In this paper, we evaluate the secrecy outage probability (SOP) of two user cooperative non-orthogonal multiple access (C-NOMA) network in the presence of a passive eavesdropper (E). The near user $(U_{1})$ harvests energy from the superimposed signal transmitted by source (S). The near user $(U_{1})$ acts as a relay and retransmits the message signal to a far user $(U_{2})$ . The E eavesdrops the signal for $U_{1}$ and $U_{2}$ transmitted from $\mathbf{S}$ and eavesdorp the signal of $U_{2}$ relayed from $U_{1}$ . An MRC is applied at E on two copies of signals obtained for $U_{2}$ . An analytical framework for evaluating SOP is presented. We obtain an analytical expression for SOP of $U_{1}$ , while an analytical expression of SOP of $U_{2}$ at high SNR approximation is obtained. Impact of several netwrok parameters such as target secrecy rate, energy harvesting parameters on SOP is indicated. Our analytical results are supported by monte-carlo simulation carried out in MATLAB.
Time variant coverage, called sweep coverage in wireless sensor networks has got attention from various re-searchers in recent time. In this problem, a set of mobile sensors are collectively monitoring certain area of interest (AoI). In various applications periodic monitoring of the AoI is sufficient. For a given time period, the objective of the sweep coverage problem is to find minimum number of mobile sensors so that the periodic monitoring can be guaranteed. This problem has been studied by various researchers in the context of point, area and barrier coverage problems. The sweep coverage problem on a graph is studied in [11]. In this problem, for a given input graph, minimum number of mobile sensors must be deployed such a way that all nodes of the graph must be visited by at least one mobile sensor. The sweep coverage problem is NP-hard and can not be approximated within a factor of 2 [16]. In this paper, we propose a fault-tolerant sweep coverage algorithm. For a given integer, using our proposed algorithm, the non-faulty sensors adjusted their movement and guarantees sweep coverage of all the nodes of an undirected weighted graph if at most sensors becomes faulty.
Many heuristic algorithms have been proposed in the literature to solve the team formation problem. The researchers considered a project as a set of skills selected randomly from the given pool of skills. But this leads to a skewed distribution of skills in the projects with many skills having very few experts, which we term as rare skills. In this work, we create a realistic bench-mark dataset for this problem. In general, any project/task in the industry can be seen to have a good mix of popular as well as rare skills. We first conduct an empirical study of the distribution of popular skills vs rare skills in the well-known DBLP (Digital Bibliography & Library Project) data set. The distribution of popularity of skills is shown to satisfy a power law with a heavy tail, indicating the presence of a large number of skills with very few experts and a small number of highly popular skills. We build a realistic a benchmark dataset using stratified random sampling to form tasks with various distributions of popular and rare skills. The classical team formation algorithms are evaluated using this new benchmark dataset. The evaluation is done with respect to the available communication costs in the literature as well as the execution time incurred by the algorithms. It has been observed from the experiments that all the measures show lower values of communication cost for tasks having higher proportion of popular skills.
Several professional societies have advocated for structured reporting in radiology, citing gains in quality, but some studies have shown that rigid templates and strict adherence may be too distracting and lead to incomplete reports. To gain the advantages of structured reporting while requiring minimal change to a radiologist's work-flow, the present work proposes a two-stage abstractive summarization approach that first finds the key findings in an unstructured report and then generates and organizes descriptions of each finding into a given template. The method uses a large manually annotated dataset and a taxonomy and other domain knowledge that were prepared in consultation with several practising radiologists. It can be used to structure reports dictated by radiologists and as post- and pre-processing steps for machine-learning pipelines. On the subtask of label extraction, the method achieves significantly better performance than previous rule-based approaches and learning-based approaches that were trained on automatically extracted labels. On the task of summarization, the method achieves more than 0.5 BLEU-4 score across 8 of the 10 most common labels and serves as a strong baseline for future experiments.
Flow table overflow attack on data plane devices is one of the prominent vulnerabilities in the Software Defined Networking (SDN) architecture. Flow table uses limited-sized TCAM to store the flow rules in the data plane. Unfortunately, TCAM based Flow tables are prone to various attacks such as memory saturation attacks, DDoS attacks, cross-plane attacks, Flow table overflow attacks, etc. These attacks lead to the starvation of benign requests, and saturation of network resources. However, the existing solutions are focused on the controller-based attack mitigation mechanism using OpenFlow switches which increases communication overhead between the control plane and data plane. This paper proposes a switch centric based in-network Flow table overflow attack detection and mitigation framework. We introduce IP_SourceGuard which keeps an audit of the flow rules by counting the threat value of a particular port. Mitigating the attack traffic when the threat value exceeds the limit of the warning threshold. Further, IP_SourceGuard blocks the attacker port from further not communicating it to the network. The solution has been implemented using the BMv2 software switch and determined that the solution reduces the Flow table utilization to 88%. From the result, it is observed that our solution mitigates the Flow table overflow attack in a real-time environment.
Nowadays Covid-19 is prevailing across the world, it has affected millions of populations across the world. The exponential increase in covid cases makes the health care system overwhelmed. Many testing methods are used for covid-19 detection like Rapid antigen test, RT-PCR test, etc. These tests have certain limitations, sometimes people got confused between respiratory infection and covid-19infection, as many symptoms are similar. So for confirming the disease, a chest x-ray is preferred. Covid-19 has similar symptoms of pneumonia, consolidation, and ground-glass opacities, in our approach we consider them as covid. In this paper, images are acquired from reputed hospitals and various online datasets used in Covidnet architecture. After accumulation, the dataset is verified by experienced radiologists. In our approach, we trained our models on various symptoms of covid19 like pneumonia, consolidation, ggopacities and finally on covid-19 dataset images. In our research, we have used single as well as ensemble models for classification. Models like densenet, efficient net, resnet, etc are used. Certain preprocessing techniques are used before passing the image dataset into training like adaptive histogram equalization, data augmentation methods, etc. Finally, a approach based on Deep Learning used for identification of covid 19. We are claiming 95% plus testing accuracy and 99% training accuracy. Beyond classification, we further generate the reports and localize the covid virus on Xray using various visualization methods. Further results are classified based on single and ensemble models on the in-house dataset.
DNA microarrays can simultaneously measure the expression level of thousands of gene within a particular mRNA sample that provide information about the state of cells and tissues. Though these expressive values are useful in cancer classification, and understand the mechanisms involved in the genesis of disease processes, however, only a few genes out of these thousands of genes contribute towards disease classification. On this basis, usage of feature selection algorithm is favourable, as the main goal of feature selection algorithm is to identify the relevant features (here genes) efficiently. In this paper, we have applied four filter Feature Selection (FS) methods, namely, Mutual Information (MI), Pearson Correlation Coefficient (PCC), Chi 2 , ReliefF along with three well-known classifiers, namely, Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbour (KNN) on six microarray datasets (both binary and multi-class) namely, Leukemia, Lung, Lymphoma and Leukemia, Gastric, SRBCT and Childhood Tumor and recorded the accuracies.
This work proposes an efficient cepstral-frequency domain based acoustic feature as a speaker identification solution for reliable biometric access control system. The Convolutional Neural Network (CNN) trained for this purpose uses the amalgamation of cepstral-frequency domain based acoustic features such as Power Normalized Cepstral Coefficients (PNCC) and Formant as PNCC-F. The PNCC-F with CNN classifier demonstrates an increase in identification efficacy. The speaker identification accuracy in clean, as well as noisy environment, has been used to evaluate the effectiveness of PNCC alone and in tandem with the formant feature. This work has been executed in a Python 3.8.8 environment using the standard database with 43 speakers called VidTIMIT. The efficiency of the PNCC-F feature was further evaluated in a real-time noisy environment by mixing babble, factory, and machine gun noises from NOISEX-92 database to speech samples with 0 to 20 dB of distortion. The proposed PNCC-F feature surpassed the conventional PNCC feature in a clean environment by 2.34%, and outperformed at all SNR levels for all different noises.
Internet of Things (IoT)-based applications are making a big impact in various sectors of our daily life, like smart cities, smart healthcare, smart industries, etc. IoT networks usually require a centralized server or certain authorities to manage and control devices scattered over the network. As a result of centralized management, IoT network security is put at risk. By integrating blockchain technology with IoT, we can create a decentralized and secure network. However, traditional blockchain implementation on IoT devices is not the best solution. It requires high computational capacity to achieve consensus over data communicated among devices, which is not feasible for resource constraint IoT devices. Furthermore, the data generated by IoT devices is massive, making it impossible to keep in blockchain after a certain point. Several models have been proposed in the literature to address these challenges. In 2021, Fusion Chain was proposed as a lightweight blockchain framework that was shown to be the lightest of all models. However, this model is not secure. In this paper, we highlight the various security issues within this framework. We then propose a secure version of Fusion chain, termed Fusion Chain-S, that overcomes these security issues and also provides enhanced storage and access policies for every data element. Also, to make it fault-tolerant, we have used COAP (Constrained Application Protocol) as an integrity channel. We provide a comprehensive security analysis of our framework and prove it using BAN logic. To the best of our knowledge, our improved model is the most lightweight and secure among the other peer models.
The use of Blockchain for consistent storage, management, and sharing of healthcare records has received a lot of recent attention as a more reliable and dependable alternative to cloud-based storage. With the potential of Blockchain technology in the health sector being explored, its practical applications in real-world healthcare scenarios on a large scale are facing challenges like scalability, the authenticity of records, and management of dynamic consent of the patients for data access and sharing of medical records. The paper describes the implementation of a secured and efficient system for the storage of medical records on the Blockchain. The proposed implementation focuses on scalability, the authenticity of medical records, data privacy, and controlled access and uses Proof-of-Authority as a consensus algorithm on a Permissioned-Blockchain and two-factor authentication through a smart card. The paper also introduces a constant-time searching algorithm for faster records retrieval of Electronic Health Records. While the Proof-of-Authority consensus mechanism resolves the scalability issues, the smart card acts as the access control token, that ensures data privacy and makes it convenient for the users to operate.
Digital to Analog Converters (DACs) have a good scope of application due to their easily implementable design structure, higher resolution, and accuracy. They are used in digital signal processing devices, digital power supplies for micro-controllers, digital potentiometers, and digital data acquisition systems. In this paper, we proposed high throughput circuit designs that perform multiple DAC operations in parallel with various resolution using the same hardware. The generated high throughput designs can be configured with the control line to perform four 2-bit, two 4-bit, and one 8-bit DAC operations. The throughput of the resistive ladder type DAC is increased by 95.70%, while that of weighted resistor type DAC is increased by 98% using the proposed hardware designs. All the proposed and existing designs are implemented with 90 nm CMOS library in Cadence Virtuoso.
Knee osteoarthritis is a condition in which the knee's articular cartilage, which is a slippery material that normally protects bones from joint friction, degenerates and changes to the underneath of the cartilage. If detected early the degeneration can be slowed down. The severity is relied on for detection on the expertise of Physicians. In this paper, to automatically measure OA severity we discuss the usage of deep CNN as a tool to successively develop a system, that is based on a grading system known as Kallgren-Lawrence (KL-grading). In this approach the OA severity is predicted using the radiographic Images. The method of automatic prediction of knee OA severity comprises three steps. a) Automatic localization of the knee joints. b) Classification of the localized knee joints and c) Create the report summary for identified symptoms The CNN is trained from scratch on the X-ray images. Along with the development of severity prediction through localization and classification, we will be developing the method to automatic report generation that consists of the description of the finding from the radiographs.
An unmanned aerial vehicle (UAV) assisted communication is a promising technique for assisting ground users in a non-functional area (NFA) or disaster area. All the base stations (BSs) in the disaster area are partially or fully damaged due to the natural calamity. Device-to-device (D2D) communication can be a good solution for direct connection between users in an NFA. In this paper, we propose a UAV-assisted multi-hop D2D communication for a hybrid power-time switching (PTS). Moreover, a D2D user of a cluster can communicate with another D2D user in a different cluster through UAVs. However, D2D users can harvest energy from their respective ad-hoc energy stations and forward the information to the nearby D2D user following a hybrid PTS-based strategy. We have proposed a time frame for the same and shown a node-based energy harvesting strategy. The expressions of outage probability, and throughput, are developed for the Rician random variable. The impact of parameters such as energy harvesting factor and energy harvesting efficiency on the network performance is also indicated. A simulation model is developed in MATLAB to assess the performance.
The identification of human emotions from facial expressions is intriguing and challenging research given the subtle differences between certain emotions. Face masks are nowadays strongly recommended to minimize infection transmission due to Covid-19. Successful emotion identification from masked faces is challenging since the lower part of the face contributes significant cues for emotion identification. In this work, we investigate transfer learning using deep pre-trained networks for emotion recognition from masked faces. Specifically, we fine-tune the pre-trained models: - EfficientNet-BO, ResNet-50, Inception-v3, Xception and AlexNet, on the benchmark Facial Expression Recognition (FER) 2013 dataset containing seven categories of emotions, namely, angry, disgust, fear, happy, sad, surprise and neutral. The experiments reveal that the Inception-v3 model outperformed all other deep learning models and the machine learning models Support Vector Machine (SVM) and Artificial Neural Network (ANN), for facial emotion recognition from masked faces.
This study investigates the performance of a relay-assisted cognitive radio (CR) enabled device-to-device (D2D) communication with Kernelized Energy Detection (KED). A D2D user uses KED technique for sensing the cellular user (CU) channel and uses the same while it is found to be idle. The D2D communication system uses multiple-input and multiple-output (MIMO), and non-orthogonal multiple access (NOMA) techniques to reduce the bad impact of fading and improve spectrum efficiency. The relay forwards the data received from a D2D source to a D2D destination and at each destination device, the received signals are combined using the maximal ratio combining (MRC) technique. The outage probability is studied as a performance metric. An analytical model of the outage probability for the considered network scenario is developed. A simulation framework has been developed and validated with the analytical framework.
Transmission control protocol (TCP) is a main transport layered communication protocol and works very well with wired networks where bit error rate is very low and the main cause of loss is congestion only. With the advancement of wireless communication technologies, the wireless networks are becoming integral part of our day to day life. Networks with wireless links meet various wireless losses due to bit error rate, host mobility, etc. But, any packet loss is interpreted as a congestion loss by TCP and inappropriate action is taken which degrades performance by reducing transmission rate. To differentiate the packet loss from congestion to non-congestion, this paper will focus on an algorithm which uses jitter ratio as a loss predictor. This algorithm will give better results than XJTCP(cross-layer jitter based transmission control protocol).
Recent advances in smartphone technologies have heavily influenced the battery drain of the system. Numerous technological and structural updations have improved the overall performance of the device along with the increase in the degree of parallelism and multitasking operations. Multiwindow is one such recently added feature among those many updates enabling multiple application being run and displayed simultaneously on the smartscreen. We present an energy profiling of popular Android applications and propose a dynamic energy saving technique for multiwindow operations with minimal impact on user experience. The profiling values reflect higher power expenses by multi-media based application combinations. Based on the user interaction with the application pair, we proposed a dynamic refresh rate partitioning technique based on critical and non-critical active applications.
In 2017, Mahmood et al have proposed an authentication scheme for providing comprehensive security requirements in communication between consumer and substations of smart grid environment to enable appropriate adjustment in electricity generation and consistent power supply in smart cities [1]. It is claimed that the scheme provides a secure remote user authentication and key agreement for the smart grids and is able to withstand all the known security attacks. In this paper we have analyzed the Mahmood et al scheme and it is found that the scheme is vulnerable to some security flaws such as user impersonation attack, known session specific temporary information attack, server masquerading attack, privileged insider attack, stolen smart card/device attack, clock synchronization problem and inability to protect user anonymity.
In cellular mobile propagation scenario, the presence of co-channel interference severely affects the throughput of the wireless system. Thus, it is essential to analyze the channel capacity of such link affected by the co-channel interference. It is also well known that the most appropriate model for capturing shadowing effect is lognormal (LN). In this paper, the closed-form expressions for the outage probability and the channel capacity with maximal ratio combining (MRC) diversity for different adaptive schemes are analyzed and investigated over the signal-to-interference (SIR) channel, where both the desired and the interferers channel are modeled as LN distribution. Firstly, the probability density function (PDF) utilizing the Fenton-Wilkinson method is derived, thereafter using these statistics, we have derived the closed-forms of the outage probability and the channel capacity under various adaptive schemes. Further, the lower bounds of the various transmission policies of the channel capacity are proposed. The analytical results are validated by comparing them with exact numerical results and Monte-Carlo simulations.
