In this paper, we analyze the low-SNR behavior of the cross-receiver mutual information (CMI) between two received signals corrupted by uncorrelated, additive Gaussian noise. This framework has use in distributed, passive sensor applications, such as passive radar and collaborative opportunistic navigation. For Gaussian and BPSK signaling, the CMI can be expressed in terms of the effective SNR between the receivers. On-off keying (OOK), while not optimal in terms of spectral efficiency for a single-receiver channel, is shown to have greater CMI than Gaussian or BPSK signaling. This is in spite of the fact that, given the same received SNRs, all three source distributions have the same linear correlation coefficient. This indicates that for OOK sources, effective SNR and correlation coefficient are not meaningful descriptors for passive receivers. Full-length version of conference paper submission.

The availability of low-cost consumer camera device that use standardCMOS sensor technology enabled us to investigate the possibility of usingsuch device to detect ionizing radiations. A CMOS with active-pixel sensorfrom a webcam with built-in electronics was evaluated in this early prelim-inary works for the detection ofγphotons from137Cs source. A computerprogram was written in Python utilizing the openCV library to record thedigitized data from the webcam. The CMOS sensor with a simple systemsetup was found able to detect theγphotons.

This document describes how to use discrete time Laguerre basis functions, and their associated z-transforms, to construct a system identification process using experimentally determined Laguerre expansion coefficients of the input and output sequences. The process is derived using a new Product Propert} for the discrete time Laguerre basis functions. The system identification process is “linear in the parameters”; it does not require assumptions/knowledge of the poles locations of the system under test. An example is presented using data generated by a system that has appeared in the recent literature. The procedure naturally produces equations that can be used to determine if the chosen model order is correct or if its order needs to be increased. Constraints can easily be incorporated.

The general diode equation or the non-ideal diode equation is the foundation of circuit models of active devices for the past several decades. Apart from the effect of p-n junction, this equation also accounts for the series bulk resistance of a diode. Despite a reasonable agreement of the equation with measured IV characteristics, it is shown here that the equation is incompatible with basic theories of circuits and systems. Therefore, a modification in the equation is proposed to remove this incompatibility. This modified equation leads to a compact model of a p-n junction diode that has an excellent agreement with the measured IV characteristics.

Position sensitive CdZnTe Compton imaging cameras are currently being studied for their use of proton beam range verification for radiotherapy applications. This work presents the use of an experimental large volume CdZnTe detector for the detection of prompt gamma rays that are emitted from proton-nuclei interaction within plastic (C2H4) targets. Two experiments were conducted where the incident angle and the dose profile of the beam were varied. The energy spectra from these experiments show that the angle at which the beam enters the target can influence the photopeak to Compton continuum ratios, resulting in more than 18% increase at 718 keV when the beam is parallel to the detector. Images of the 718 keV and 4.44 MeV characteristic prompt gamma ray emission from carbon-proton interactions are reconstructed using list-mode maximum likelihood expectation maximization (MLEM). Images from these prompt gamma emissions line up well with the expected location of the proton beam within the plastic targets.

The deployment of mobile-Small cells (mScs) is widely adopted to intensify the quality-of-service (QoS) in high mobility vehicles. However, the rapidly varying interference patterns among densely deployed mScs make the resource allocation (RA) highly challenging. In such scenarios, RA problem needs to be solved nearly in real-time, which can be considered as drawback for most existing RA algorithms. To overcome this constraint and solve the RA problem efficiently, we use deep learning (DL) in this work due to its ability to leverage the historical data in RA problem and to deal with computationally expensive tasks offline. More specifically, this paper considers the RA problem in vehicular environment comprising of city buses, where DL is explored for optimization of network performance. Simulation results reveal that RA in a network using Long Short-Term Memory (LSTM) algorithm outperforms other machine learning (ML) and DL-based RA mechanisms. Moreover, RA using LSTM provides less accurate results as compared to existing Time Interval Dependent Interference Graph (TIDIG)-based, and Threshold Percentage Dependent Interference Graph (TPDIG)-based RA but shows improved results when compared to RA using Global Positioning System Dependent Interference Graph (GPSDIG). However, the proposed scheme is computationally less expensive in comparison with TIDIG and TPDIG-based algorithms.

Generalized zero-shot learning (GZSL) is one of the most realistic problems, but also one of the most challenging problems due to the partiality of the classifier to supervised classes. Instance-borrowing methods and synthesizing methods solve this problem to some extent with the help of testing semantics, but therefore neither can be used under the class-inductive instance-inductive (CIII) training setting where testing data are not available, and the latter require the training process of a classifier after generating examples. In contrast, a novel method called Semantic Borrowing for improving GZSL methods with compatibility metric learning under CIII is proposed in this paper. It borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unseen or unknown classes would not be available for training while this approach does NOT need any information of semantics of unseen or unknown classes. The experimental results on representative GZSL benchmark datasets show that it can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification.

This paper is about the derivation of a jump-diffusion model of a microgrid operating between standalone and grid-tied modes.

Our simulation data is generated from energy vector using ED technique.

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale distribution system of 11,000 nodes, the DERMS software controller, and 90 power hardware photovoltaics (PVs) and battery inverters. The applied DERMS algorithm is designed based on a realtime optimal power flow algorithm and implemented with acceleration design that performs fast dispatch of simulated PVs and real physical hardware DER devices every 4 seconds.

This research presents a method for developing electric power system models that are calibrated using synchrophasor measurements and are suitable for power system planning studies.

The max-cut problem addresses the problem of finding a cut for a graph that splits the graph into two subsets of vertices so that the number of edges between these two subsets is as large as possible. However, this problem is NP-Hard, which may be solved by suboptimal algorithms. In this paper, we propose a fast and accurate Riemannian optimization algorithm for solving the max-cut problem. To do so, we develop a gradient descent algorithm and prove its convergence. Our simulation results show that the proposed method is extremely efficient on some already-investigated graphs. Specifically, our method is on average 50 times faster than the best well-known techniques with slightly losing the performance, which is on average 0.9729 of the max-cut value of the others.

This paper addresses the challenge of sustaining the intrusion detection effectiveness of machine learning-based intrusion detection systems in the Internet of Things (IoT) in the presence of concept and data drifts. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To address data drifts, we first propose a series of data preparation steps that help improve the quality of the data and avoid inconsistencies. To counter concept drifts, we capitalize on an online deep neural network model that relies on an ensemble of varying depth neural networks that cooperate and compete together to enable the model to steadily learn and adapt as new data come, thus allowing for stable and long-lasting learning. Experiments conducted on a real-world IoT-based intrusion detection dataset, designed to address concept and data drifts, suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static deep neural network model, which is widely used for intrusion detection.

We have compared the data of three clocks A, B and D moving in relative uniform motion with relative speed/velocity between A and B set at 0.6c, relative speed/velocity between A and D set at 0.8c and relative speed between B and D set at (5c/13) = 0.3846c as per the velocity addition formula (0.8-0.6)/(1-0.8*0.6). We have compared the time readings of the clocks when they meet at three events. Event 1 meeting of A and B, Event 2 meeting of A and D, Event 3 meeting of B and D.

Steerable directional antennas are increasingly utilised to improve the overall performance of the traditional wireless sensor networks. Steerable directional antenna based networking solutions increase the network capacity by providing a longer range of transmission and reduced interference as compared to networking solutions with omni-directional antennas. However, the use of smart antennas requires complex algorithms and such algorithms may not be easily leveraged in low power Internet of Things (IoT) networks. This study presents mechanisms for integrating low complexity smart antenna solutions into IETF 6TiSCH protocol with the aim of creating scalable and reliable industrial IoT networks. The solution defines extensions to MAC layer and scheduling mechanisms of IETF 6TiSCH protocol to enable its seamless integration with low complexity steerable smart antennas. The results of this study show that smart antenna enabled 6TiSCH protocol stack outperforms the legacy 6TiSCH stack in terms of data delivery performance especially in high density scenarios.

In power systems dynamic analysis, one of the well-known used toolboxes is Power System Analysis Toolbox (PSAT). In order to easy cooperate with this toolbox, custom codes are required to do complicated optimization computations automatically and without much effort by the user. In this article, a simple code was proposed for this aim. It can be a base technique to write user defined optimization programs to be run fast and reliable without difficulties of handy manipulation of numbers by the user, especially for Small Signal Stability Constrained Optimal Power Flow (SSSC-OPF) optimizations.

Hyperspectral imaging (HSI) provides much more information than regular optical microscopy and spectroscopy, making it valuable in areas such as biomedicine, materials inspection and food safety. However HSI is challenging because of the large amount of data that has to be acquired, and large measurement times. Compressed sensing (CS) approaches to hyperspectral imaging have been developed to address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types objects or scenes. Here, we develop improved compressed sensing approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. This is an augmentation of single-pixel-camera-style acquisition for HSI, where a single spectrum is measured per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing, and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased, while maintaining reconstruction speed as well as accuracy. The methods are validated computationally, via noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ~10 times smaller measurement plus reconstruction time as compared to full sampling HSI, without compromise to reconstruction accuracies across the different sample images tested. Multitrack non-adaptive CS (sparse recovery) suffers a large reconstruction time, but is the most robust to Poisson noise.

This paper introduces Euclidean distance filter as a new method for image filtering. It discuses conceptual aspects of the filter and implements examples to illustrate it's application.