This paper presents an overview of the M-ary Aggregate Spread Pulse Modulation (M-ASPM), and provides an assessment of its suitability and advantages for use in low-power wide-area networks (LPWANs). M-ASPM combines high energy-per-bit efficiency, robustness, resistance to interference, and a number of other favorable technical characteristics, with the spread-spectrum ability to maintain the network capacity while extending its range. Throughout the paper, LoRa is used for benchmark comparison and quantification of various M-ASPM features. In particular, we demonstrate that, while sharing many essential properties with LoRa, M-ASPM provides far more effective network range extension. When used in the same manner as LoRa, M-ASPM can serve as an appealing LoRa alternative. In addition, while being different LPWAN solutions, M-ASPM and LoRa can be designed to concurrently operate in the same spectral band and geographical area, cooperatively complementing each other’s coverage.

With the increasing availability of more advanced prostheses individuals with a transradial amputation can now be fit with single to multi-degree of freedom hands. Reliable and accurate control of these multi-grip hands still remains challenging. This is the first multi-user study to investigate at-home control and use of a multi-grip hand prosthesis under pattern recognition and direct control. Individuals with a transradial amputation were fitted with and trained to use an OSSUR i-Limb Ultra Revolution with Coapt COMPLETE CONTROL system. They participated in two 8-week home trials using the hand under myoelectric direct and pattern recognition control in a randomized order. While at home, participants demonstrated broader usage of grips in pattern recognition compared to direct control. After the home trial, they showed significant improvements in the Assessment of Capacity for Myoelectric Control (ACMC) outcome measure while using pattern recognition control compared to direct control; other outcome measures showed no differences between control styles. Additionally, this study provided a unique opportunity to evaluate EMG signals during home use. Offline analysis of calibration data showed that users were 81.5% [7.1] accurate across a range of three to five grips. Although EMG signal noise was identified during some calibrations, overall EMG quality was sufficient to provide users with control performance at or better than direct control.

Tight integration of Global Navigation Satellite System (GNSS) and Ultra-Wide Band (UWB) is growing popularity as a positioning strategy relying on mass-market devices for applications dealing with localisation and tracking of agents in challenging environments. However, due to independent clocks of Commercial off-the-shelf (COTS) devices, the offset between the time-scales with respect to which measurements are time tagged must be mitigated to achieve accurate state-estimation via centralised, recursive filtering architectures. In this paper, it is analysed a GNSS/UWB Tightly-Coupled (TC) scheme based on an Extended Kalman Filter (EKF), and the integration of asynchronous GNSS/UWB measurements is investigated to highlight how it can induce state-estimation errors under different receiver kinematics. GNSS/UWB time calibration is addressed as a filtering problem, for which an EKF-based framework is developed to recursively model the GNSS/UWB time-offset as a further unknown in the system state-space model. Moreover, a double-update filtering model is proposed with embedded optimisations for the adaptive tuning of UWB ranging statistics. Experimental results with simulated data show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain 41.60 % over a plain EKF integration with uncalibrated time-offset and of 15.43 % over the EKF with naive time-offset calibration.

In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the $4 \times 1$ vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to $13$ sources, while for the array located in free space, the PVCA can identify $9$ sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the efficacy of the PVCA is verified by numerical simulations.

Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six input signals. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04±0.02 Nm/kg, corresponding to 2.9±1.6% of the ankle torque’s dynamic range. Comparatively, a manually-derived analytical regression model predicted ankle torques with a RMSE of 0.35±0.53 Nm/kg, corresponding to 26.6±40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average drop in performance of 1.7% of the ankle torque’s dynamic range. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.

we propose a novel QRS detection algorithm based on a new Permutation Entropy (PE) method that we developed and referred to as the Adaptive Improved Permutation Entropy (AIPE) method. The parameters of the AIPE method are determined based on the specific signal properties. Implementing the AIPE method leads to prominently preserving the QRS complex and eliminating noises of the ECG signal without smoothing the ECG signal.

Process optimization and control can become challenging when the measurements are affected by irregular noise. Classical approaches utilize Gaussian method like Kalman filtering to alleviate the sensory noise. However, many industries involve skewed noise in their processes. While the closed skew normal (CSN) distribution generalizes a Gaussian distribution with additional parameters, its dimension increases during recursive estimation, making it impractical. Even though there exist some techniques for the solution, they are typically either too complicated or inaccurate for the higher dimensional problems. This study proposes a novel online optimization scheme to reduce the dimensionality of a CSN distribution while considering the properties of the complete empirical distribution. Since the objective function used during the optimization step considers the geometry of the metric space, the proposed scheme achieves the higher accuracy without sacrificing computational efficiency. After finding the reliable combination of objective function and optimizer, the proposed filter is applied to two real-time pilot-scale experiments. The results indicate that it is beneficial for recursive state estimation in the presence of skewed noise.

The combination of multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system with the aim of maximizing the system sum-rate for every channel realization. The first one is a novel contextual bandit (CB) approach with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is properly met. Both proposals perform remarkably better than state-of-the-art heuristic methods in high multi-user interference scenarios.

Temporary overvoltage (TOV) is becoming one challenge for the integration of significant power electronics resources into a power system. To assess the risk of TOV in a Multi-Infeed Power Electronic System (MIPES), a worst-case estimation approach is proposed to assess TOV risk. The paper starts with analyzing the mechanism of TOV, which reveals that TOV is dominated by excessive reactive power resulting from control lagging of converters. Then, the principle of approximating the TOV for a Voltage Source Converter (VSC) connecting to an infinite bus is introduced within the context of the RMS model. After that, a methodology based on the concept of generalized short-circuit ratio for overvoltage (gSCR-TOV) is introduced to quantify the risk of TOV in a multi-bus system. The intent of this concept is to permit screening to identify those nodes at greatest need for further EMT assessment. To localize these nodes, a concept of weighted sensitivity matrix is further proposed to pinpoint the bus subject to highest TOV in a MIPES. Finally, case studies of EMT time-domain simulations are carried out and verifies the effectiveness of the proposed methodologies.

Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatio-temporal variations of the demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios assume that the vehicular traffic follows certain spatio-temporal patterns, which may change in different seasons, and create capacity plans accordingly. In other words, they consider long-term capacity planning, leaving the adaptation to temporary changes or unexpected variations out of scope. In this work, we propose an integer linear programming (ILP) based framework to optimize the routing strategy of vehicular fog nodes (VFNs) in order to maximize the profit received by the service provider, taking into account the quality of service (QoS) received by the users and service level agreement (SLA) of various applications. To adapt to the temporal variations in demand, we predict the traffic flow and resource consumption from the users with feedback from service evaluation. To reduce the computational time and enable parallel processing, we create the capacity plan in two steps, namely global planning and regional planning. Through simulations, we show that the proposed solution achieves an 85% higher profit and a 20% higher service rate compared to the strategy where the VFNs randomly travel and serve the surrounding users without demand prediction. It achieves similar network latency compared to the strategy using only stationary fog nodes, but with a higher cost-efficiency. We also evaluate the impacts of number of VFNs, cost parameters, and regional size on the capacity plan. We find that a high number of VFNs, a small regional size, a high penalty cost, and low traveling and rental costs will lead to a high service rate; while a large regional size and low traveling, rental, and penalty costs will result in a high profit.

Selecting only the relevant subsets from all gathered data has never been as challenging as it is in these times of big data and sensor fusion. Multiple complementary methods have emerged for observing similar phenomena and oftentimes many of these techniques are superimposed in order to make the best possible decisions. A pathologist for example uses microscopic and spectroscopic techniques for the discrimination between healthy and cancerous tissue. Especially in the field of spectroscopy, immensely many frequencies are recorded and appropriately sized datasets are rarely acquired due to time intensive measurements and the lack of patients. In order to cope with the curse of dimensionality in machine learning, it is necessary to reduce the overhead from irrelevant or redundant features. In this article, we propose the FeaSel algorithm, that can be embedded in conventional neural networks. It recursively prunes the input nodes after the optimizer in the neural network achieves satisfying results. The weights of nodes that do not contribute critical information for the decision making will be deleted during the pruning process. We demonstrate the performance of the feature selection algorithm on different datasets and compare it to existing feature selection methods. Our algorithm combines the advantages of neural networks' non-linear learning ability and the embedding of the feature selection algorithm into the actual classifier optimization.

This paper presents a novel technique for improving the performance of a modal filter by using an electromagnetic absorber. The study considers a two-conductor modal filter based on a microstrip transmission line. The authors analyzed the effect of the absorber on the MF performance in suppressing conducted and radiated interferences (in the near and far fields). The frequency characteristics of the device were then redefined into the time characteristics in the Advanced Design System. As an excitation, the authors used an ultra-wideband interference pulse of 155 ps (at 0.5 level) and then employed N-norms to estimate and characterize the decomposed pulses. The results show that the use of the absorber can significantly improve the attenuation of the ultra-wideband interference. It was experimentally demonstrated that the value of N1 decreased by a factor of 4.31, N2 – by a factor of 6.52, N5 – by a factor of 1.87. In terms of radiated interference in the near field, the improvement in attenuation of each norm achieved at least 2.81 times. The analysis of the characteristics in the far field showed that the use of the electromagnetic absorber allows reducing the transmission coefficient on average by 2.25 times at a distance of 1 m. To validate the measurement results, the electrodynamic simulation of the modal filter characteristics in the frequency range up to 6 GHz was carried out and showed full agreement with the experimental results.

This paper presents a design methodology and prototyping of an angular speed sensor based on microwave polarimetry. The detection mechanism relies on the transmission response of a dual-polarized circular aperture irradiating an array of regularly spaced copper traces. The generated response is a regular pulse train defined by the location of a copper trace with reference to the center of the circular aperture. For speed measurement, the response was acquired within a 10s duration with the circular aperture response effectively sampled at 100 Hz. The acquired response was analyzed in the frequency domain using Fast Fourier Transform (FFT) and the rotor speed was obtained from the frequency spectrum of the acquired pulse train. The detection system can detect speeds as low as 0.4 RPM with an upper limit of 5000 RPM limited by the maximum sampling rate of the acquisition system. With the adoption of the FFT technique for speed estimation, the proposed angular speed measurement system can instantaneously detect angular speed with a maximum absolute estimation error of 0.0625 RPM within the 10 s acquisition time. The rotor occupies an area of 84.64 cm2.

It is necessary to control contact force through modulation of joint stiffness in addition to the position of our limb when manipulating an object. This is achieved by contracting the agonist muscles in an appropriate magnitude, as well as, balancing it with contraction of the antagonist muscles. Here we develop a decoding technique that estimates both the position and torque of a joint of the limb in interaction with an environment based on activities of the agonist-antagonistic muscle pairs using electromyography in real time. The long short-term memory (LSTM) network that is capable of learning time series of a long-time span with varying time lags is employed as the core processor of the proposed technique. We tested both the unidirectional LSTM network and bidirectional LSTM network. A validation was conducted on the wrist joint moving along a given trajectory under resistance generated by a robot. The decoding approach provided an agreement of greater than 93% in kinetics (i.e. torque) estimation and an agreement of greater than 83% in kinematics (i.e. angle) estimation, between the actual and estimated variables, during interactions with an environment. We found no significant differences in performance between the unidirectional LSTM and bidirectional LSTM as the learning device of the proposed decoding method.

Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications, such as finding missing children, family photo organization, or criminal investigations. Over the past decade, many efforts have been devoted to improving the verification performance of human faces only while lacking other biometric information, e.g., speaking voice. In this paper, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. Since there is still no standard and public audiovisual kinship dataset, we first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep learning-based fusion method, named Unified Adaptive Adversarial Multimodal Learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multi-modal features. First, the modality adversarial learning eliminates the cross-modality variations by confusing the discriminator. The attention module quantifies the importance of kinship interested features. The overall multimodal fusion network is trained in Siamese fashion to encourage the compactness of kinship and separation of non-kinship. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Further, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human kinship verification ability on a sub-set of TALKIN-Family. It indicates that human has higher accuracy when they have access to both faces and voice. The machine learning methods could effectively and efficiently outperform human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.

Recent years have seen unprecedentedly fast-growing prosperity in the commercial space industry. Several privately funded aerospace manufacturers, such as Space Exploration Technologies Corporation (SpaceX) and Blue Origin have innovated what we used to know about this capital-intense industry and gradually reshaped the future of human civilization. As private spaceflight and multi-planetary immigration gradually become realities from science fiction (sci-fi) and theory, both opportunities and challenges are presented. In this article, a review of the progress in space exploration and the underlying space technologies is firstly provided. For the next, a revisit and a prediction are paid and made to the K-Pg extinction event, the Chelyabinsk event, extra-terrestrialization, terraforming, planetary defense, including the emerging near-Earth object (NEO) observation and NEO impact avoidance technologies and strategies. Furthermore, a framework of the Solar Communication and Defense Networks (SCADN) with advanced algorithms and high efficacy is proposed to enable an internet of distributed deep-space sensing, communications, and defense to cope with disastrous incidents such as asteroid/comet impacts. Furthermore, the perspectives on the legislation, management, and supervision of founding the proposed SCADN are also discussed in depth.

The energy system decarbonization leads to a decentralization of generation and flexibility. Among the new concepts to integrate the distributed flexibility are local energy markets. While a broad range of research investigates local energy markets on a distribution grid level in detail, research on their system impact has been limited. Therefore, we develop a framework to investigate different configurations of local markets and operation of distributed energy resources and their interaction with the wholesale markets. This framework is applied to a pan-European case study with detailed bottom-up modeling of local energy markets in France and Germany. Additionally, transmission grid simulations are carried out to investigate the impact of local trading on grid congestions. Our results show that a direct coordination of distributed flexibility within the wholesale market is more efficient than a preference of local trading. Nonetheless, local energy markets can be used as an incentive for expansion of distributed flexibility as well as a way to break down the complexity of coordinating millions of distributed assets in a single market. Additionally, our results show that local trading relieves the transmission grid.

Assisting employees in acquiring the knowledge and skills necessary to grasp and use new services and technologies on the shop floor is critical for manufacturers to adapt to Industry 4.0 successfully. In this paper, we employ a Learning, Training, Assistance (LTA) approach and propose a framework for a Language-enabled Virtual Assistant (VA) to facilitate this adaptation. In our system, the human-robot interaction is achieved through spoken natural language and a dashboard implemented as a web-based application. This type of interaction enables operators of all levels to control a collaborative robot intuitively in several industrial scenarios and use it as a complementary tool for developing their competencies. Our proposed framework has been extensively tested with 29 users who completed various tasks while interacting with the proposed VA and industrial robots. Through three different scenarios, we evaluated the usability of the system for LTA based on an established System Usability Scale and the cognitive effort required by the users based on the standardised NASA-TLX questionnaire. The qualitative and quantitative results of the study show that users of all levels found the VA user friendly with low requirements for physical and mental effort during the interaction. Additionally, the study demonstrates that the VA enables operators to streamline the learning and training phases of new tasks and improve their user experience during the assistance phase for daily tasks. The source code of the proposed VA and the supplementary material of the user study are accessible at https://bit.ly/VA_MAX to support the reproducibility of the proposed framework.