UAVs can be used as aerial relays to provide communication services in remote uncovered areas or dense environments with occasional high capacity demands. However, due to the low power of Internet-of-Things (IoT) devices, UAV-based IoT applications, such as precision agriculture and environment monitoring, may experience high shadowing or equipment failure, which degrades the communications’ quality between IoT devices and their gateways. To tackle this issue, we consider the long range (LoRa) communication technology. Specifically, we investigate the performance of LoRA-enabled aerial communications, where a LoRa gateway communicates with a distant IoT device through the assistance of an amplify-and-forward aerial relay. Under the assumption of shadowed Rician fading channels, we characterize at first the end-to-end LoRa communication link. Then, we derive an exact symbol error rate expression for the underlying system model. Finally, numerical results are presented to corroborate the efficacy of our derived expressions and provide valuable insights into the error performance of LoRa-enabled aerial networks.

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to tackle the problem of human activity discovery fully unsupervised and map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm does not require any prior knowledge of the number of activities to be discovered and can find the optimal number. Multi-objective optimization problems typically cannot have a single optimal solution. To solve this issue, Nash Equilibrium (NE) is applied to the pareto front to choose the best solution. NE does not just look for the best solution, but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. Experiments on five challenging datasets demonstrate that the proposed approach is the most efficient to achieve better accuracy in human activity discovery and also can determine the optimal number of clusters.

The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging technology enabling the development of smart city applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application’s Quality of Service (QoS) in terms of execution time, and respecting a single SLA constraint. They do not consider the impact of overlapped multi-requests processing nor the vehicle’s varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application’s execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to random offloading, and baseline genetic-based approaches. Results show QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the random offloading approach and with 59.9% fewer SLA violations. In contrast, the baseline genetic-based approach increases the requests’ performance by 1.14 times, with 19.8% more SLA violations.

In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of “flash lidar”. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

Rest tremor is a most common symptom of Parkinson’s Disease (PD), with diagnosis and severity estimation often being hindered by subjectivity and limitations of existing methods. Additionally, treatment strategies for PD face challenges in monitoring symptom fluctuations with clinical methods, like the MDS-UPDRS and motor diaries, suffering from subjectivity, limited sensitivity, and infrequent sampling, potentially impacting treatment effectiveness. Hence, methods that can yield explainable biomarkers that accurately describe properties of PD rest tremor, while accounting for the presence of ongoing treatments, such as Deep Brain Stimulation (DBS) and medication, are important for integration in accurate AI-powered wearable systems. To that end, a Higher Order Spectrum (HOS)-based analysis to extract features from index finger velocity recordings of 16 PD patients is proposed. Two different scenarios are implemented for characterizing and classifying treatment (medication/DBS) effectiveness and tremor severity (Low-/High- Amplitude (LAT/HAT)), by means of statistical tests and a leave-one-subject-out cross-validation classification scheme, respectively. The proposed analysis resulted in area under the Receiver Operating Characteristics curve (AUC) score of 0.94 for the Medication treatment classification, 0.71 for the DBS treatment and 0.83 for LAT/HAT prediction. Our results demonstrate that the proposed approach can effectively assess the influence of medication and DBS and recognize rest tremor severity. Finally, our HOS-based methodology enables the establishment of new rest tremor classes, based on its nonlinearity and allows for new insights about the dynamic nature of the resting tremor production system.

In many sea-exploration scenarios, a wireless optical communication link between an onshore station and a floating marine structure or between a floating buoy and an underwater vehicle is required. In order to establish high-quality wireless optical communication, the effect of sea-waves must be taken into consideration. In this work, we investigate the influence of surface water waves having various amplitudes, frequencies and waveforms on the quality of an analog speech signal transmitted over a wireless optical communication link. Such investigation is also carried out on an encrypted speech signal. The quality of the speech signals is determined by utilizing a deep learning neural network that is trained to recognize ten speech commands and produces classification probabilities. In our experiments, either the detector or the light source is non-stationary as a result of the water-waves, thus distorting the received analog signal. We also show a method of deriving the frequency of the water waves by using a remote tracking unit based on micro-electro-mechanical systems (MEMS) mirrors.

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMGsignals as part of the control strategy have increased in the last decade. In this review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched from July-September 2020: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,327 papers were initially assessed and 117 were finally included in this review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated large variations were found across studies between the different control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

Includes synthetic data processing and real data (IRIS community Wavefield experiment) processing examples.

This article focuses on insider-threat detection within healthcare infrastructures. There are inherent dangers to digitising patient records and considering the sensitive nature of the data, EHR is equally at risk of both external threats and insider attacks, but security applications are predominantly facing the outer boundary of the network. In this work, the focus is on insider data misuse detection. The approach involves the use of supervised classification (decision tree, random forest and support vector machine) based off pre-labelled real world data collated from a UK-based hospital for the detection of EHR data misuse. The results demonstrate that by employing a machine learning approach to analyse EHR data access, anomaly detection can be achieved with a 0.9896 accuracy from a test set and 0.9908 from the validation set using a support vector machine classifier. The emphasis of this research is on the detection of EHR data misuse, through the detection of anomalous behavioural patterns. Based on the results, the recommendation is to adopt an SVM for data misuse/insider threat detection

This article has been submitted to the IEEE Transactions on Microwave Theory and Techniques and is currently under review. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field three-dimensional imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. Their accuracy, however, quickly degrades when applied to close-range imaging, where amplitude variations are significant and where the range distance to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses a measured point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann-Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography.

Grid-forming inverters are sensitive to large grid disturbances that may engender overcurrent due to their voltage source behavior. To overcome this critical issue and ensure the safety of the system, current limitation techniques have to be implemented. In this context, the variable virtual impedance (VI) appears as a suitable solution for this problem. The design of the variable virtual impedance basically rests on static considerations, while, its impact on the system stability and dynamics considering both small-signal and large-signal aspects can be significant. This paper proposes small-signal and nonlinear power models to assess the impact of the virtual impedance parameters on the grid current dynamics and on the angle stability. Thanks to the developed theoretical approaches in this paper, the virtual impedance ratio X/R, which is the unique degree of freedom has shown a contradictory effect on the small and large signal stability. To overcome this constraint, a Variable Transient Virtual Resistance (VRTVR) has been proposed as an additional degree of freedom to the variable virtual impedance. It acts as a transient damper to ensure the maximum angle stability margin allowed by the variable virtual impedance. The effectiveness of the proposed control has been proven through time-domain simulations.

We propose an Ekblom promoting adaptive algorithm (EPAA) which uses Ekblom norm to construct a data reusing scheme to achieve better performance under impulsive noise (IN) environments. By exerting Ekblom-norm constraint on the a posterior error vector, a sliding window cost function is created to realize the EPAA that is derived to reduce IN effects. The derivation and theoretical convergence analysis of the EPAA are presented in detail. The simulation examples are setup to illustrate the superior performance of the EPAA compared to popular data reusing algorithms for system identification under IN environments.

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants (known as Byzantine clients) may upload arbitrary local updates to the central server in order to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schemes. However, there is a lack of systematical comparison and empirical study thereof. In this paper, we conduct an experimental study of Byzantine-robust aggregation schemes under different attacks using two popular algorithms in federated learning, FedSGD and FedAvg. We first survey existing Byzantine attack strategies, as well as Byzantine-robust aggregation schemes that aim to defend against the Byzantine attacks. We also propose a new scheme, ClippedClustering, to enhance the robustness of a clustering-based scheme by automatically clipping the updates. Then we provide an experimental evaluation of eight aggregation schemes in the scenario of five different Byzantine attacks. Our experimental results show that these aggregation schemes sustain relatively high accuracy in some cases, but they are not effective in all cases. In particular, our proposed ClippedClustering successfully defends against most attacks under independent and identically distributed (IID) local datasets. However, when the local datasets are Non-IID, the performance of all the aggregation schemes significantly decreases. With Non-IID data, some of these aggregation schemes fail even in the complete absence of Byzantine clients. Based on our experimental study, we conclude that the robustness of all the aggregation schemes is limited, highlighting the need for new defense strategies, in particular for Non-IID datasets.

In this paper, a new scheme is proposed to estimate the directions-of-arrival (DOAs) of multiple sources using a coprime electromagnetic vector sensor (EMVS) array. Each EMVS consists of three mutually orthogonal electric dipoles and three mutually orthogonal magnetic loops, all collocated at a single point in space. By exploiting the polarization diversities embedded in the vector sensors, the COarray Polarization Smoothing (COPS) is presented to increase the degrees of freedom (DOFs) from the polarization coarray domain. In contrast to the widely used spatial smoothing technique, the COPS offers the following new insights: 1) it is source polarization-dependent and is proved to offer $20$ DOFs at most, 2) it does not reduce the efficient spatial coarray aperture of the coprime array so that it enables the use of all virtual coarray sensors without resorting to the nontrivial sparse recovery or array interpolation operations, and 3) it can be synthesized with the spatial smoothing to get the DOFs multiplied. Finally, the efficacy of the COPS scheme is verified by numerical examples.

The sound emission characteristics of unmanned aerial systems are of interest in many contexts. For a realistic representation of the directional characteristics and the radiated sound energy, it is useful if the aircraft is operated under realistic conditions. However, deriving emission-based acoustic quantities for a typical mode of operation such as cruise flight is difficult. In this paper, the directivity and sound power of a quadcopter drone are determined using a microphone array measurement of a single flight through a predefined corridor. The recorded data are processed to both reconstruct the drone’s flight path and characterize its acoustic emission. To verify the reliability of the presented method, the signal processing is tested with simulated data from moving sources with the directivity of a monopole and a dipole, respectively. Using different discretizations of the radiation direction space and with the evaluation of the frequency-dependent directivity factor, it is discussed how the sound radiation can be described as comprehensively as possible on the basis of as few quantities as possible.

Benefiting from the development of image sensors and the popularity of light-emitting diode (LED) lighting technology, visible light positioning (VLP) technology based on image sensors has ushered in vigorous development and broad prospects, which can provide low-cost and high-accuracy position service. However, the existing approaches require dense LEDs or sensors such as gyroscopes to assist positioning, which limit the area and lower the accuracy of positioning because of the errors from imperfect sensors. In this paper, we propose a simultaneous localization and calibration VLP method based on double coplanar circular LED lights aiming to get rid of the dependence on additional sensors and dense LED transmitters. By the pinhole camera model and the perspective projection of circle, our proposed method extends the available position area and relaxes the required quantity of LED to two. The experiment result shows that our system has a mean 3D positioning accuracy of 7.91cm, a mean angle error of less than 1.6°, and an average latency of 182ms on mobile devices.

In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Fully differential topologies are well-suited for biopotential amplifiers, mainly for single-supply battery-powered circuits such as portable wearable devices where a reduced number of parts is desired. A novel fully differential biopotential amplifier is proposed with the goal of providing electrode offset rejection, bandwidth limitation, and a temporal response compliant with biomedical standards with only a single commercial quad operational amplifier (OA) integrated circuit.