Speech COVID-19 detection systems have gained popularity as they represent an easy-to-use and low-cost solution that is well suited for at-home long-term monitoring of patients with persistent symptoms. Recently, however, the limited generalization capability of existing deep neural network based systems to unseen datasets has been raised as a serious concern, as has their limited interpretability. In this paper, we propose two innovations to help overcome these issues. First, we propose the use of a 3-dimensional modulation frequency tensor (called modulation tensorgram representation, MTR) as input to a convolutional recurrent neural network for COVID-19 detection. The representation is known to provide robustness against different environmental factors seen across datasets. Next, we propose the use of spectro-temporal saliency masking to aggregate regions of the MTR related to COVID-19, thus helping further improve the generalizability and interpretability of the model. Experiments are conducted on three public datasets and results show the proposed solution consistently outperforming two benchmark systems in within-, across-, and unseen-dataset tests. The proposed method relies on a similar number of parameters to the benchmark, thus a promising solution for at-home monitoring of COVID-19 infection.

In this article, I/We report a novel eye-movement assessment method using a digital camera to measure eye conjugacy in healthy individuals while performing a neurological examination. This is clinically significant because this approach overcomes the limitations of complex and expensive setups (e.g., infrared cameras) that often make it impractical to scale up and translate to clinical use. Moreover, this approach removes the need for a calibration procedure which has caused prior studies to exclude participants, potentially introducing selection bias and limiting generalizability. Our study suggests that this technology could be deployed for clinical use in the clinic or pre-hospital setting, including telemedicine or emergency medical services (EMS) encounters to detect neurological injury or diseases that cause neuro-ocular deficits, like stroke.

This paper presents a chance-constrained rollover-free manipulation planning method for robotic arms under payload mass uncertainty. The corresponding motion planning problem is stated as a chance-constrained nonlinear optimal control problem (NOCP) subject to kinematics and rollover stability constraints. The latter takes the form of a chance constraint that ensures a certain probability of the robot maintaining dynamic rollover stability in the presence of payload mass uncertainty. To achieve efficient solutions to the NOCP, a novel geometric bound for the stability region is derived. The novel bound is then utilized to modify the rollover-stability constraint. To showcase its benefit, comparisons between the proposed bound of probabilistic rollover stability measure and the naive noise model are provided through statistical analysis. The formulation’s practicality is demonstrated through experiments with a Kinova Jaco 2 arm mounted on a free-to-roll platform. Results demonstrate greater robustness of the robot’s motion plan to mass uncertainty and computational efficiency of the trajectory generation.Â

Engineering education is constantly evolving to keep up with the latest technological developments and meet the changing needs of the engineering industry. One promising development in this field is the use of generative artificial intelligence technology, such as the ChatGPT conversational agent. ChatGPT has the potential to offer personalized and effective learning experiences by providing students with customized feedback and explanations, as well as creating realistic virtual simulations for hands-on learning. However, it is important to also consider the limitations of this technology. ChatGPT and other generative AI systems are only as good as their training data and may perpetuate biases or even generate and spread misinformation. Additionally, the use of generative AI in education raises ethical concerns such as the potential for unethical or dishonest use by students and the potential unemployment of humans who are made redundant by technology. While the current state of generative AI technology represented by ChatGPT is impressive but flawed, it is only a preview of what is to come. It is important for engineering educators to understand the implications of this technology and study how to adapt the engineering education ecosystem to ensure that the next generation of engineers can take advantage of the benefits offered by generative AI while minimizing any negative consequences.

This is a three-part series, where the numerical  discrete-time analysis and comparative analysis of 72 Ah,Lithium Ion Battery (LIB) is presented. The numerical analysis has been explained in detailed with two non-linear Kalman filter varients, namely Unscented Kalman Filter(UKF) and Extended Kalman Filter(EKF), both in combination with event based Kalman Filter algorithm. The discrete-time state equations have been developed for 8RC equivalent circuit model, to model any variant of LIB, irrespective of recommended application type. LIB has been modeled with 8 parallel RC branches with also taking into account various non-linear parameters,thermal(modeled with thermal mass, convection, ambient temperature variation),battery charge-disharge cycle degration effect and aging. The comparative results have been tabulated from 1 RC to 8 RC and compared with experimentally measured. 400 cells are arranged to give 72 Ah, where each cell has rating of 6.66 Wh. In the first part, the UKF algoritm along with event-based kalman filter has been explained with the derivation of equations.I m not completing research paper, the earlier version got lost in computer crash. One part is survived, uploaded it.I will not re-write or submit paper as left R&D.

Classification, segmentation, and the identification of the infection region in MRI images of brain tumors are labor-intensive and iterative processes. Numerous anatomical structures of the human body may be envisioned using an image processing theory. With basic imaging methods, it is challenging to see the aberrant human brain’s structure. The neurological structure of the human brain may be distinguished and made clearer using the magnetic resonance imaging technique. The MRI approach uses a number of imaging techniques to evaluate and record the human brain’s interior features. In this study, we focused on strategies for noise removal, gray-level co-occurrence matrix (GLCM) extraction of features, and segmentation of brain tumor regions based on Discrete Wavelet Transform (DWT) to minimize complexity and enhance performance. In turn, this reduces any noise that could have been left over after segmentation due to morphological filtering. Brain MRI scans were utilized to test the accuracy of the classification and the location of the tumor using probabilistic neural network classifiers. The classifier’s accuracy and position detection were tested using MRI brain imaging. The efficiency of the suggested approach is demonstrated by experimental findings, which showed that normal and diseased tissues could be distinguished from one another from brain MRI scans with about 100% accuracy.

By extending metric space to expression in hash of directed graph, spatiotemporal observation could be described in matrix quantized state. (Working paper, more contents would be added soon)

Concerning the Perspective 3-Point (P3P) Problem, Grunert’s system of three quadratic equations has a repeated solution if and only if the cubic polynomial introduced by Finsterwalder has a repeated root. This polynomial is here shown to be obtainable from a particularly simple cubic polynomial with complex coefficients via a simple Möbius transformation. This provides surprising geometric insight into the P3P problem. In particular, (1) the discriminant of Finsterwalder’s polynomial can be written using the formula for the standard deltoid curve, and (2) this discriminant vanishes on a surface that approaches a deltoid shape when the camera is moved infinitely far from the control points in a direction perpendicular to the control points plane (the “limit case”). These two facts have been previously reported, but obscure reasoning was required to establish them. In contrast, the present article uses the newly discovered cubic polynomial to easily produce the first fact, which then provides a basis for better understanding the second fact. A detailed geometric description of the P3P solution points in the “limit case” is also provided.

This paper investigates covert communication in simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) systems, assuming for the sake of being more practical that the successive interference cancellation (SIC) is defective. To assess the covertness performance of the considered system, we first derive closed-form expressions for the detection error probability, the optimal detection threshold and the average minimum detection error probability of the warden, as well as the outage probability of the NOMA user pair. Then an optimization problem to maximize the effective covert rate (ECR) is proposed. According to the results of the simulation, it is possible to increase the maximum ECR of the considered system by varying the amount of components of the STAR-RIS.

The last decade witnessed the proliferation of automated content analysis in communication research. However, existing computational tools have been taken up unevenly, with powerful deep learning algorithms such as transformers rarely applied as compared to lexicon-based dictionaries. To enable social scientists to adopt modern computational methods for valid and reliable sentiment analysis of English text, we propose an open and free web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates diverse tools and offers validation metrics, empowering users with limited computational knowledge and resources to reap the benefit of state-of-the-art computational methods. Two cases demonstrate the functionality and usability of TLSA. The performance of different tools varied to a large extent based on the dataset, supporting the importance of validating various sentiment tools in a specific context.

Interference based microwave photonic bandpass filters (MPBF) form an important component of modern RF signal processing. The presence of multiple optical components in an RF photonic system e.g. fiber amplifiers, waveguides, cause a degradation in the MPBF profile and the out-of-band rejection due to the dispersion induced phase shift. Here, we compare the performance, using simulations and experiments, of two interference based MPBF configurations: (i) double-sideband phase modulation (DSB-PM) and (ii) double-sideband intensity modulation (DSB-IM), which exploit stimulated Brillouin scattering (SBS) for sideband processing, under different dispersion conditions. While the profile and out-of-band rejection for the DSB-PM based MPBF degrade drastically under the effect of dispersion, DSB-IM based MPBF achieves an out-of-band rejection of $>$ 40 dB and maintain the 3 dB and 20 dB bandwidths of 16.7 $\pm$ 0.4 MHz and 104.5 $\pm$ 5.4 MHz, respectively, under different dispersion conditions. The DSB-IM based MPBF exploits the bias dependent phase of the modulation sidebands of a z-cut intensity modulator to mitigate the dispersion induced impairment. The use of an intensity modulator for dispersion compensation makes our approach dynamic and compatible with integrated microwave photonic systems.

In this paper, we study the outage and ergodic rate performance of a rate-splitting multiple access (RSMA)-based cognitive radio (CR) system, where the secondary transmitter aims to communicate with two RSMA users. Imperfect successive interference cancellation (ipSIC) and channel estimation errors (CEEs) are considered in the proposed analysis. Analytical expressions for the outage probability (OP) and the ergodic rate (ER) are calculated. For a deeper understanding, the asymptotic behavior of OP and ER at high signal-to-noise ratio (SNR) regimes are carried out. Illustrative simulation results are presented and reveal that: i) The OP decreases and the ER gradually increases with the increase of SNR, and eventually approaching a constant; ii) ipSIC and CEEs have a negative impact on the considered system; iii) The outage and ergodic rate performance of RSMA-based CR network outperforms CRNOMA network due to flexible power allocation between common and private messages; iv) The OP first decreases and then increases with the power allocated to the common message, and there exists an optimal power allocation factor to ensure the reliable performance.

Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360° video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360° video. In this research, we propose a novel approach for 360- degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2’s stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. Our BBAG method is proven to improve QoE from 3.56% to 93.71% and the average viewport bitrate from 1.78% to 80.49% compared to state-of-the-art methods.

The emerging cybersecurity industry in Canada is not seen as anywhere near the scale of its American cyber-industrial complex counterpart, with limited-scale projects occurring to improve security sensor technology for institutions. However, due to international trade increasingly occurring through cyberspace, it is expected that the cyber industry will grow in Canada through both international and domestic partnerships, with Canadian critical infrastructure partners requiring greater security controls in order to ensure the confidentiality, integrity and availability of their data and operations.

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to mitigate fading and improve spectral efficiency. In a cooperative scenario, the performance of the system is improved by selecting the best relay for data transmission among a group of available relays. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that improves the overall achievable performance of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), in the present work we model a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and propose two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, and they reduce intercept probability by approximately 15\% and 30\%, respectively, compared to the ARMA approach.

Abstract— This work-in-progress presents a compact toolkit for reproducing the training of T5-based multiinstruments and multi-track transcription models on multiple MIR tasks. We plan to release our code in early 2023 at https://github.com/mimbres/YourMT3.

In this paper, a computationally efficient chance-constrained rollover-free motion planning method is presented. Specifically, the method is developed to plan motions for reconfigurable vehicles with the knowledge of a 3-D terrain model that has limited accuracy. The overall motion planning problem is formulated as a nonlinear optimal control problem (NOCP) that employs a constraint in the form of a bound on the probability of rollover under terrain-induced vehicle orientation uncertainty. To increase the computational efficiency of the NOCP, a geometric interpretation of the chance constraint is derived based on the characteristics of SO(3), the 3-D rotation group. Monte Carlo simulations are provided to demonstrate the usefulness of the geometric interpretation through comparisons with other methods. Experimental data gathered from driving a mobile robot through real forests are also used to validate the proposed model. Finally, path and trajectory generation results obtained with the proposed planning method for a feller-buncher machine traversing through uncertain 3-D terrain are presented to showcase the method’s overall performance and efficiency.

A document by Taissir Elganimi . Click on the document to view its contents.