Physical layer security (PLS) can be adopted for efficient key sharing in secured wireless systems. The random nature of the wireless channel and channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable high-rate secret key generation (SKG) process. The proposed system integrates physically unclonable functions (PUFs) and CR principle to securely exchange secret keys between two nodes. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. The proposed system can operate efficiently even when the channel is nearly flat or time invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. We also propose a novel bit extraction scheme that reduces the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs), which is substantially faster than other PLS techniques. Moreover, the results show that inducing AF into static channels reduces the mismatch ratio between the generated secret sequences and degrades the eavesdropper’s capability to predict the secret keys.

In this work, CANTERBURY QUESTION SET and  Yahya et al QUESTION SET were used for effective classifications.

This article introduces a method of evaluating subsamples until any prescribed level of classification accuracy is attained, thus obtaining arbitrary accuracy. A logarithmic reduction in error rate is obtained with a linear increase in sample count. The technique is applied to specific emitter identification on a published dataset of physically recorded over-the-air signals from 16 ostensibly identical high-performance radios. The technique uses a multi-channel deep learning convolutional neural network acting on the bispectra of I/Q signal subsamples each consisting of 56 parts per million (ppm) of the original signal duration. High levels of accuracy are obtained with minimal computation time: in this application, each addition of eight samples decreases error by one order of magnitude.

The electricity demand is increasing day by day and that is exhausting limited conventional energy resources rapidly. Thus, renewable energy is becoming very attractive source of electricity to meet the load and prevent the global warming threat. The challenges in integration of renewable energy sources are its intermittency and old existing power system infrastructure. Renewable energy sources collocated with energy storage facilities can mitigate the effect of renewable energy uncertainty. However, high penetration of green energy is only possible by upgrading the existing power system network. Most of the time transmission lines are under loaded from its actual power handling capacity, as worst possible power handling capacity of line is considered while scheduling power to generators. Actual power capacity of line depends on many environmental factors and changes accordingly with the weather conditions. This article elucidates the impact of weather variation on economic operation of a practical Indian 75 bus power system network placed at Uttar Pradesh, India. The simulation is carried out by accounting the proposed solar power plants in that region and the effect of dynamic line rating on renewable curtailment, thermal power fuel cost and line congestion has been studied.

Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their brain activity. In this study, we develop machine learning models to detect moments of distraction (due to mind wandering or drowsiness) during meditation practice using EEG signals. We use EEG data of 24 participants while performing a breath focus meditation with experience sampling and extract twelve linear and non-linear EEG features. Features are fed to ten supervised machine learning models to classify (i) Breath Focus Awake (BFA) vs Breath Focus Sleepy (BFS), and (ii) BFA vs Mind Wandering (MW). We observe that the linear features achieve a maximum accuracy of 86% for classifying awake (BFA) and sleepy (BFS), whereas non-linear features have more predictive ability for classifying between BFA and MW with a maximum accuracy of nearly 78% . In addition, visualization of unsupervised t-SNE lower embeddings supports the evidence of distinct clusters for each condition. Overall our results show that machine learning algorithms can successfully identify periods of distraction during meditation practice in novice meditators based on linear and non-linear features of the EEG signal. Consequently, our results have important implications for the development of mobile EEG neurofeedback protocols aimed at facilitating meditation practice.

With the development of wireless LANs, they are expected to have wide applications not only in traditional home but also in industrial applications. Therefore, the next generation of IEEE 802.11 wireless LAN standard demands higher throughput and lower latency. Full duplex (FD) technology, which allows a device to simultaneously transmit and receive signals on the same channel by cancelling self-interference, is one of the promising solutions to meet those requirements. Feasibility studies for applying FD technology to wireless LAN systems started in IEEE 802.11 standardization. In this article, we provide an overview of the FD mechanism and the technical problems for the realization. Furthermore, we provide the direction of the FD system for wireless LANs in IEEE 802.11 standardization. In addition, we discuss the expected low latency effect of the FD mechanism on the wireless LAN systems based on the direction in IEEE 802.11 standardization.

The data related to the classification of micro-Doppler result of human's and held object's motion

This work discusses the polarization of nonuniform plane waves (NPWs). A NPW can be created when a uniform plane wave is transmitted from a lossless medium into a lossy medium at an oblique angle. When a linearly polarized wave is transmitted into a lossy medium, the in-plane field transitions to elliptical polarization while the transverse field remains linearly polarized. This is a completely different behavior of a polarized wave compared to those that are created by introducing a phase difference between two perpendicularly polarized waves, where the polarization of both electric and magnetic fields changes together. The zero-divergence restriction determines the rotation direction of the NPW’s elliptical polarization.

This paper is about one of the functions of the data link layer, which controls access to shared media. The algorithm presented must be able to operate accurately and efficiently. In the approach focused on in this article, stations must specify the time of using services for access to the server. Then they will enter a system that arrives at the exit without any collisions; it will use the server based on the timing of the stations themselves. Many methods have been published on this, but in this paper .We tried a novel approach with its special features where the performance of this method is shown compared to other methods.

A document by Amjad khan . Click on the document to view its contents.

The South African rail industry is seeking a critical measure to monitor and save energy usage in the freight rail sector. The rail industry is experiencing increasing operational costs and high energy consuming driving records  along various coal lines. Eco-driving is a modern and efficient way of driving that emphasizes fuel efficiency, speed, and safety. This study provides an algorithm to find the optimal trajectory for a freight train hauling a load over a specific distance. Optimised speed profile is composed of optimal acceleration, coasting, and deceleration. The Freight Eco-Driving Energy Optimiser (FEDEO) solution is not yet applied to freight trains globally, especially in Africa. In this study, the eco-driving strategy of a freight train is formulated as an optimisation problem, whose objective function is the energy cost. The decision variables are the tractive and braking effort notches, and the speed, acceleration and distance limits are formulated as constraints. The formulated eco-driving problem is solved by Mixed-Integer Non-Linear Programming (MINLP) from the Opti-Solver toolbox. The FEDEO algorithm is applied to a train consisting of eight 19E locomotives with two-hundred CCR-9 wagons, over a distance of 90.64 km. The results show up to 34.76% reduction in energy costs.

A new approach is proposed to deal with the metastability errors in asynchronous SAR ADCs. This proposal avoids the disadvantages of conventional metastability detectors (additional metastability events due to the detector itself,  lowness from additional comparator loading, additional logic delays, need to perform detection on each bit), but provides a significant decrease in metastability errors and their amplitudes. The approach uses both comparators’ outputs to detect whether metastability occurred at all during conversion (without determining in which particular bit). Theoretical examination and Octave simulation show that this information suffices to correct half of metastability errors.

Mammography is the most widely used breast screening method for early detection and routine monitoring of breast cancer. Most mammogram classification or segmentation techniques are limited to simple binary classifications of the abnormalities in mammograms or classifying specific cropped regions. However, these methods encode merely the texture features of the X-ray-filmed lesions, despite the relationships and patterns. Thus, most of these models have inefficient learning capacities, hindered by supervised learning processes that require extensive annotated data, which is not feasible in the medical domain. To overcome these challenges, we model the deep texture and spatial features of every small segment of the mammogram in a complex multi-graph network. Moreover, we proposed a semi-supervised mammogram multi-graph attention network (MMGAT) to classify each element into all probabilistic categories. The attention and the dense relationships in the multi-graph network empower the proposed model with significant learning capacities, resulting in a competitive performance outperforming the recent state-of-the-art mammogram classification models. The proposed model can localise every abnormal segment of the mammogram and classify each as a malignant or benign region of a mass or calcification cancerous. The proposed MMGAT requires only 40\% of the data for training to achieve the highest AUC of 0.97 and an F1 score of 0.95 on the publically available DDSM.

The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements, thus enabling massive Machine Type Communications. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZED-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZED-based networks.

This work provided a study of multiple polarization for use in a MIMO system. Currently, only 2 polarization MIMO system model is in use. However, there are multiple antennas in Tx and Rx sides, therefore, increasing the number of polarization equivalent to the number of antennas can improve MIMO channel capacity. We experimented with different combinations for polarization for 4T4R MIMO system. The 4 suitable polarization obtained is further studied for higher capacity with Transmitter Beam Angular Diversity. Tranmistter Beam Angular Diversity reduce the correlation among non orthogonal polarization channel and improve MIMO system capacity.

A document by Tushar Bose . Click on the document to view its contents.

Under high-dimensional and nonlinear stochastic power system environment, artificial intelligence (AI) is becoming a promising alternative towards the urgent demand of real-time AC optimal power flow (OPF). However, traditional AI only fit the settlement results of other method, and is unable to find in-depth law of power flow. This may result in an undesired poor generalizability. To address this issue, a unsupervised OPF model combining physics- guided graph neutral networks and Lagrangian dual is proposed. The model incorporates the physical constraints into training tensor graph, as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. At the same time, since topology of power system may vary, the branch characteristics are embeded into node graph to adapt to grid topology contingency. Numerical tests on benchmarks demonstrate that, in unseen operating condition, the proposed method enables a near or even better solution than conventional optimization algorithm, but consumes much more than 100 times calculation efficiency.

5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.