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This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset. [ABSTRACT FROM AUTHOR]

The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead. [ABSTRACT FROM AUTHOR]

One's working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction. [ABSTRACT FROM AUTHOR]

Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field. [ABSTRACT FROM AUTHOR]

Practical tools which can be quickly administered are needed for measuring subtle changes in cognitive–motor performance over time. Frailty together with cognitive impairment, or ‘cognitive frailty’, are shown to be strong and independent predictors of cognitive decline over time. We have developed an interactive instrumented trail-making task (iTMT) platform, which allows quantification of motor planning error (MPE) through a series of ankle reaching tasks. In this study, we examined the accuracy of MPE in identifying cognitive frailty in older adults. Thirty-two older adults (age = 77.3 ± 9.1 years, body-mass-index = 25.3 ± 4.7 kg/m2, female = 38%) were recruited. Using either the Mini-Mental State Examination or Montreal Cognitive Assessment (MoCA), 16 subjects were classified as cognitive-intact and 16 were classified as cognitive-impaired. In addition, 12 young-healthy subjects (age = 26.0 ± 5.2 years, body-mass-index = 25.3 ± 3.9 kg/m2, female = 33%) were recruited to establish a healthy benchmark. Subjects completed the iTMT, using an ankle-worn sensor, which transforms ankle motion into navigation of a computer cursor. The iTMT task included reaching five indexed target circles (including numbers 1-to-3 and letters A&B placed in random order) on the computer-screen by moving the ankle-joint while standing. The ankle-sensor quantifies MPE through analysis of the pattern of ankle velocity. MPE was defined as percentage of time deviation between subject’s maximum ankle velocity and the optimal maximum ankle velocity, which is halfway through the reaching pathway. Data from gait tests, including single task and dual task walking, were also collected to determine cognitive–motor performance. The average MPE in young-healthy, elderly cognitive-intact, and elderly cognitive-impaired groups was 11.1 ± 5.7%, 20.3 ± 9.6%, and 34.1 ± 4.2% (p < 0.001), respectively. Large effect sizes (Cohen’s d = 1.17–4.56) were observed for discriminating between groups using MPE. Significant correlations were observed between the MPE and MoCA score (r = −0.670, p < 0.001) as well as between the MPE and dual task stride velocity (r = −0.584, p < 0.001). This study demonstrated feasibility and efficacy of estimating MPE from a practical wearable platform with promising results in identifying cognitive–motor impairment and potential application in assessing cognitive frailty. The proposed platform could be also used as an alternative to dual task walking test, where gait assessment may not be practical. Future studies need to confirm these observations in larger samples. [ABSTRACT FROM AUTHOR]

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao-Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics. [ABSTRACT FROM AUTHOR]

This paper reports on the design and evaluation of direct 3D gesture interaction with a full horizontal parallax light field display. A light field display defines a visual scene using directional light beams emitted from multiple light sources as if they are emitted from scene points. Each scene point is rendered individually resulting in more realistic and accurate 3D visualization compared to other 3D displaying technologies. We propose an interaction setup combining the visualization of objects within the Field Of View (FOV) of a light field display and their selection through freehand gesture tracked by the Leap Motion Controller. The accuracy and usefulness of the proposed interaction setup was also evaluated in a user study with test subjects. The results of the study revealed high user preference for free hand interaction with light field display as well as relatively low cognitive demand of this technique. Further, our results also revealed some limitations and adjustments of the proposed setup to be addressed in future work. [ABSTRACT FROM AUTHOR]

The relentlessly increasing number of small-sized devices with limited powering and computational capabilities requires the adoption of new approaches to spectrum access. In this paper, we analyze an underlay cooperative cognitive wireless system based on available statistical channel state information (CSI) that is applicable to the cognitive system with limited computational resources due to its low complexity. We considered the scenario where the primary and the cognitive network coexist in the same spectrum band, under the constraints of interference threshold and maximal tolerable outage permitted by the primary user. The communication in the secondary decode-and-forward (DF) relaying system is established via a self-sustainable relay, which harvests energy from both cognitive and primary transmitters. The closed-form expressions for the outage probability of the cognitive network are derived, which are valid for both time-switching relaying (TSR) and power-splitting relaying (PSR) protocols. We analyze the influence of both cognitive and primary systems as well as the impact of channel parameters on the cognitive system outage performance. The derived analytical results are corroborated by an independent simulation method. [ABSTRACT FROM AUTHOR]

The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators' cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators' MWL, whilst also allowing for increased integrity and reliability of the system. [ABSTRACT FROM AUTHOR]

This study presents ambient backscatter communication (AmBC) network as a concept of "modulation in the air" that has drawn growing interest by both academia and industry recently. In particular, we investigate and analyze an AmBC system relying on cognitive radio, where the primary destination is equipped with multiple antennas and maximum ratio combining (MRC). A wireless powered relay is necessary to serve both primary and secondary destinations. Benefiting from the surrounding radio frequency (RF) source, the relay can support the backscattering signal. To facilitate the performance analysis of received nodes, this study presents exact closed-form expressions of the outage probability. For comparison, the outage and throughput performance of these nodes are considered in numerical simulation. Taking advantage of the AmBC technology, the impact of the backscatter ratio on system performance is carefully studied considering various other parameters. Simulation results demonstrate the exactness of the derived outage probabilities and show that the optimal throughput performance can be achieved at specific parameters. [ABSTRACT FROM AUTHOR]

The integration of unmanned aerial vehicles (UAVs) with a cognitive radio (CR) technology can improve the spectrum utilization. However, UAV network services demand reliable and secure communications, along with energy efficiency to prolong battery life. We consider an energy harvesting UAV (e.g., surveillance drone) flying periodically in a circular track around a ground-mounted primary transmitter. The UAV, with limited-energy budget, harvests radio frequency energy and uses the primary spectrum band opportunistically. To obtain intuitive insight into the performance of energy-harvesting, and reliable and secure communications, the closed-form expressions of the residual energy, connection outage probability, and secrecy outage probability, respectively, are analytically derived. We construct the optimization problems of residual energy with reliable and secure communications, under scenarios without and with an eavesdropper, respectively, and the analytical solutions are obtained with the approximation of perfect sensing. The numerical simulations verify the analytical results and identify the requirements of length of sensing phase and transmit power for the maximum residual energy in both reliable and secure communication scenarios. Additionally, it is shown that the residual energy in secure communication is lower than that in reliable communication. [ABSTRACT FROM AUTHOR]

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback–Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels. [ABSTRACT FROM AUTHOR]

The improvement of stable, energy-efficient mobile-based clustering and routing protocols in wireless sensor networks (WSNs) has become indispensable so as to develop large-scale, versitale, and adaptive applications. Data is gathered more efficiently and the total path length is shortened optimally by means of mobile sink (MS). Two algorithms as bacterial interaction based cluster head (CH) selection and energy and transmission boundary range cognitive routing algorithm with novel approach for heterogeneous mobile networks are proposed in this study. The more reliable and powerful CH selection is made with the greedy approach that is based on the interaction fitness value, energy node degree, and distance to adjacent nodes in a compromised manner. The best trajectories, thanks to intersection edge points of the visited CHs, are obtained in the proposed routing algorithm. In this way, the MS entry to transmission range boundaries of the CH has been a sufficient strategy to collect information. As in energy model, we adopt energy consumption costs of listening and sensing channel as well as transmit and receive costs. Comprehensive performance analyzes have been seriously carried out via the Matlab 2016a environment. We validate that the proposed scheme outperforms existing studies in terms of several performance metrics as simulations. [ABSTRACT FROM AUTHOR]