It is well known from the extant circuit theories that some important nonlinear circuit configurations cannot be modeled analytically. For resistive devices, the crux of this limitation is found to be the existence of non-ohmic resistances. Here, it is argued that the idea of nonlinear devices being always non-ohmic is misconceived and Ohm’s law should rather be applicable to all resistive nonlinear devices and circuits. Based on a recent research on modification of the general diode equation, an interpretation of the Ohm’s law is drawn so that it can be applied to nonlinear circuits as well. Additionally, the physics behind the important phenomenon of induced change in resistance, that enables the use of Ohm’s law in semiconducting junction devices, is explained and its inclusion is made in the equations for circuit design and analysis.

Phishing Website Data set from UCI ML Repository Size (11055,31), 11055 samples and 30 features with 1 class label. Reference [10] in the paper. [10] R. M. Mohammad, F. Thabtah, and L. McCluskey, “Phishing Websites  Features,” School of Computing and Engineering, University of  Huddersfield, 2015. [Online]. Available: Index of /ml/machine?learning-databases/00327 (uci.edu)

In this paper, a 5.8 GHz linear power amplifier (PA) in a 130 nm CMOS process using transconductance linearization technique is presented. The power stage consists of a main amplifier with Class-F load impedance and two auxiliary amplifiers which are biased for weak Class-AB. The proposed configuration increases both the linearity of the PA at high output power levels and the efficiency at back-off powers. The AM-AM and AM-PM nonlinearities of the auxiliary amplifiers cancel the same nonlinearities of the main amplifier at higher output powers. A bypass network is introduced to prevent a positive feedback and common-mode oscillations through the supply path. The PA shows a saturated output power of 23 dBm with 41% PAE and 17dB small-signal gain. Dynamic supply voltage allows a low-power operation with improved efficiency for back-off input powers. For supply voltages of 1.8V, 2.2V, and 3.3V the linear output power and PAE are 12.67dBm, 16.77dBm, and 18.1dBm and 11%, 19.27%, and 20.44%.

In this paper, a novel reconfigurable 5.8 GHz linear power amplifier with a digital pulse-shaping filter (PSF) to improve the spurious is presented for dedicated short-range communication (DSRC) transceivers. An on-chip reconfigurable load transfer network (LTN) is able to match the output load variations to PA while keeping output return loss blow -10 dB. Furthermore, LTN improves the stability by varying quality factor of LTN for different operation conditions. The power amplifier consists of a Class-AB driver stage and a cascode topology for the PA core. To improve the spurious of the PA and controlling modulation index a gaussian-based digital pulseshaping filter is implemented which has achieved an occupied bandwidth (OCB) of less than 2 MHz. Realized in a 0.13-m CMOS technology with an area of 1.1 mm2, the power amplifier achieves 10 dBm and 25 dB output power and dynamic range, respectively. PA dissipates 76 mA from 3-V supply voltage.

In this work, we present a deep neural network method for solving two-dimensional boundary value problems (BVPs) formulated in terms of boundary integral equations (BIEs). It is assumed that the boundary is parameterized by Non-Uniform Rational B-Splines (NURBS), commonly used in CAD, and the solution is approximated by a deep neural network with unknown weights and biases. The network is trained to minimize the loss function, which is formulated as an error in the BIE at a set of collocation points. The method inherits main advantages of boundary-type methods over the domain type methods: (a) the problem is solved on the boundary only, hence it requires much smaller number of collocation points, leading to significant savings in the computational cost; (b) for unbounded domains, asymptotic behavior of the solution at infinity is taken into account automatically and BIE is formulated on the inner boundary only; (c) high precision due to the exact NURBS-parameterization of the boundary, tight link to CAD and the ability to treat irregular boundaries (cracks, sharp corners, etc). Standard approaches to integration and removal of kernel singularity, used in Boundary Element Methods (BEM), are adopted. Application of the method to three benchmark problems for the Laplace equation is demonstrated. In all cases, a good agreement with the analytical solutions is achieved.

Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2D ultrasound imaging. The reliability of these measurements strongly depends on the correct pose of the transducer such that the 2D imaging plane properly aligns with the heart for standard measurement views, and is thus dependent on the operator’s skills. In this work, we propose a deep learning-based tool that provides real-time feedback on how to move the transducer to obtain the required views. We believe this method can aid less-experienced users to acquire recordings of better quality for measurements and diagnosis, and to improve standardization of images for more experienced users. Training data was generated by slicing 3D ultrasound volumes, which permits to simulate movements of a transducer and 2D imaging plane. Each slice was labelled with an anatomical reference obtained through a semi-automatic annotation procedure, which allowed us to generate substantial amounts of training data. The method was validated and tested on 2D images from several datasets representative of a prospective clinical setting. We proposed a new metric to score the correctness of the transducer movement feedback according to several given criteria, and achieved a success rate of 75% for all models and 95% for the rotational movement. A real-time prototype application was developed based on data streaming from a clinical ultrasound system, which demonstrated the ability of the method to robustly predict the apical rotation and tilt of the 2D ultrasound image plane relative to the heart.

Without violating the 4 natural laws of physics viz. cosmic censorship hypothesis, chronology protection conjecture, special and general theory of relativity, a theory has been established in this paper which would allow the phenomena of explaining the UFOs seen from Earth, International Space Station, Aerial Aircraft in such a way that, mysteries can be solved on how those UFOs can come to Earth either by crossing Mega Parsec (10 6 *3.28 Light Years), Giga Parsec (10 9 *3.28 Light Years) or from the faraway future Earth. Emphasis has been given to mathematical models to solve these mysteries without violating the aforesaid models of physical laws. Time has been treated as a physical dimension parameterized by measurements like other 3 D’s. This model aims to reduce space and time using a completely new idea called “Temporal Choke Point” and its associated mathematics by completely relying on known fundamental theories but without violating them in any proper ways possible. And apart from that, a detailed analysis has been done on ‘gravity’ from the perspectives of string theories and thereby concluding some interesting Phenomenology happening at the micro perspective of the ”Temporal Choke Point ξ³ᴰ.

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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.

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.

Intelligent and autonomous networks require precise and fast mechanisms that ensure error-free and efficient operation. Modern solutions are increasingly based on  artificial intelligence, in particular on machine learning, to reliably process huge amounts of data. Therefore, high-quality datasets are essential to train machine learning models. Unfortunately, the problem of assessing the quality of datasets is very challenging and often overlooked. This paper proposes a method for assessing the dataset quality in the context of binary classification. It is based on permutation testing and examines the strength of the relationship between observations and labels. Experiments carried out on simulated and real network datasets show that the method is sensitive to detect errors/mislabels in the labeled dataset. We also present theoretical considerations justifying our results.

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

The inverse source problem of near-field antenna measurements is often formulated in terms of unknown equivalent electric and magnetic surface current densities. Exploiting just the constraints for the radiated field exterior to a closed Huygens surface results in ambiguous equivalent sources due to the excitation of non-radiating currents during the solution process. Therefore, enforcing the zero-field or Love side condition for the suppression of non-radiating currents based on a left-hand side preconditioner in the form of a Calderón projector is proposed. Transformation results based on realistic measurements demonstrate the effectiveness of this approach.

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.

For antenna measurements, the device under test (DUT) is usually transported to an anechoic chamber equipped with a scanner system. To measure on-site, unmanned aerial vehicles (UAVs) are a flexible solution as they can precisely maneuver a probe antenna over a surface in the DUT’s near-field region. In this work, the designed multi-rotor UAV enables electromagnetically optimal placement of the probe antenna and effective gravity center re-balacing by payload position adjustment. The on-board software-defined radio (SDR) serves as a dual-channel receiver for the dual-polarized probe and as a signal source. For phase-coherent measurements, the source signal is transmitted to the DUT via an optical fiber tethering the UAV to the ground control station. Power supply cables allow unlimited flight times. A laser-based tracking system originating from virtual reality (VR) applications measures the probe position and orientation. The developed operator software guides the UAV along a trajectory and records the irregularly distributed near-field samples. An inverse equivalent sources solver (IESS) with full probe correction computes equivalent sources for antenna diagnostics and the DUT far field. The deployed components make the system very cost-effective. Still, verification measurements demonstrate its usability and the accuracy of the results for frequencies up to several GHz.

Amid the recent advances in robotics and machine learning, unmanned aerial vehicles (UAVs) have shown evident proliferation across various applications. Consequently, the involvement of UAVs in populated environments has progressively become inevitable, putting forward stringent safety and security measures. In this work, we develop a deep reinforcement learning-based UAV-navigation approach that blends decision making with behavioral intelligence. In particular, a reinforcement learning (RL) agent is trained to instruct the UAV on how to accomplish a goal-oriented task, while assuring the safety of the UAV and its surroundings. Upon arriving at the goal position, the RL agent slows the UAV down preparing it for landing. The safety of the UAV and the environment are attained through a robust collision avoidance capability, embedded into the RL-based navigation system and considers both static and dynamic obstacles in the environment. Training is exclusively carried out in simulation, where a high fidelity UAV controller model is used to perform the simulated maneuver. The proposed approach was tested in simulation and then shown to directly transfer to reality without explicit sim2real gap transfer techniques. Experimental results demonstrated the agent’s capability to achieve the navigation task with 90% success rate.

Wideband Audio Waveform Evaluation Networks (WAWEnets) are convolutional neural networks that operate directly on wideband audio waveforms in order to produce evaluations of those waveforms. In the present work these evaluations give qualities of telecommunications speech (e.g., noisiness, intelligibility, overall speech quality). WAWEnets are no-reference networks because they do not require “reference” (original or undistorted) versions of the waveforms they evaluate. Our initial WAWEnet publication introduced four WAWEnets and each emulated the output of an established full-reference speech quality or intelligibility estimation algorithm. We have updated the WAWEnet architecture to be more efficient and effective. Here we present a single WAWEnet that closely tracks seven different quality and intelligibility values. We create a second network that additionally tracks four subjective speech quality dimensions. We offer a third network that focuses on just subjective quality scores and achieves very high levels of agreement. This work has leveraged 334 hours of speech in 13 languages, over two million full-reference target values and over 93,000 subjective mean opinion scores. We also interpret the operation of WAWEnets and identify the key to their operation using the language of signal processing: ReLUs strategically move spectral information from non-DC components into the DC component. The DC values of 96 output signals define a vector in a 96-D latent space and this vector is then mapped to a quality or intelligibility value for the input waveform.

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.