Events for July 29, 2026

Wireless power transfer has emerged as a transformative technology. Traditionally, biomedical devices use batteries as a power source. Therefore, every battery replacement requires surgery. The concept of seamlessly delivering power within the human body through implanted coils and antennas has opened a new frontier in healthcare, enabling the development of innovative medical devices and <a href="http://systems.Some" target="_blank" title="systems.Some">systems.Some applications of implanted wireless power transfer technology include implantable medical sensors, monitoring devices, drug delivery systems, and neurostimulators. The implanted coils and antennas ensure these medical devices function optimally without the need for invasive procedures for battery replacement or <a href="http://recharging.When" target="_blank" title="recharging.When">recharging.When it comes to wireless power transfer, there are two major methods used in implanted devices. One is magnetic field coupling, which uses coils, and the second method involves electromagnetic waves transferred through antennas. The development of these components demands careful consideration of factors such as miniaturization, biocompatibility, and efficient power transfer over varying distances and orientations within the human body. Magnetic coupling offers high power transfer efficiency but is limited by the depth at which power can be effectively transferred. Beyond a certain depth, efficiency drops significantly. Radiative power transfer via electromagnetic waves can transfer power over larger distances, but its efficiency may become very <a href="http://low.An" target="_blank" title="low.An">low.An interesting research topic is how to take advantage of both methods to extend the range of power transfer while optimizing power transfer efficiency. Another primary focus in this field is the development of safe and efficient systems that comply with safety regulations. Particularly, the regulated levels of exposure in terms of Specific Absorption Rate (SAR) are critical considerations in the design and implementation of these <a href="http://technologies.To" target="_blank" title="technologies.To">technologies.To improve power transfer efficiency, careful modeling and simulation of these devices is essential, as well as rigorous testing in phantom and laboratory environments. This talk aims to explore some of these topics, considering the significance, challenges, and potential of wireless power transfer technology for implanted <a href="http://devices.Speaker(s):" target="_blank" title="devices.Speaker(s):">devices.Speaker(s): Dr. Sima NoghanianRoom: B-600.16, Bldg: Pavillion Principal, Galerie Rolland (B-600.16), 2500 Chem. de Polytechnique, Montréal, Quebec, Canada, H3T 1J4, Virtual: https://events.vtools.ieee.org/m/560674

Undoubtedly, a question that resonates with every Ph.D. student is the career choice between academia and industry. There are multiple career options ahead of any graduate student. Some of them are traditional jobs such as becoming a professor or an engineer, and some may be different. Regardless of the chosen trajectory, certain fundamental skills are key to success and resilience in any career. While specific skills vary according to the job. In this presentation, I will draw from personal experience and offer insights into this critical decision-making process. By sharing personal anecdotes and lessons, it is aimed to shed light on the symbolic relationship between academia and industry, illustrating how navigating between these two encourages innovation, enriches professional growth, and shapes a dynamic, multi-faceted career <a href="http://trajectory.Speaker(s):" target="_blank" title="trajectory.Speaker(s):">trajectory.Speaker(s): Dr. Sima NoghanianRoom: B-600.16, Bldg: Pavillion Principal, Galerie Rolland (B-600.16), 2500 Chem. de Polytechnique, Montréal, Quebec, Canada, H3T 1J4, Virtual: https://events.vtools.ieee.org/m/560678

IEEE-Mentors/Mentees WebinarIEEE Jamaica Section <a href="http://Excom.All" target="_blank" title="Excom.All">Excom.All Affinity group/YP chairsAll chapter chairsCo-sponsored by: IEEE ComSoc Jamaica ChapterSpeaker(s): Balvin, ProfessorVirtual: https://events.vtools.ieee.org/m/568402

IEEE-Mentors/Mentees WebinarIEEE Jamaica Section <a href="http://Excom.All" target="_blank" title="Excom.All">Excom.All Affinity group/YP chairsAll chapter chairsCo-sponsored by: IEEE ComSoc Jamaica ChapterSpeaker(s): Dr. Balvin Thorpe, ProfessorVirtual: https://events.vtools.ieee.org/m/568403

Reliable phoneme classification is fundamental to intelligent hearing-aid systems because it enables adaptive amplification, environmental noise suppression, and speech enhancement, thereby improving speech intelligibility for individuals with hearing impairment. Previous studies have shown that Mel Frequency Cepstral Coefficients (MFCCs) combined with non-parametric k-Nearest Neighbors (k-NN) classifiers can achieve competitive speech classification performance with relatively low computational complexity. However, recognition accuracy decreases for short-duration phonemes, and limited training data can restrict model generalization. To overcome these limitations, a hybrid speech classification architecture was developed by combining MFCC-based acoustic feature extraction, deep feature representation using a Convolutional Neural Network (CNN), and weighted k-Nearest Neighbour (k-NN) decision making. The proposed methodology was implemented within the MATLAB environment and experimentally validated using the TIMIT Acoustic–Phonetic Continuous Speech Corpus. System performance was quantified through recall, classification accuracy, F1 score, precision, confusion matrix evaluation, Receiver Operating Characteristic (ROC) analysis, and computational efficiency. The experimental evaluation yielded an overall phoneme recognition accuracy of 96.4%, while achieving 82.8% accuracy for short- duration phonemes, demonstrating the effectiveness of the proposed hybrid learning strategy. It outperformed conventional HMM, SVM, ANN, standalone CNN, and MFCC- based k-NN classifiers while maintaining computational efficiency suitable for embedded implementation. The experimental findings indicate that the proposed hybrid framework substantially improves phoneme discrimination and establishes a scalable, computationally efficient architecture capable of supporting future real-time intelligent hearing-aid <a href="http://applications.Co-sponsored" target="_blank" title="applications.Co-sponsored">applications.Co-sponsored by: Dr. Christopher UDEAGHASpeaker(s): Dr. BalvinOwen Thorpe, Virtual: https://events.vtools.ieee.org/m/568541