The droop controllers of inverter-based resources (IBRs) can be adjustable by grid operators to facilitate regulation services. Considering the increasing integration of IBRs at power distribution level systems like microgrids, cyber security is becoming a major concern. This paper investigates the data-driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement learning for inverter-based microgrids. Firstly, the full-order high-fidelity model and reduced-order small-signal model of typical inverter-based microgrids are recapitulated. Then the destabilizing attack on the droop control gains is analyzed, which reveals its impact on system small-signal stability. Finally, the attack and defense problems are formulated as Markov decision process (MDP) and adversarial MDP (AMDP). The problems are solved by twin delayed deep deterministic policy gradient (TD3) algorithm to find the least effort attack path of the system and obtain the corresponding robust defense strategy. The simulation studies are conducted in an inverter-based microgrid system with 4 IBRs and IEEE 123-bus system with 10 IBRs to evaluate the proposed method.

We present a new machine learning algorithm, Latent Similarity, and use it to predict subject (endo)phenotypes from fMRI data. fMRI can be used to predict dysfunctional mental states. In addition, endophenotypes are known to be predictive of disease status, and are of interest in developmental studies. However, fMRI studies often suffer from small cohort size and high feature dimensionality, making reproducible prediction challenging. The innovation of our algorithm is to combine a kernel similarity function with metric learning to increase the robustness of prediction. Our algorithm becomes robust by utilizing the N squared connections between the N subjects in the cohort, rather than the features of the N subjects themselves. We identify important functional connections in the default mode and uncategorized functional networks for predicting age, sex, and intelligence. We also find that only a few connections contain most of the information required for any predictive task. We believe that our algorithm can be beneficial in small sample size, high noise, high dimensionality settings.

Cognitive unmanned aerial vehicles (CUAVs) play a vital role in next-generation wireless networks as they assist in massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. This study focuses on multiple CUAV-enabled networks, wherein CUAVs are paired with each other. We analyze the data rate, energy efficiency, and latency of such networks by applying the finite information block length theory, wherein mMTC and URLLC information use a non-orthogonal multiple access technique. Furthermore, we formulate an optimization problem to maximize the energy efficiency of paired CUAV devices by jointly optimizing the transmission power of the mMTC and URLLC information to satisfy the latency requirement. The numerical results indicate that our proposed multiple-CUAV-enabled scheme enhances the network performance of CUAV devices in terms of energy efficiency and latency better than the existing scheme.

A novel algorithm for on-board satellite hyperspectral data compression is proposed. Computational complexity is targetted with comparable compression performance with state-ot-the-art on-board satellite hyperspectral data compression methods.

This article has been accepted for publication in IEEE/OSA Journal of Lightwave Technology. Citation information: DOI 10.1109/JLT.2022.3198649 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 limits 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 showed 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 this work, using investigation of odd order OAM modes in the two-dimensional MMI waveguides, a novel integrated optical OAM device is introduced to do mode conversion between beams carrying OAM with opposite topological charges. The proposed convertor is passive and the input and output ports can be swapped. The ability to produce high purity output mode (94% and 82% for OAM modes with l=±1 and l=±3, respectively), reciprocal behavior and compatibility with silicon on insulator technology are the remarkable features that make the proposed passive convertor utilizable in many classical and quantum communication systems.

The direct array factor synthesis method is extended in this paper. Two parallel current sheets are used for synthesizing array factors with non mirror-symmetrical radiation patterns in the two sides of the sources.  Analysis shows that the synthesized current distributions are affected by the distance between the two current sheets.  Numerical results demonstrated that the method is efficient for synthesizing large scale antenna arrays with non mirror-symmetrical footprints.

Chronic wounds affect millions of people worldwide every year. An adequate assessment of a wound’s prognosis is a critical aspect of wound care since it assists clinicians in understanding wound healing status, severity, triaging and determining the efficacy of a treatment regimen, thus guiding the clinical decision making. The current standard of care involves using wound assessment tools, such as Pressure Ulcer Scale for Healing (PUSH) and Bates-Jensen Wound Assessment Tool (BWAT), to determine wound prognosis. However, these tools involve manual assessment of a multitude of wound characteristics and skilled consideration of a variety of factors, thus, making wound prognosis a slow process which is prone to misinterpretation and high degree of variability. Therefore, in this work we have explored the viability of replacing subjective clinical information with deep learning-based objective features derived from wound images, pertaining to wound area and tissue amounts. These objective features were used to train prognostic models, that quantified the risk of delayed wound healing, using a dataset consisting of 2.1 million wound evaluations derived from more than 200,000 wounds. The objective model, which was trained exclusively using image-based objective features, achieved at minimum a 5% and 9% improvement over PUSH and BWAT, respectively. Our best performing model, that used both subjective and objective features, achieved at minimum an 8% and 13% improvement over PUSH and BWAT, respectively. Moreover, the reported models consistently outperformed the standard tools across various clinical settings, wound etiologies, sexes, age groups and wound ages, thus establishing the generalizability of the models.

Advances in Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) are enabling remote health monitoring in everyday settings for early detection and prevention of chronic health problems. Such solutions can be used to augment a conventional physician-centered healthcare system. Stress, as one of the critical health problems, affects individuals adversely in terms of both physical and mental health. Prior studies on stress evaluation utilize a centralized cloud-based approach that combines data from each client for modeling. However, such a centralized approach raises data privacy concerns. To preserve privacy, decentralized federated learning has been proposed as a potential alternative framework. Nevertheless, existing federated learning algorithms have to deal with data heterogeneity; data skewness in each participant can significantly degrade the overall model performance. To tackle this challenge, we present a personalized, low-overhead clustered federated learning algorithm for stress-level recognition. The proposed algorithm outperforms two state-of-the-art baseline algorithms by providing over 7% and 12% increase in accuracy, respectively. The proposed algorithm also obtains a reduction of 37.5% and 9.6% in the training runtime compared to the two baseline algorithms. We also present a novel cold-start algorithm for new clients who join the trained system. Our results suggest that this cold-start algorithm is robust in terms of individual classification accuracy and total training time.

Nanometer-resolution position sensing is a critical requirement in several precision applications such as Atomic Force Microscopy (AFM), especially over large bandwidths. Giant magnetoresistance-based (GMR) sen?sors have shown great promise for this; however, they exhibit significantly higher 1/f noise even over large band?widths, which limits the achievable SNR. Therefore, the sensing system needs to be designed at the system-level in order to minimize excess intrinsic noise generation and transmission, as well as external noise pickup. This article, both analytically and experimentally, investigates the noise characteristics of the GMR sensor and its readout circuit components, and their effect on SNR. A general process for designing a low-noise high-bandwidth readout circuit, applicable not just to GMR sensors but also to other high?1/f-noise sensors, is presented. A resolution of 2.5 nm over a bandwidth of 100 kHz is demonstrated on an AFM nanopositioner, a ten-fold improvement over previously reported performance and comparable to other state-of?the-art, but more complex, position sensing schemes. The readout scheme is simple to implement using COTS com?ponents and easily extendable to higher bandwidths, unlike other schemes, such as modulation/demodulation, where the requirement for increasingly higher carrier frequencies renders further improvements in bandwidth impractical.

Fully Metallic Gap Waveguide Based Bandpass Frequency Selective Surface Radome for Fixed Beam Array Antenna at mmWave Band

Water utilities across the globe are concerned with the inspection and replacement of buried metallic water pipes due to corrosion-related structural damages. Internal pipe linings are commonly used as a renewal method to improve structural strength as they are regarded to be a less expensive alternative to costly and time-consuming pipe replacements. However, linings are also prone to failure as well. Therefore, water authorities regularly monitor lining performance, where defect evolution over a long period of time is an important parameter to note. It requires an accurate in-pipe robot localization technology. In this article, we propose a novel method for in-pipe robot localization and tag mapping that uses battery-free UHF-RFID sensor wireless signals. It utilizes a signal mapping approach in combination with a tailored pose-graph simultaneous localization and mapping algorithm. Evaluation results of a field-extracted pipe sample from Sydney Water’s distribution network show the proposed approach is capable of localizing the robot within 2.5cm accuracy in a 50m equivalent pipe with an unknown UHF-RFID distribution. The proposed approach outperformed other reported similar work in the literature.

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)—originally designed for nonlinear dimensionality reduction—for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probabilistic density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

Graph signal processing (GSP) techniques are powerful tools that model complex relationships within large datasets, being now used in a myriad of applications in different areas including data science, communication networks, epidemiology, and sociology. Simple graphs can only model pairwise relationships among data which prevents their application in modeling networks with higher-order relationships. For this reason, some efforts have been made to generalize well-known graph signal processing techniques to more complex graphs such as hypergraphs, which allow capturing higher-order relationships among data. In this paper, we provide a new hypergraph signal processing framework (t-HGSP) based on a novel tensor-tensor product algebra that has emerged as a powerful tool for preserving the intrinsic structures of tensors. The proposed framework allows the generalization of traditional GSP techniques while keeping the dimensionality characteristic of the complex systems represented by hypergraphs. To this end, the core elements of the t-HGSP framework are introduced, including the shifting operators and the hypergraph signal. The hypergraph Fourier space is also defined, followed by the concept of bandlimited signals and sampling. In our experiments, we demonstrate the benefits of our approach in applications such as clustering and denoising.

This work proposes two algorithms for maximizing the sum-rate and fairness in downlink non-orthogonal multiple-access (DL-NOMA) systems with indoor-outdoor user pairing. Specifically, one of the proposed algorithms is based on the streamlined simplex method (SSM), while the other utilizes the least and most cost method (LMCM). The fairness rate in both cases is optimized using the max-min approach for the indoor device, which is assumed to have a lower channel gain due to its inherently more challenging environment. Various performance metrics such as the achievable data rate, fairness index, and energy-efficiency are analyzed to evaluate the effectiveness of the proposed algorithms relative to different benchmarks such as the Near-Far pairing (NFP) and the random user-pairing (RUP). The results show that the LMCM algorithm incurs lower complexity and offers better data rate and energy-efficiency when compared to the SSM algorithm, the NFP and RUP methods.

Computed tomography (CT) as an important clinical diagnostics method can profit from extension with dark-field imaging, as it is currently restricted to X-rays’ attenuation contrast only. Dark-field imaging allows access to more tissue properties, such as micro-structural texture or porosity. The up-scaling process to clinical scale is complex because several design constraints must be considered. The two most important ones are that the finest grating is limited by current manufacturing technology to a 4.8µm period and that the interferometer should fit into the CT gantry with minimal modifications only. In this work we discuss why an inverse interferometer and a triangular G1 profile are advantageous and make a compact and sensitive interferometer implementation feasible. Our evaluation of the triangular grating profile reveals a deviation in the interference pattern compared to standard grating profiles, which must be considered in the subsequent data processing. An analysis of the grating orientation demonstrates that currently only a vertical layout can be combined with cylindrical bending of the gratings. We also provide an in-depth discussion, including a new simulation approach, of the impact of the extended X-ray source spot which can lead to large performance loss and present supporting experimental results. This analysis reveals a vastly increased sensitivity to geometry and grating period deviations, which must be considered early in the system design process.

Analysis of sound source directivity uncertainty for room acoustics simulations and auralizations.

A key challenge for a common waveform for Integrated Sensing and Communications (ISAC) – widely seen as an attractive proposition to achieve high performance for both functionalities, while efficiently utilizing available resources – lies in leveraging information-bearing channel-coded communications signals (c.c.s) for sensing. In this paper, we investigate the sensing performance of c.c.s in (multi-user) interference-limited operation, and show that it is limited by sidelobes in the range-Doppler map, whose form depends on whether the c.c.s modulates a single-carrier or OFDM waveform. While uncoded communications signals – comprising a block of N i.i.d zero-mean symbols – give rise to asymptotically (i.e., at large N) zero sidelobes due to the law of large numbers, it is not obvious that the same holds for c.c.s, as structured channel coding schemes (e.g., linear block codes) induce dependence across codeword symbols. In this paper, we show that c.c.s also give rise to asymptotically zero sidelobes – for both single-carrier and OFDM waveforms – by deriving upper bounds for the tail probabilities of the sidelobe magnitudes that decay as exp( - O(code rate x block length)). This implies that for any code rate, c.c.s are effective sensing signals that are robust to multi-user interference at sufficiently large block lengths, with negligible difference in performance based on whether they modulate a single-carrier or OFDM waveform. We verify the latter implication through simulations, where we observe the sensing performance (characterized by the detection and false-alarm probabilities) of a QPSK-modulated c.c.s (code rate = 120/1024, block length = 1024 symbols) to match that of a comparable interference-free FMCW waveform even at high interference levels (signal-to-interference ratio of -11dB), for both single-carrier and OFDM waveforms.