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Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.

We propose a novel data-driven feature extraction approach based on direct causality and fuzzy temporal windows (FTWs) to improve the precision of human activity recognition and mitigate the problems of easily-confused activities and unlabeled data, which significantly degrade classification performance owing to the correlation of labeled data. In recognizing activities, the proposed approach not only considers the importance of oncoming short-term sensor data but also considers the continuity from past activities of the preceding long-term sensor data. In terms of the oncoming data, the causality feature is extracted using the direct transfer entropy to determine the unique pattern of an activity, which represents the quantified causal relationship between sensor activations. In terms of the preceding data, several hours of historical data are compressed to fuzzy features based on FTWs. Subsequently, the causality and fuzzy features are fused by matrix multiplication to express distinct features of activities. To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used. Leave-one-day-out cross-validation was performed based on the CASAS open datasets, including Aruba, Cairo, and Milan. The results showed that the macro-F1-scores were improved by 16.4, 37.5, and 18.5%, respectively, compared with those of the FTW-only environments. In addition, the proposed approach could improve the precision of activity recognition and mitigate the problems associated with the environments containing unlabeled data.

Demand response (DR) is a voluntary program that encourages related stakeholders, in this case electricity consumers, to cut down on usage during periods of high electricity load. One key to fully exploiting DR is to encourage residential customers to join the DR program. Unlike in the DR programs for commercial and industrial customers, for the successful operation of the residential DR program, several issues have to be addressed, one of which is to provide a group-level incentive to participating customers. In particular, the issue comes up when the incentive calculated for a group is not equal to the aggregated incentives for each customer (i.e., non-equal incentive problem). The non-equal incentive problem deteriorates the successful operation of residential DR by decreasing the motivation of DR operators and customers. We first prove the non-equal incentive problem through mathematical and experimental methods. We then propose the novel single group-based indirect incentive calculation method. The basic idea of our approach is to indirectly calculate the incentive for each customer not using the customer’s data but using the data of other customers of the same DR group. Through experiments involving the electricity usage data of 42,193 households and the real DR events in Korea, we show that our method solves the non-equal incentive problem in most cases. Furthermore, our method improves the accuracy of the baseline estimation (used for calculating the contribution).

In this paper, a hybrid satellite-terrestrial spectrum sharing system allows terrestrial secondary network to cooperate with a primary satellite network and to further provide higher spectrum efficiency. For massive connections design, we implement non-orthogonal multiple access (NOMA) technique to form cognitive radio based satellite-terrestrial (CR-NSHT) system relying on NOMA and further achieve more benefits compared with traditional schemes. The secondary network only remains its stable operation when the outage probability of such system is guaranteed, and thereby, to explore advantages of spectrum sharing opportunities. Considering Shadowed-Rician fading for satellite links, and Nakagami- $m$ as well as Rician fading for terrestrial links, we derive the closed-form expressions of the outage probability to evaluate the performance of secondary network. We consider further system performance metrics including ergodic capacity, energy efficiency and multi-user scenarios. We also demonstrate the impacts of power allocation factors, transmit signal-to-noise ratio (SNR) at the source, parameters of satellite links, target rates, and fading severity parameters on the system performance. Numerical and simulation results validate our analysis and highlight the performance gains of the proposed schemes for CR-NSHT with relay link serving secondary network and direct link serving the primary network.

In realizing unsupervised pixel-precise anomaly localization by utilizing a generative model, a reference image must be generated (for comparison with an input image) by transforming abnormal patterns of an input image, if any, into normal patterns. In this study, a patch-level operation with adaptive patch control is proposed to improve anomaly localization by generating a better reference image. As a way to exploit a generative model, we divide an image into non-overlapped patches of the same size, generate patch-level reference images, and stitch the patch-level reference images into a single reference image. We then conduct anomaly localization by comparing an input image with the stitched, reconstructed image. To effectively apply the patch-level operation, we propose adaptive patch control to determine the number of non-overlapped patches to be applied. For this, we synthesize defective images using normal images and examine how well the candidate methods with different numbers of patches remove the synthesized defects. In the same way, we utilize adaptive patch control to select a promising model among the candidate generative models. Based on experiments conducted using the MVTec Anomaly Detection dataset, we demonstrate that our method outperforms previous existing methods. Under a real-world scenario, our method shows ROC AUC of 0.926, in contrast to the best value of 0.893 from existing studies. Furthermore, we prove the feasibility of the adaptive patch control by showing that the removal of the synthesized defects and the anomaly localization for real defective images are highly correlated.

High-resolution (HR) 360° images offer great advantages wherever an omnidirectional view is necessary such as in autonomous robot systems and virtual reality (VR) applications. One or more 360° images in adjacent views can be utilized to significantly improve the resolution of a target 360° image. In this paper, we propose an efficient reference-based 360° image super-resolution (RefSR) technique to exploit a wide field of view (FoV) among adjacent 360° cameras. Effective exploitation of spatial correlation is critical to achieving high quality even though the distortion inherent in the equi-rectangular projection (ERP) is a nontrivial problem. Accordingly, we develop a long-range 360 disparity estimator (DE360) to overcome a large and distorted disparity, particularly near the poles. Latitude-aware convolution (LatConv) is designed to generate more robust features to circumvent the distortion and keep the image quality. We also develop synthetic 360° image datasets and introduce a synthetic-to-real learning scheme that transfers knowledge learned from synthetic 360° images to a deep neural network conducting super-resolution (SR) of camera-captured images. The proposed network can learn useful features in the ERP-domain using a sufficient number of synthetic samples. The network is then adapted to camera-captured images through the transfer layer with a limited number of real-world datasets.

To improve the utilization of spectrum and system capacity, non-orthogonal multiple access (NOMA) is considered as a promising multiple access method for emerging communication technologies. Its advantageous benefits are implemented in two-way wireless networks. Power allocation factors assigned to multiple users play a key role in the successful deployment of fairness balance in NOMA. This paper considers fixed power allocation, and performance degradation is predicted in worse situation in which non-optimal power scheme and the imperfect channel state information (CSI) happen. More challenging issue happens in this system, and this paper addresses the situation of NOMA supporting multi-pair of users, in which group of users can communicate to other users to achieve acceptable throughout in two kinds of mode, i.e. delay-limited mode and delay-tolerant mode. Two performance metrics have been considered, namely, ergodic rate and outage probability. For both metrics, we derived closed-form analytical expressions as well as asymptotic performance. Numerical results show that the proposed system presents benefits to cellular networks by extending ability to serve more users who have different demands on data communication. Monte Carlo simulations are provided throughout to validate the accuracy of the derived analytical expressions.

In conventional cognitive radio (CR), the users in secondary network (SN) can only access the idle spectrum when users in primary network (PN) are absent. This novel strategy provides higher spectrum efficiency when detecting the presence of the PN. Hence, spectrum utilization of the traditional scheme can be further improved as exploiting application of non-orthogonal multiple access (NOMA). As combination of CR and NOMA, such CR-NOMA has been proposed to improve spectrum efficiency to adapt to requirements in 5G communications. In this study, the relaying scheme is employed in the SN of the proposed CR-NOMA and the relay is allowed to energy harvesting (EH) from the secondary transmitter to serve signal forwarding to distant secondary users. With this regard, the complex model of EH-assisted CR-NOMA is explored in outage behavior and throughput performance as awareness on imperfect successive interference cancellation (SIC) at the receiver. As most important results, the exact closed-form of the exact outage probability is derived for each NOMA destination by assuming that the channel coefficients among considered links follow Rayleigh distribution. Furthermore, performance gap between two NOMA users can be controlled by various parameters such as transmit power, energy harvesting coefficients and levels of imperfect SIC. Simulation results verify our analytical results.

In this article, a conceptual approach is presented for pre-fault detection regarding the waveform analytic aspects of distribution monitoring and measuring devices. Also included are event patterns specifically arranged by feature and classification methods. A waveform class patterning algorithm on time-series is applied experimentally to field waveforms that were obtained for several years. The waveforms are processed with consideration of waveform classification and event sequence processing because these detect fault-related phenomena. This approach demonstrates that conspicuous patterns in fault-related sequences can be discovered by the data-driven structure described in the paper by designing the event structure depending on the network configuration. The application is applied in a pattern-learning and pattern-detection process so that integrating both approaches provides a meaningful consolidation for detecting abnormal conditions on distribution lines. This event-based fault prevention is employed using actual acquisition data from a domestic-scale distribution system and a unique sequence model is constructed to determine normal and abnormal conditions. Event index manipulation analysis on different risk levels defines the pattern and its impact on monitoring results. The proposed model guides recognition of event patterns and waveforms that can be pre-emptively detected in advance of distribution line failures.

The future of wireless communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) wireless networks. Since the traditional wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces (RIS) in wireless communications enables, on the other hand, network operators to control the radio waves (the scattering, reflection, and refraction characteristics) to eliminate the negative effects of natural wireless propagation. Recent results have revealed that non-orthogonal multiple access (NOMA) benefits from RIS mechanism which can effectively provide effective transmissions. Motivated by the potential of these emerging technologies, we study the impact of hardware impairment in RIS-aided NOMA system in term of performance metrics. We then derive analytical expressions of outage probability and throughput as main performance metrics. Simulations are conducted to validate the analytical expressions. We find that the number of meta-surfaces in RIS, transmit power at the base station, power allocation factors play important role to demonstrate improvement in system performance of RIS relying on NOMA compared with orthogonal multiple access (OMA). Numerical results are presented to validate the effectiveness of the proposed RIS-aided NOMA transmission strategies.

This study presents an automatic sleep-stage classification system based on utilizing compressive sensing (CS) for data reduction. The amount of electroencephalogram (EEG) signal data required for sleep-stage classification can be significantly reduced by applying CS at the cost of distortion in the reconstructed EEG signal. A neural network trained on features extracted from the reconstructed EEG signal was implemented as a classifier to avoid compromising the accuracy of classification in the presence of distortion in the reconstructed EEG signal. A radial basis function (RBF) neural network that uses a simple Manhattan distance function instead of complicated computation, such as vector multiplication matrix (VMM), was selected to reduce the hardware complexity. A classification method that utilizes information from the previous sleep stage based on the unique nature of human sleep was also presented and implemented on the proposed RBF classifier for further improvement of the classification accuracy. The classification system has been evaluated using EEG data from eight subjects in the Cyclic Alternating Pattern (CAP) sleep dataset. The measurement results showed that the classification system achieved high classification accuracy comparable to the previously reported sleep-stage classifiers that do not utilize signal compression.