In this paper, a novel reinforcement learning (RL) approach, continuous dynamic policy programming (CDPP) is proposed to tackle the issues of both learning stability and sample efficiency in the current RL methods with continuous actions. The proposed method naturally extends the relative entropy regularization from the value function-based framework to the actor-critic (AC) framework of deep deterministic policy gradient (DDPG) to stabilize the learning process in continuous action space. It tackles the intractable softmax operation over continuous actions in the critic by Monte Carlo estimation and explores the practical advantages of the Mellowmax operator. A Boltzmann sampling policy is proposed to guide the exploration of actor following the relative entropy regularized critic. Evaluated by several benchmark tasks, the proposed method clearly illustrates the positive impact of the relative entropy regularization including efficient exploration behavior and stable policy update in RL with continuous action space and successfully outperforms the related baseline approach in both sample efficiency and learning stability.

The accuracy of near-field antenna measurements combined with near-field far-field transformations (NFFFT) is deteriorated if objects are located in close vicinity to the antenna under test (AUT). We compare two equivalent sources based modeling techniques to suppress the influence of structures in the vicinity of the AUT. Simulation results for perfect electrically conducting objects showcase the capabilities and limitations of both approaches. The presented methods can easily extend existing NFFFTs in order to remove the influence of mounting structures or parts of the positioning system in anechoic near-field measurement facilities.

A low-order discretization scheme for the magnetic field integral equation (MFIE) with considerably improved accuracy is discussed and analyzed. The formulation is based on the classical form of the MFIE discretized with Rao-Wilton-Glisson functions and introduces weak-form basis transformations, which have recently been employed with great success within the context of the so-called combined source integral equation. Only the identity operator discretization, which is known to be a major error source, is modified in order to obtain highly accurate solutions. Numerical results are given to corroborate the effectiveness of the new scheme.

The electric field integral equation (EFIE) for perfect electrically conducting objects is augmented by a weak form combined source side condition. Thereby, the solution of the resulting combined source integral equation (CSIE) is unique and the interior resonance problem is circumvented. The discretization of electric and magnetic surface current densities and the testing of the electric field is solely performed with Rao-Wilton-Glission functions. Simulation results of scattering by a sphere demonstrate the good iterative solver convergence and excellent accuracy, as compared to EFIE as well as magnetic and combined field integral equations.

The accuracy problem of the classically Rao-Wilton-Glisson (RWG) discretized magnetic field and combined field integral equations originates from the strong singularity of the identity operator. With a novel basis transformation scheme, i.e. two subsequent weak-form rotations of the RWG basis functions, the accuracy of the identity operator discretization is improved. The presented discretization scheme is immediately compatible with fast algorithms and is combined with the multilevel fast multipole algorithm. The good accuracy and the good conditioning behavior of the formulation are verified in several numerical simulations, also including electrically large problems.

Amputees face significant challenges in performing daily activities after losing an upper limb. Replacing a lost limb with a device with capabilities comparable to a human hand is a keen area of interest for prosthetics researchers. Despite advances in the field of prosthetics, the number of amputees who use these devices remains low. This study presents an open-source, low-cost, and anthropomorphic prosthetic hand solution for people with upper-limb amputations that outperforms currently available devices in terms of usability and biomimetic performance. The work aims to improve the acceptance of these devices among amputees by reducing the cost and improving the performance of these devices. We briefly discussed how the adaptive force magnification technique could be applied to a prosthetic device to improve biomimetic performance characteristics such as gripping force and velocity. A non-backdriveable modular under-actuated mechanism is designed that synergistically moves the fingers to adapt according to the shape of the object. The developed device costs one order less than the cheapest available prosthetics in the market and is customizable according to the user. The experiments were conducted to evaluate the biomimetic performance characteristics and the grasping effectiveness of the device in performing activities of daily life.

Abstract: Tensor tracking which is referred to as online (adaptive) decomposition of streaming tensors has recently gained much attention in the signal processing community due to the fact that many modern applications generate a huge number of multidimensional data streams over time. In this paper, we propose an effective tensor tracking method via the tensor-train format for decomposing high-order incomplete streaming tensors. On the arrival of new data, the proposed algorithm minimizes a weighted least-squares objective function accounting for both missing values and time-variation constraints on the underlying tensor-train cores, thanks to the recursive least-squares filtering technique and the block coordinate descent framework. Our algorithm is fully capable of tensor tracking from noisy, incomplete, and high-dimensional observations in both static and time-varying environments. Its tracking ability is validated with several experiments on both synthetic and real data. Code: https://github.com/thanhtbt/ATT-miss/ Comment: Accepted by Signal Processing Journal Â

Starting from the monopolar representation of the Rao-Wilton-Glisson (RWG) functions, an approximate inverse (AI) of the RWG Gram matrix is constructed, where only 3x3 matrices for all individual triangles have to be inverted. The inversion of the Gram matrix is, however, only approximate, since it results as the inverse of a product of singular matrices, which is not easily obtained. The AI is shown to be an effective preconditioner for the RWG Gram matrix and is in particular useful for ill-conditioned Gram matrics on distorted meshes.

Optical full-field recovery makes it possible to compensate fiber impairments such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processing. Coherent detection dominates over the long-haul transmission system due to its high spectral efficiency and tolerance against optical impairments. For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect, and improve the spectral efficiency for larger potential capacity. Polarization division multiplexing (PDM) can further double both the spectral efficiency and the system capacity of these SP carrier-assisted direct detection (DD) schemes. However, the so-called polarization fading phenomenon induced by random polarization rotation is a fundamental obstacle to exploit the polarization diversity for carrier-assisted DD systems. In this paper, we propose a receiver of Jones-space field recovery (JSFR) to realize polarization diversity with SP carrier-assisted DD schemes in the Jones space. There different receiver structures of proposed JSFR and the simplification for JSFR are explored theoretically. The proposed JSFR pushes the SP DD schemes towards PDM without no extra optical signal-to-noise ratio (OSNR) penalty. In addition, the JSFR shows good tolerance to PMD since the optical field recovery is conducted before polarization recovery. In the concept-of-proof experiment, we demonstrate 448-Gb/s reception over 80-km single-mode fiber using the proposed JSFR based on 2×2 couplers. Qualitatively, we compare the optical field recovery in the Jones space and Stokes space from the perspective of the modulation dimension.

We proposed a universal clustering algorithm by constructing an adaptive density gradient. This algorithm showed no data preference, and performs well on data with arbitrary density, overlap and shape. In comparative experiments, it outperformed other state-of-the-art algorithms on all types of synthetic and real data.

Our proposed system, Audibly, is a desktop app which records the input using microphone. The system then converts the input audio into text and processed.After processing text, the text will be split to an individual word. Each word will be searched in the ASL dictionary and its corresponding videoclip will be merged to output video. If not found, the word will be stemmed to its root word and searched again else videoclip of each alphabet will be used. ASL dictionary contains videoclips of more than 800 words and alphabets. Output video will be formed by merging sub videos in mp4 format. This video will be displayed in output page and users can if needed.

In this paper, we introduce new distributed diffusion algorithms to track a sequence of hidden random matrices that evolve on the special orthogonal group. The algorithms are based on the Adapt-then-Combine and the Random Exchange methods, and diffuse Gaussian approximations of posterior densities computed in the Lie algebra of the special orthogonal group. Simulation results show that, in a scenario with strongly nonlinear observation functions, the proposed algorithms perform similarly to the centralized particle filter estimator and can outperform competing Extended Kalman Filters.

Automatic detection of surface deformation features from a large volume of Interfero16 metric Synthetic Aperture Radar (InSAR) data is challenging because the magnitude of InSAR measurement noise varies substantially in both space and time. In this work, we present a computer vision algorithm based on Laplacian of Gaussian (LoG) filtering for detecting the size and location of unknown surface deformation features. Because our algorithm detects spatially coherent features, tropospheric noise artifacts that share similar spatial characteristics may also be detected. We estimate the tropospheric noise spectrum directly from data, which allows us to simulate new instances of noise that resemble the actual InSAR observations. Based on these simulations, we quantify the likelihood that a detected feature is a real deformation signal. We demonstrate the performance of our algorithm using Sentinel-1 data acquired between 2014 and 2019 over the ∼ 80,000 km2 oil-producing Permian Basin in West Texas, one of the most productive oil fields in the world. We detect clusters of deformation features associated with oil production, wastewater injection, and fault activities. The number of detected deformation features increases substantially over the study period, which is consistent with the over-all rise in oil production within the Permian Basin since 2014. Our algorithm is robust and flexible, and can be integrated to various multi-temporal InSAR time series methods for detecting a broad range of deformation features.

Equivalence, duality, and invariance are the pin-points of unification in modern theoretical physics that got the twist of topologies when going beyond the notions of differential and conformal domains to geometries to the symplectic norm of topologies with the pillars being the algebraic geometry taking the counting of specified states through the Enumerative ones.

Conventional phaseless near-field measurement data is not adequate for reliable transformation into the far field. We tackle this challenge by a formulation working with multifrequency phaseless measurements under the assumption of coherently measured spectra. Such data is, for instance, obtained with a transmitting antenna under test and standard receivers featuring a nonzero coherent bandwidth. The focus of this work is on an advanced algorithm to demonstrate that this ansatz is very promising for further research and real-world investigations. Empirical studies based on simulation data demonstrate that appropriately merging this multi-frequency data significantly increases the chance of successful phaseless near-field far-field transformation. As a by-product, this multi-frequency phase retrieval method supports also the retrieval of the phase of farfield antenna measurements.

We present a low-cost and light-weight measurement equipment based on a software-defined radio (SDR) and a radio-frequency over fiber (RFoF) connection. The main purpose of the hardware are near-field antenna measurements, but possible use-cases also include imaging and other inverse-source scenarios. The two features “low-cost” and “light-weight” of the hardware are important for the use on an unmanned aerial vehicle: Crashes should not financially ruin the operator and payload is of course a central issue for any flying object. Regarding the RF properties of the hardware, the RFoF connection has the great benefit of extremely low loss as compared to coaxial cables, while the SDR offers a great deal of flexibility concerning measurement frequency, bandwidth, and signal filtering. One transmit channel of the SDR is employed to provide a coherent signal to an antenna and two receive channels can capture two components of the field, e.g., two linearly independent polarizations. As an important part of the RF circuitry, a drift compensation is implemented as a countermeasure against changing temperature conditions.

A near-field antenna measurement system is presented that consists of components that are rather unusual compared to conventional antenna measurement setups. Instead of a vector network analyzer (VNA), a dual-channel wideband software defined radio (SDR) is used to measure the signals at the ports of a dual-polarized probe antenna. Instead of an exact multi-axis positioner for the antenna under test (AUT) or the probe antenna, a multicopter moves the probe along a predefined trajectory on a surface around the AUT. Instead of using expensive laser interferometry equipment, the position and orientation of the probe antenna are determined by a 6-D tracking system that was originally developed for virtual reality (VR) applications. Still, the first measurement results show the usability of the low-cost system for antenna measurements in the frequency range of mobile communication services.

The free-space radiation characteristic of an antenna under test (AUT) is determined from measurements in proximity of a scatterer by time gating reconstructed equivalent currents for the AUT and the scatterer. The presented approach effectively combines spatial filtering methods with time domain methods while mitigating their individual drawbacks. In contrast to conventional time-gating methods which usually work on the measured probe signals this approach allows to get rid of undesired echo perturbations even if measurement samples are located in the shadow region of the scatterer and the line of sight (LOS) contribution and the echo contribution are indistinguishable for the field probe. In contrast to conventional frequency domain methods, mutual coupling contributions of the AUT currents are identified and removed in the time domain representation of the reconstructed currents. Numerical examples show that, due to the sparse time domain representation of the currents, the free-space radiation of the AUT can be determined accurately even at the borders of the measured bandwidth.