used for documentation of experiences, and for communication. Existing writing technology require a writing tool, like a pen, and a dedicated writing surface, like paper, or more recently an electronic tablet. These accessories of writing, of writing tool and service, are not available in many daily life situations. Furthermore, the writing accessories, are not natural, in many cases are not ergonomic, and thus can cause fatigue, and in extreme cases contribute to muscular and neurological diseases. In this work, we suggest to step back in history and step forward in technology, and to create, for the first time, an alternate writing solution without any accessories, using one own finger as writing tool, and write on almost any surface. For this, we used directional pressure sensors attached to the fingernail. Changes in the pressure induced on the fingertip in different directions while writing, are projected to the fingernail, and then assessed as a voltage pattern by the sensor. Decoding the pattern, can reveal symbols like letters, punctuations, and writing commands. In this paper, we describe the new pressure sensing modality and tailor processing methods. We tested the new technology on two subjects having different writing patterns while writing alphabet and sentences on different surfaces. We reached letter detection of over 80% while writing on a table, and the word detection rate, was near 70%, after applying the correction algorithm include language priors. The results of this work can revolutionize the way people write and communicate using more convenient, and more approachable, finger-tip writing.

This article analyzes the serum electrolyte disturbances of patients through deep learning algorithms. Among the 104 patients with electrolyte disturbances, 6 cases of serum potassium, sodium, chloride, calcium, phosphorus, and magnesium electrolyte disturbances have occurred, the proportion of occurrence The order is sodium > chlorine > calcium > potassium > phosphorus > magnesium. This paper proposes a deep learning algorithm for serum electrolyte disorder, and analyzes and implements the functions at various levels according to the characteristics of the Hadoop framework. The system includes electronic medical records shared storage, distributed realization of definite learning algorithms, classification and recognition of myocardial ischemia by deep learning, and Web system assisting doctor diagnosis, which lays the foundation for the construction of serum electrolyte disorder scientific research data center. In order to explore this relationship, without artificially extracting features, a deep learning model of convolution and long-term and short-term memory circulation neural network cascade was proposed to determine the positive or negative myocardial ischemia by classifying serum disorders. Conduct clinical experiments, including patients with suspected coronary heart disease and coronary angiography as the research object, taking coronary angiography results as the detection standard. Experimental results show that the model has an accuracy of 89.0% for detecting myocardial ischemia, a sensitivity of 91.7%, and a specificity of 81.5%. A linear combination model of CNN and LSTM is proposed to classify and recognize serum electrolyte disorders. Determine the learning theory to dynamically model the ST-T segment in the ECG to obtain serum electrolyte disturbance, which more vividly shows the changes in electrical information under myocardial ischemia. In order to reveal the relationship between serum electrolyte disturbance and myocardial ischemia, this paper builds a neural network model to learn and train serum electrolyte disturbance data to realize the classification of positive or negative myocardial ischemia. Tests on serum electrolyte disturbance data collected in clinical experiments show that this model can better achieve early detection of myocardial ischemia.

The past couple of years have marked continued growth in the applications and services of the Internet of Things (IoT). This has attracted the attention of new operators as well as institutional, corporate, and private investors in every sector of the economy, and as a result, new businesses are springing up rapidly. These include many start-up companies that are producing various kinds of useful IoT devices and Smart Applications (smart apps). While this can be seen as a boost for innovation in the IoT, some of these companies produce IoT devices and smart apps with security vulnerabilities. In this paper, we propose the IoT Hardware Platform Security Advisor (IoT-HarPSecA), a security framework intended to provide support to such IoT producers. IoT-HarPSecA offers three functionality features, namely security requirement elicitation, security best practice guidelines for secure development, and above all, a feature that recommends specific LightWeight Cryptographic Algorithms (LWCAs) for both software and hardware implementations. Accordingly, IoT-HarPSecA is composed of three main components, namely Security Requirements Elicitation (SRE) component, Security Best Practice Guidelines (SBPG) component, and LightWeight Cryptographic Algorithms Recommendation (LWCAR) component, each of them servicing one of the aforementioned features. We implement a command-line tool in C++ to serve as an interface between users and the proposed framework. IoT-HarPSecA can be employed during the early stages of IoT systems design, and it can also be used to facilitate the implementation of security in existing IoT systems. This paper presents a detailed description, design, and implementation of the SRE, SBPG, and LWCAR components of the proposed framework. Using real-world practical scenarios, we show how IoT-HarPSecA can be used to elicit security requirements and recommend appropriate LWCAs based on user inputs. While a full performance evaluation of the SRE and SBPG components is beyond the scope of this paper, we present a detailed performance evaluation of the LWCAR component, which shows that IoT-HarPSecA can serve as a roadmap for secure IoT development.

This paper explores the immunological model and implements it in the domain of intrusion detection on computer networks. The main objective of the paper is to monitor, log the network traffic and apply detection algorithms for detecting intrusions within the network. The proposed model mimics the natural Immune System (IS) by considering both of its layers, innate immune system and adaptive immune system respectively. The current work proposes Statistical Modeling based Anomaly Detection (SMAD) as the first layer of Intrusion Detection System (IDS). It works as the Innate Immune System (IIS) interface and captures the initial traffic of a network to find out the first-hand vulnerability. The second layer, Adaptive Immune-based Anomaly Detection (AIAD) has been considered for determining the features of the suspicious network packets for detection of anomaly. It imitates the adaptive immune system by taking into consideration the activation of the T-cells and the B-cells. It captures relevant features from header and payload portions for effective detection of intrusion. Experiments have been conducted on both the real-time network traffic and the standard datasets KDD99 and UNSW-NB15 for intrusion detection. The SMAD model yields as high as 96.04% true positive rate and around 97% true positive rate using real-time traffic and standard data sets. Highly suspicious traffic detected in the SMAD model is further tested for vulnerability in the AIAD model. Results show significant true positive rate, closer to almost 99% of accurately detecting the file-based and user-based anomalies for both the real-time traffic and standard data sets.