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Contribution: This research presents a case study of an online peer assessment (PA) implementation in a random processes course. Students were required to submit simulation assignments, and then to evaluate their peers' submissions. The research explored the students' perception of the activity throughout the course using surveys, and their behavior and academic performance using data obtained in the course. Background: With decreased resources and larger class sizes, the obligatory theoretical random processes course in the electrical engineering program presents a challenge with regard to students' motivation and assimilation of the academic material. Intended Outcomes: In preceding research, PA was found to provide benefits for students' learning, without significantly burdening teaching staff resources. The implementation was directed at achieving these benefits. Application Design: Simulation exercises were designed to provide illustrations of relatively abstract material, and PA was instituted to facilitate better learning of the material without imposing greater resource requirements on the teaching staff. Findings: Results indicated that PA, although executed in accordance with the best practices presented in the literature, failed to achieve the benefits expected, and might even have contributed to lower grades in the course.

One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify slight variations in the consumed current under steady-state conditions. An experimental database of current consumption signals of two similar groups containing 20 same-model computer displays was collected. The resulting signals were classified using various state-of-the-art time-series classification (TSC) methods. We successfully identified 40 similar (same-model) electrical devices with about 94% precision, while most errors were concentrated in confusion between a small number of devices. A simplified empirical wavelet transform (EWT) paired with a linear discriminant analysis (LDA) classifier was shown to be the recommended classification method.

Two important aspects of digital audio forensics are integrity validation of the recorded contents and identification of the acquisition device that produced the recording. One of the effective methods for integrity validation is based on induced electrical network frequency (ENF) signal analysis. The goal of this paper is to extend the usage of the ENF signal for the identification of the acquisition device. The proposed identification method is based on the harmonic amplitude coefficients of ENF signals that are effectively applied as feature vectors. The evaluation results demonstrate the efficacy of the proposed approach., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Soft-decision multiple-pulse-position modulation (MPPM) is one of the favored free-space optical communication modulations. This paper derives theoretical expressions for the symbol error rate of both fast and slow fading conditions applied for soft-decision MPPM without channel state information at the receiver as a particular case of nonmemoryless channel conditions. The theoretical expressions for lognormal and gamma-gamma modeled turbulence were successfully verified by simulation.

Free-space optical (FSO) communication may be severely influenced by the inevitable turbulence effect that results in channel gain fluctuations and fading. The objective of this paper is to provide a simple and effective simulator of the weak-turbulence FSO channel that emulates the influence of the temporal covariance effect. Specifically, the proposed model is based on lognormal distributed samples with a corresponding correlation time. The simulator is based on the solution of the first-order stochastic differential equation (SDE). The results of the provided SDE analysis reveal its efficacy for turbulent channel modeling.

The orientation of remote objects can be easily measured optically and very accurately using optical technologies. In this paper, we address the accuracy of a roll angle estimation technique that is based on the estimation of the polarization of a retroreflected beam and is applied in a long-range measurement task. Over long transmission ranges, the optical power decreases significantly leading to severe SNR deterioration. As a result, the measurement accuracy decreases. In this study, the estimation is carried out using a maximum-likelihood estimator and its performance is evaluated using the analytical Cramer-Rao bound.