Electrical and Electronic Engineering - Research Publications

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    Maximizing Sum-Rate via Relay Selection and Power Control in Dual-Hop Networks
    Dayarathna, S ; Senanayake, R ; Evans, J (IEEE, 2022-01-01)
    In this paper, we focus on the sum-rate optimization problem in a general dual-hop relay network by considering the joint relay selection and power control in the presence of interference. First, we propose a new relay selection algorithm which has better sum-rate performance than the existing relay selection techniques. Then we combine relay selection and power control to propose a novel iterative algorithm based on the tight lower bound approximation which maximizes the achievable sum-rate. We also prove that for the special case of two-user networks, binary power allocation is optimum for at least two transmitting nodes. Extensive numerical examples are used to compare the performance of the proposed algorithm and to illustrate the accuracy of the analysis.
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    Sum-Rate Optimization in Flexible Half-Duplex Networks with Transmitter/Receiver Scheduling
    Dayarathna, S ; Senanayake, R ; Evans, J (Institute of Electrical and Electronics Engineers (IEEE), 2021-01-01)
    In this paper, we focus on the problem of transmitter and receiver scheduling to maximize the achievable sum-rate of a flexible half-duplex network where nodes have the flexibility to either transmit, receive or be silent in a given time slot. We consider a network with multiple transmitters and receivers where each transmitter has specific information it needs to send to a set of receiving nodes. First, we conduct some structural analysis and show that the achievable sum-rate is maximized when each transmitter only transmits to a single receiver at a given time. Next, we consider one instance of the flexible network and by reducing the symmetric multiple receiver network to a single receiver network, we also show that the achievable sum-rate is maximized when either one transmitter or all the transmitters transmit. In fact, there exists a unique received signal-to-noise ratio at which the optimality changes from all-to-one. Finally, we design a novel low-cost algorithm that gives a sub-optimal solution to the achievable sum-rate maximization problem in a flexible half-duplex network. We also provide a comprehensive comparison of the proposed algorithm with respect to existing resource allocation techniques, and observe that our proposed algorithm provides significant sum-rate gains.
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    Genetic optimization of mid-infrared filters for a machine learning chemical classifier
    Tan, H ; Cadusch, JJ ; Meng, J ; Crozier, KB (Optica Publishing Group, 2022-05-23)
    Miniaturized mid-infrared spectrometers present opportunities for applications that range from health monitoring to agriculture. One approach combines arrays of spectral filters with infrared photodetectors, called filter-array detector-array (FADA) microspectrometers. A paper recently reported a FADA microspectrometer in tandem with machine learning for chemical identification. In that work, a FADA microspectrometer with 20 filters was assembled and tested. The filters were band-pass, or band-stop designs that evenly spanned the microspectrometer’s operating wavelength range. However, given that a machine learning classifier can be trained on an arbitrary filter basis, it is not apparent that evenly spaced filters are optimal. Here, through simulations with noise, we use a genetic algorithm to optimize six bandpass filters to best identify liquid and gaseous chemicals. We report that the classifiers trained with the optimized filter sets outperform those trained with evenly spaced filter sets and those handpicked to target the absorption bands of the chemicals investigated.
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    Investigation on repetition-coding and space-time-block-coding for indoor optical wireless communications employing beam shaping based on orbital angular momentum modes
    Li, J ; Yang, Q ; Dai, X ; Lim, C ; Nirmalathas, A (Optica Publishing Group, 2022-06-06)
    In this paper, we propose a novel beam shaping technique based on orbital angular momentum (OAM) modes for indoor optical wireless communications (OWC). Furthermore, we investigate two spatial diversity techniques, namely repetition-coding (RC) and Alamouti-type orthogonal space-time-block-coding (STBC) for indoor OWC employing the new beam shaping technique. The performance of both diversity schemes is systematically analyzed and compared under different beam shaping techniques using different OAM modes with different power ratios of the modes. It is shown that both RC and STBC can improve the system performance and effective coverage and RC outperforms STBC in all the beam shaping techniques regardless of the power ratios of the different modes. In addition, to further understand the performance of RC and STBC schemes against the signal delays induced during OAM mode conversion, the system tolerance of the two schemes to the delay interval is investigated with different OAM mode-based beam shaping techniques. Numerical results show that higher resistance to the delay interval can be achieved in STBC scheme. The advantage is more obvious when employing OAM0 and OAM1 based beam shaping technique.
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    Deep-learning-enabled high-performance full-field direct detection with dispersion diversity
    Li, X ; An, S ; Ji, H ; Li, J ; Shieh, W ; Su, Y (Optica Publishing Group, 2022-03-28)
    Data center interconnects require cost-effective photonic integrated optical transceivers to meet the ever-increasing capacity demands. Compared with a coherent transmission system, a complex-valued double-sideband (CV-DSB) direct detection (DD) system can minimize the cost of the photonic circuit, since it replaces two stable narrow-linewidth lasers with only a low-cost un-cooled laser in the transmitter while maintaining a similar spectral efficiency. In the carrier-assisted DD system, the carrier power accounts for a large proportion of the total optical signal power. Reducing the carrier to signal power ratio (CSPR) can improve the information-bearing signal power and thus the achievable system performance. To date, the minimum required CSPR is ∼7 dB for all the reported CV-DSB DD systems having electrical bandwidths of approximately half of baud rates. In this paper, we propose a deep-learning-enabled DD (DLEDD) scheme to recover the full optical field of the transmitted signal at a low CSPR of 2 dB in experiment. Our proposal is based on a dispersion-diversity receiver with an electrical bandwidth of ∼61.0% baud rate and a high tolerance to laser wavelength drift. A deep convolutional neural network enables accurate signal recovery in the presence of a strong signal-signal beat interference. Compared with the conventional method, the proposed DLEDD scheme can reduce the optimum CSPR by ∼8 dB, leading to a significant signal-to-noise ratio improvement of ∼5.8 dB according to simulation results. We experimentally demonstrate the optical field reconstruction for a 28-GBaud 16-ary quadrature amplitude modulation signal after 80-km single-mode fiber transmission based on the proposed DLEDD scheme with a 2-dB optimum CSPR. The results show that the proposed DLEDD scheme could offer a high-performance solution for cost-sensitive applications such as data center interconnects, metro networks, and mobile fronthaul systems.
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    Feature issue introduction: ultra-wideband optical communications.
    Zhuge, Q ; Chen, X ; Plant, DV ; Shieh, W (Optica Publishing Group, 2022-04-11)
    This Feature Issue covers the important aspects to develop ultra-wideband optical communication systems including optoelectronics, impairment modeling and compensation, optical amplification, superchannel and multi-band transmission and control, and so forth. This Introduction provides a summary of the articles on these topics in this Feature Issue.
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    Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors
    Datta, S ; Karmakar, CK ; Yan, B ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-06-01)
    Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, their normalized difference CSDR and the descriptors Complex Cross-Correlation Measure ( C3M) and Activity Asymmetry Index ( AAI) can distinguish between mild, moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.
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    SHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMBMOTION
    Datta, S ; Karmakar, C ; Rathore, P ; Palaniswami, M (IEEE, 2021-01-01)
    The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.
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    Microgrids Against Wildfires: Distributed Energy Resources Enhance System Resilience
    Moreno, R ; Trakas, DN ; Jamieson, M ; Panteli, M ; Mancarella, P ; Strbac, G ; Marnay, C ; Hatziargyriou, N (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-01-01)
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    ASYMPTOTICS OF EIGENSTRUCTURE OF SAMPLE CORRELATION MATRICES FOR HIGH-DIMENSIONAL SPIKED MODELS
    Morales-Jimenez, D ; Johnstone, IM ; McKay, MR ; Yang, J (STATISTICA SINICA, 2021-01-01)
    Sample correlation matrices are widely used, but for high-dimensional data little is known about their spectral properties beyond "null models", which assume the data have independent coordinates. In the class of spiked models, we apply random matrix theory to derive asymptotic first-order and distributional results for both leading eigenvalues and eigenvectors of sample correlation matrices, assuming a high-dimensional regime in which the ratio p/n, of number of variables p to sample size n, converges to a positive constant. While the first-order spectral properties of sample correlation matrices match those of sample covariance matrices, their asymptotic distributions can differ significantly. Indeed, the correlation-based fluctuations of both sample eigenvalues and eigenvectors are often remarkably smaller than those of their sample covariance counterparts.