The College of Engineering and Computer Science: Recent submissions
Now showing items 41-60 of 143
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Joint communications and sensing design for multi-carrier MIMO systems
(2023)With the emergence of ultra-reliable and low latency communication (URLLC) services, link adaptation (LA) plays a pivotal role in improving the robustness and reliability of communication networks via appropriate modulation ... -
Joint communications and sensing design for multi-carrier MIMO systems
(2023)In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized for the whole frequency band, resulting in ... -
Joint communication and computation offloading for ultra-reliable and low-latency with multi-tier computing
(2023-02)In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency ... -
RIS-Assisted Full-Duplex Integrated Sensing and Communication
(2023-10)In this letter, we explore the application of recon- figurable intelligent surface (RIS) in the integrated sensing and communication network, where a full-duplex multi-antenna base station (BS) concurrently detects a target ... -
Intelligent traffic steering in beyond 5G open RAN based on LSTM traffic prediction
(2023-11)Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, namely enhanced mobile broadband ... -
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
(2023-04-15)Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based ... -
Intelligent blockchain-based edge computing via deep reinforcement learning: Solutions and challenges
(2022)The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things (IoT) networks, enabling task offloading with security enhancement based on ... -
Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion
(2022)Laser powder bed fusion (L-PBF) is one of the most widely used metal additive manufacturing technology for fabrication of functional and structural components. However, inconsistency in quality and reliability of L-PBF ... -
Asymmetric hashing for fast ranking via neural network measures
(2022-11-01)Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching ... -
Silicon surface lattice resonances and halide perovskite semiconductors for exciton-polaritons at room temperature
(2023-01-01)Owing to their high oscillator strength, binding energy, and low-cost fabrication, two-dimensional halide perovskites have recently gained attention as excellent materials for generating exciton-polaritons at room temperature. ... -
A novel approach for pill-prescription matching with GNN assistance and contrastive learning
(2022)Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. ... -
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
(2023)We propose for the first time a new strategy to train slice-level classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional ... -
Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling
(2022-01-31)Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires accurate classification of the phases. Current approaches typically utilize ... -
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
(2023)Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning ... -
Optical glucose sensors based on chitosan-capped ZnS-doped Mn nanomaterials
(2023-03-06)The primary goal of glucose sensing at the point of care is to identify glucose concentrations within the diabetes range. However, lower glucose levels also pose a severe health risk. In this paper, we propose quick, simple, ... -
Toward efcient and intelligent video analytics with visual privacy protection for large‐scale surveillance
(2021-05-15)Nowadays, the explosion of CCTV cameras has resulted in an increasing demand for distributed solutions to efficiently process the vast volume of video data. However, the use of surveillance, where people are being watched ... -
On the effect of isotropy on VAE representations of text
(2022-05)Injecting desired geometric properties into text representations has attracted a lot of attention. A property that has been argued for, due to its better utilisation of representation space, is isotropy. In parallel, VAEs ... -
Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks
(2022-10-19)Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing ... -
Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination
(2022-08-08)The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of ... -
Multi-stream fusion for class incremental learning in pill image classification
(2022)Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they ...
