Electrical and Electronic Engineering - Research Publications

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    Frequency Permutation Subsets for Joint Radar and Communication
    Dayarathna, S ; Senanayake, R ; Smith, P ; Evans, J (Institute of Electrical and Electronics Engineers (IEEE), 2024-02-01)
    This paper focuses on waveform design for joint radar and communication systems and presents a new subset selection process to improve the communication error rate performance and global accuracy of radar sensing of the permutation based random stepped frequency radar waveform. An optimal communication receiver based on integer programming is proposed to handle any subset of permutations followed by a more efficient sub-optimal receiver based on the Hungarian algorithm. Considering optimal maximum likelihood detection, the block error rate is analyzed under both additive white Gaussian noise and correlated Rician fading. We propose two methods to select a permutation subset with an improved block error rate and an efficient encoding scheme to map the information symbols to selected permutations under these subsets. From the radar perspective, the ambiguity function is analyzed with regards to the local and the global accuracy of target detection. Furthermore, a subset selection method to reduce peak-to-sidelobe ratio (PSLR) is proposed by extending the properties of Costas arrays. Finally, the process of remapping the frequency tones to the symbol set used to generate permutations is introduced as a method to improve both the communication and radar performances of the selected permutation subset.
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    High Reliability Radar and Communications Based on Random Stepped Frequency Waveforms
    Dayarathna, S ; Senanayake, R ; Evans, J ; Smith, P (IEEE, 2023)
    This paper is on the waveform design of joint radar and communication systems. Focusing on permutation code based random stepped frequency waveforms, we present a new joint radar and communication system that has improved communication error rate performance when compared to existing approaches. More specifically, we propose a subset selection process to improve the Hamming distance between communication waveforms. An efficient encoding scheme is proposed to map the information symbols to selected permutations. Further, an optimal communication receiver based on integer programming followed by a more efficient sub-optimal receiver based on the Hungarian algorithm is also proposed. Considering the optimum maximum likelihood detection, the block error probability is analyzed under both additive white Gaussian noise channels and Rician fading channels. Finally, we discuss the radar performance under the new system and highlight that it has negligible effect on the radar local and global accuracy.
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    Bit Modulated Frequency Permutation Waveforms for Joint Communications and Radar
    Dayarathna, S ; Senanayake, R ; Evans, J ; Smith, P (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023-12)
    In this paper, we propose the selection of a subset of waveforms based on the random stepped frequency permutation waveform to support joint radar and communication. More specifically, we solve two critical implementation problems arising from the subset selection which is motivated by the fundamental bit level operation requirements of communication systems. Noting that the practicality of any selected subset depends on the feasibility of efficient implementation, we focus on finding a specific subset for which we can design an efficient mapping process and a receiver implementation. More specifically, we propose an efficient process to map information bits to waveforms based on the factorial number system. An efficient optimal communication receiver that utilizes the Hungarian algorithm is also designed. For additive white Gaussian noise and correlated Rician fading channels, the bit error rate is analyzed in accordance with the optimum maximum likelihood detection.
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    Display of Native Antigen on cDC1 That Have Spatial Access to Both T and B Cells Underlies Efficient Humoral Vaccination.
    Kato, Y ; Steiner, TM ; Park, H-Y ; Hitchcock, RO ; Zaid, A ; Hor, JL ; Devi, S ; Davey, GM ; Vremec, D ; Tullett, KM ; Tan, PS ; Ahmet, F ; Mueller, SN ; Alonso, S ; Tarlinton, DM ; Ploegh, HL ; Kaisho, T ; Beattie, L ; Manton, JH ; Fernandez-Ruiz, D ; Shortman, K ; Lahoud, MH ; Heath, WR ; Caminschi, I (American Association of Immunologists, 2020-10-01)
    Follicular dendritic cells and macrophages have been strongly implicated in presentation of native Ag to B cells. This property has also occasionally been attributed to conventional dendritic cells (cDC) but is generally masked by their essential role in T cell priming. cDC can be divided into two main subsets, cDC1 and cDC2, with recent evidence suggesting that cDC2 are primarily responsible for initiating B cell and T follicular helper responses. This conclusion is, however, at odds with evidence that targeting Ag to Clec9A (DNGR1), expressed by cDC1, induces strong humoral responses. In this study, we reveal that murine cDC1 interact extensively with B cells at the border of B cell follicles and, when Ag is targeted to Clec9A, can display native Ag for B cell activation. This leads to efficient induction of humoral immunity. Our findings indicate that surface display of native Ag on cDC with access to both T and B cells is key to efficient humoral vaccination.
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    Investigation of Gallium–Boron Spin‐On Codoping for poly-Si/SiOx Passivating Contacts
    Truong, TN ; Le, TT ; Yan, D ; Phang, SP ; Tebyetekerwa, M ; Young, M ; Al-Jassim, M ; Cuevas, A ; Macdonald, D ; Stuckelberger, J ; Nguyen, HT (Wiley, 2021-12)
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    Comparative Analysis of Power Ramp Rate Control Strategies for Photovoltaic Systems
    Yan, HW ; Liang, G ; Rodriguez, E ; Beniwal, N ; Farivar, GG ; Pou, J (IEEE, 2023-01-01)
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    Flexible Power Point Tracking Aided Power Ramp Rate Control for Photovoltaic Systems With Small Energy Storage Capacity
    Yan, HW ; Liang, G ; Beniwal, N ; Rodriguez, E ; Farivar, GG ; Pou, J (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-02)
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    Learning the Vector Coding of Egocentric Boundary Cells from Visual Data
    Lian, Y ; Williams, S ; Alexander, AS ; Hasselmo, ME ; Burkitt, AN (SOC NEUROSCIENCE, 2023-07-12)
    The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animal's point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. Furthermore, some egocentric boundary cells learnt by the model can still function in new environments without retraining. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas, including the grid cells in entorhinal cortex and place cells in the hippocampus.SIGNIFICANCE STATEMENT The computational model presented here demonstrates that the recently discovered egocentric boundary cells in retrosplenial cortex can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Additionally, our model generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This transformation between sensory input and egocentric representation in the navigational system could have implications for the way in which egocentric and allocentric representations interface in other brain areas.
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    Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis
    Zhang, H ; Quadeer, AA ; Mckay, MR (NATURE PORTFOLIO, 2023-11-17)
    Direct-acting antiviral agents (DAAs) provide efficacious therapeutic treatments for chronic Hepatitis C virus (HCV) infection. However, emergence of drug resistance mutations (DRMs) can greatly affect treatment outcomes and impede virological cure. While multiple DRMs have been observed for all currently used DAAs, the evolutionary determinants of such mutations are not currently well understood. Here, by considering DAAs targeting the nonstructural 3 (NS3) protein of HCV, we present results suggesting that epistasis plays an important role in the evolution of DRMs. Employing a sequence-based fitness landscape model whose predictions correlate highly with experimental data, we identify specific DRMs that are associated with strong epistatic interactions, and these are found to be enriched in multiple NS3-specific DAAs. Evolutionary modelling further supports that the identified DRMs involve compensatory mutational interactions that facilitate relatively easy escape from drug-induced selection pressures. Our results indicate that accounting for epistasis is important for designing future HCV NS3-targeting DAAs.
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    Evolving graph-based video crowd anomaly detection
    Yang, M ; Feng, Y ; Rao, AS ; Rajasegarar, S ; Tian, S ; Zhou, Z (Springer, 2024)
    Detecting anomalous crowd behavioral patterns from videos is an important task in video surveillance and maintaining public safety. In this work, we propose a novel architecture to detect anomalous patterns of crowd movements via graph networks. We represent individuals as nodes and individual movements with respect to other people as the node-edge relationship of an evolving graph network. We then extract the motion information of individuals using optical flow between video frames and represent their motion patterns using graph edge weights. In particular, we detect the anomalies in crowded videos by modeling pedestrian movements as graphs and then by identifying the network bottlenecks through a max-flow/min-cut pedestrian flow optimization scheme (MFMCPOS). The experiment demonstrates that the proposed framework achieves superior detection performance compared to other recently published state-of-the-art methods. Considering that our proposed approach has relatively low computational complexity and can be used in real-time environments, which is crucial for present day video analytics for automated surveillance.