We address the end-effector full-pose tracking control problem in free-floating space manipulators, experiencing constant non-zero linear and angular momentum. The aim is to develop an output-tracking (workspace) control law free of singularities due to parameterizing the end-effector motion and being robust against singularities of the input-output decoupling matrix (generalized Jacobian matrix). Space manipulators are modelled as open-chain multi-body systems with single- and multidegree-of-freedom joints, whose kinematics and dynamics are formulated on the Special Euclidean group SE(3). Such systems exhibit conserved (not necessarily zero) total momentum when operating in the free-floating regime, which we use to systematically reduce their dynamical equations by eliminating the base spacecraft’s motion. To avoid parameterizing the end-effector motion, we consider its full pose as the system output and develop a novel feedback linearization technique on the matrix Lie group SE(3) in the reduced phase space of the space manipulator. We then propose an intrinsic feedforward, feedback proportional-integral-derivative workspace controller involving a coordinate-free pose error function on SE(3) and velocity error on its Lie algebra. Using a Lyapunov candidate, this controller is proven to stabilize the end-effector pose to a feasible desired trajectory. The input-output decoupling matrix in the proposed control law can lose rank at some regions of the configuration space; hence, we implement a singularity-robust inverse, derived from the damped least squares method, to avoid impractical joint torques in these regions. The developed controller is implemented on a 7-degree-of-freedom manipulator onboard a spacecraft and its efficacy and robustness are demonstrated trough series of simulations.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-,  scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

Type-II emphasizing Type-II(B) in Ramond–Ramond Sector has been analyzed and computed from the Atiyah–Hirzebruch spectral sequence taking E_i sheets for the concerned values of i = 4=∞ and for E_n^(p,q) for n=1,2,3; several varieties of K–Theories where a transitive approach has been shown from the KK–Theory to K–Theory to String Theory concerning Fredholm modules of Atiyah–Singer Index Theorem and the Baum–Connes conjecture with respect to the Hilbert–A, Hilbert–B module and c*-algebras also in the reduced form taking Morita equivalence and the Kasparov composition product where extended relations has been provided between the equivalence of noncommutative geometry and noncommutative topology channelized through Poincaré Duality, Thom Isomorphism and Todd class.

Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from handcrafted features and deep features. Although handcrafted ones provide rich expert knowledge, current fusion methods do not thoroughly investigate the relatively important of handcrafted features with deep features, feature contribution imbalance, and the way features learn collaboratively. In this paper, a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) is proposed to effectively realize handcrafted feature and deep feature fusion in SAR ship classification task. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the feature map quality learned by each branch through auxiliary feature supervision, and introduces synergy loss to facilitate the information interaction between deep features and handcrafted features. The latter utilizes an attention mechanism to adaptively balance the importance among various features, and to assign the corresponding feature contribution to the total loss function based on the generated feature weights. We conduct extensive experimental and ablation studies on two public OpenSARShip-1.0 and FUSAR-Ship datasets, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous Internal FC Layer and Terminal FC Layer fusion, and exhibits better performance than the current state-of-the-art methods.

Hardware Trojan (HT) is the most critical threat due to outsourcing of Integrated circuit designing phases. Therefore, a new technique is proposed that utilizes structural and SCOAP features to detect HT from the gate-level netlist using Light Gradient Boosting (Light GBM). Further, a model agnostic Shapley additive explanations (SHAP) is employed to identify each feature global and local impact on model prediction. Moreover, a quartile-based feature selection method is proposed, which uses SHAP to identify the optimal feature set by keeping low retraining rounds. Experimental results show that the proposed technique accurately detects always-on-Trojans and HT nets from Trust-Hub, DeTrust, and DeTest benchmarks.

The performance of an underlay coordinated direct and relay transmission (CDRT) based non-orthogonal multiple access (NOMA) network is limited by the random nature of the transmit powers due to the interference temperature limit (ITL) imposed by the primary network. In this paper, we show that combining the signals at both near and far users can significantly enhance throughput. Considering the practical scenario of imperfect successive interference cancellation, the performance of near and far user nodes is analyzed in terms of throughput and outage probability. The traditional case of static ITL is considered first. Then, analysis is presented for the case when the ITL depends on the primary channel state information, and it is shown that the gain in performance can be very large over static ITL. Closed-form expressions for NOMA power pportioning are presented for a special case. Further, it is shown that how optimum parameters can be chosen to maximize the performance. Simulations validate the derived analytical expressions.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled massive multiple-input multiple-out (MIMO) non-orthogonal multiple access (NOMA) full-duplex (FD) two-way relay (TWR) system with low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) architecture. Minimum mean-square error (MMSE) channel estimator and maximum ratio combining/maximum ratio transmission (MRC/MRT) at the UAV are utilized to obtain the UAV-GUs channel state information (CSI) and process the signals of multi-pair ground user (GUs), respectively. By adopting the additive quantization noise model (AQNM), the approximate sum spectrum/energy efficiency (SE/EE) with imperfect CSI and imperfect successive interference cancellation (SIC) are derived. To evaluate the effects of the parameters on system performance, the asymptotic analysis is presented. Then, the power scaling laws are provided to characterize the asymptotic performance. The numerical results verify the accuracy of theoretical analysis and show that the interference and noise can be effectively eliminated by deploying large-scale antennas, lager Rician factors, and applying proper power scaling law. We also find that the proposed system can obtain better SE performance by adjusting the height of the UAV. Moreover, the performance of the proposed system is related to the ADCs/DACs quantization bits, where the SE saturate values increase with the increasing number of quantization bits, while the EE first increases and then decreases. Finally, the SE/EE trade-off of this low-resolution ADC/DAC quantized system can be achieved by choosing the appropriate number of quantized bits, and the trade-off region grows as Rician factor increases.

Data augmentation is an effective method to improve model robustness and generalization. Conventional routines of data augmentation pipeline are used as a preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. This survey provides a comprehensive and structured overview of the advances in DDA, with a focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations. We then categorize existing DDA works accordingly. Afterwards, the survey investigates the utilization of DDA in selected of practical applications, specifically neural augmentation and differentiable augmentation search. Finally, we discuss current challenges of DDA and some promising future research directions.

A node microstrip architecture is proposed for designing a compact Butler Matrix (BM) that is integrated with a dual-polarized endfire antenna array. The node microstrip architecture can be used to form a variety of compact 2*2 port networks including, but not limited to, couplers and crossovers. The optimized compact BM is potentially suitable for use in 5G wireless communication handsets.

In this study, the performance of underwater optical wireless communication links is investigated by taking into account turbulence, absorption and scattering effects. Weak turbulent channel is modeled using log-normal distribution while moderate and strong turbulence channels are modeled using gamma-gamma distribution. Rytov variance of Gaussian beam is derived analytically for oceanic turbulence optical power spectrum. Subsequently, scintillation index is calculated using the computed Rytov variance. Moreover, the closed-form expression of bit-error-rate (BER) for underwater wireless optical communication (UWOC) systems using intensity-modulated/direct detection (IM/DD) implementation and on-off-keying (OOK) modulation scheme is obtained. Results show that the performance of wireless optical communication link between two platforms in underwater medium is degraded significantly due to turbulence, absorption and scattering. In fact, as the turbulence level increases, its effect becomes quantitatively comparable to those of absorption and scattering effects. The variation of both scintillation index and BER performance are presented for various underwater medium and communication system parameters, such as chlorophyll concentration, average temperature, average salinity concentration, temperature and dissipation rates, wavelength, link length and receiver aperture size. Optical network and internet of underwater things (IoUT) applications, which are growing day by day and requiring high data rates, will benefit from the results of this study.

This work presents a 340-GHz frequency-modulated continuous-wave (FMCW) pulse-Doppler radar. The radar system is based on a transceiver module with about one milli-Watt output power and more than 30-GHz bandwidth. The front-end optics consists of an off-axis parabola fed by a horn antenna from the transceiver unit, resulting in a collimated radar beam. The digital radar waveform generation allows for coherent and arbitrary FMCW pulse waveforms. The performance in terms of sensitivity and resolution (range/cross-range/velocity) is demonstrated, and the system’s ability to detect and map single particles (0.1–10 mm diameter), as well as clouds of particles, at a 5-m distance, is presented. A range resolution of 1 cm and a cross-range resolution of a few centimeters (3-dB beam-width) allow for the characterization of the dynamics of particle clouds with a measurement voxel size of a few cubic centimeters. The monitoring of particle dynamics is of interest in several industrial applications, such as in the manufacturing of pharmaceuticals and the control/analysis of fluidized bed combustion reactors.

By applying rotor angle sensorless control methods the costs of the electrical drive can be decreased while its reliability is increased. Traditionally, the design of both the rotor angle estimator and the drive controller require a detailed motor model and manual tuning leading to significant effort by human experts. In this work, a rotor anisotropy-based sensorless control scheme for interior permanent magnet synchronous motors (IPMSM) is proposed that enables sensorless control with minimum tuning effort and a priori motor knowledge. Due to these characteristics the scheme is particularly suitable for self-commissioning or low-cost drive applications. The scheme contains a finite control set model predictive current controller (FCS-MPCC) with an additional inequality constraint that enables the identification of the motor model in the stator-fixed coordinate system by using the last three measurement samples. Utilizing an automatic system identification procedure, the motor model is determined online in a data-driven fashion. The identified motor model serves both as prediction model of the FCS-MPCC and as  baseline for the rotor angle estimation via an eigenvalue decomposition approach. Challenging experimental investigations at standstill up to the medium speed range prove the applicability of the proposed approach. Here,  highly dynamic speed and current transients can be handled by the proposed method.

Optical microcombs represent a new paradigm for generating laser frequency combs based on compact chip-scale devices, which have underpinned many modern technological advances for both fundamental science and industrial applications. Along with the surge in activity related to optical micro-combs in the past decade, their applications have also experienced rapid progress ‒ not only in traditional fields such as frequency synthesis, signal processing, and optical communications, but also in new interdisciplinary fields spanning the frontiers of light detection and ranging (LiDAR), astronomical detection, neuromorphic computing, and quantum optics. This paper reviews the applications of optical microcombs. First, an overview of the devices and methods for generating optical microcombs is provided, which are categorized into material platforms, device architectures, soliton classes, and driving mechanisms. Second, the broad applications of optical microcombs are systematically reviewed, which are categorized into microwave photonics, optical communications, precision measurements, neuromorphic computing, and quantum optics. Finally, the current challenges and future perspectives are discussed.

When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learningbased localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

Thin shell model of a coated conductor with a ferromagnetic support, efficient spectral method for numerical simulations

The work is related to the privacy of smart meter and the benifits of using URLLC in smart grid network.

We introduce a neural network model for regression in which prediction uncertainty is quantified by Gaussian random fuzzy numbers (GRFNs), a newly introduced family of random fuzzy subsets of the real line that generalizes both Gaussian random variables and Gaussian possibility distributions. The output GRFN is constructed by combining GRFNs induced by prototypes using a combination operator that generalizes Dempster’s rule of Evidence Theory. The  three output units indicate the most plausible  value of the response variable, variability around this value, and epistemic uncertainty. The network is trained by minimizing a loss function that generalizes the negative log-likelihood. Comparative experiments show that this method is competitive, both in terms of prediction accuracy and calibration error, with state-of-the-art techniques such as random forests or deep learning with Monte Carlo dropout. In addition, the model outputs a predictive belief function that can be shown to be calibrated, in the sense that it allows us to compute conservative prediction intervals with specified belief degree.

Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment.