Electrical and Electronic Engineering - Research Publications

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    A Bayesian approach to (online) transfer learning: Theory and algorithms
    Wu, X ; Manton, JH ; Aickelin, U ; Zhu, J (Elsevier BV, 2023-11)
    Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem. While conceivable that knowledge from one task could help solve a related task, if not executed properly, transfer learning algorithms can impair the learning performance instead of improving it – commonly known as negative transfer. In this paper, we use a parametric statistical model to study transfer learning from a Bayesian perspective. Specifically, we study three variants of transfer learning problems, instantaneous, online, and time-variant transfer learning. We define an appropriate objective function for each problem and provide either exact expressions or upper bounds on the learning performance using information-theoretic quantities, which allow simple and explicit characterizations when the sample size becomes large. Furthermore, examples show that the derived bounds are accurate even for small sample sizes. The obtained bounds give valuable insights into the effect of prior knowledge on transfer learning, at least with respect to our Bayesian formulation of the transfer learning problem. In particular, we formally characterize the conditions under which negative transfer occurs. Lastly, we devise several (online) transfer learning algorithms that are amenable to practical implementations, some of which do not require the parametric assumption. We demonstrate the effectiveness of our algorithms with real data sets, focusing primarily on when the source and target data have strong similarities.
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    Input-mapping based data-driven model predictive control for unknown linear systems via online learning
    Yang, L ; Li, D ; Ma, A ; Xi, Y ; Pu, Y ; Tan, Y (WILEY, 2022-06-16)
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    Progress and Future Prospects of Wide-Bandgap Metal-Compound-Based Passivating Contacts for Silicon Solar Cells
    Gao, K ; Bi, Q ; Wang, X ; Liu, W ; Xing, C ; Li, K ; Xu, D ; Su, Z ; Zhang, C ; Yu, J ; Li, D ; Sun, B ; Bullock, J ; Zhang, X ; Yang, X (WILEY-V C H VERLAG GMBH, 2022-07)
    Advanced doped-silicon-layer-based passivating contacts have boosted the power conversion efficiency (PCE) of single-junction crystalline silicon (c-Si) solar cells to over 26%. However, the inevitable parasitic light absorption of the doped silicon layers impedes further PCE improvement. To this end, alternative passivating contacts based on wide-bandgap metal compounds (so-called dopant-free passivating contacts (DFPCs)) have attracted great attention, thanks to their potential merits in terms of parasitic absorption loss, ease-of-deposition, and cost. Intensive research activity has surrounded this topic with significant progress made in recent years. Various electron-selective and hole-selective contacts based on metal compounds have been successfully developed, and a champion PCE of 23.5% has been achieved for a c-Si solar cell with a MoOx -based hole-selective contact. In this work, the fundamentals and development status of DFPCs are reviewed and the challenges and potential solutions for enhancing the carrier selectivity of DFPCs are discussed. Based on comprehensive and in-depth analysis and simulations, the improvement strategies and future prospects for DFPCs design and device implementation are pointed out. By tuning the carrier concentration of the metal compound and the work function of the capping transparent electrode, high PCEs over 26% can be achieved for c-Si solar cells with DFPCs.
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    Polymeric Nanoneedle Arrays Mediate Stiffness-Independent Intracellular Delivery
    Yoh, HZ ; Chen, Y ; Aslanoglou, S ; Wong, S ; Trifunovic, Z ; Crawford, S ; Lestrell, E ; Priest, C ; Alba, M ; Thissen, H ; Voelcker, NH ; Elnathan, R (WILEY-V C H VERLAG GMBH, 2021-09-01)
    Abstract Tunable vertically aligned nanostructures, usually fabricated using inorganic materials, are powerful nanoscale tools for advanced cellular manipulation. However, nanoscale precision typically requires advanced nanofabrication machinery and involves high manufacturing costs. By contrast, polymeric nanoneedles (NNs) of precise geometry can be produced by replica molding or nanoimprint lithography—rapid, simple, and cost‐effective. Here, cytocompatible polymeric arrays of NNs are engineered with identical topographies but differing stiffness, using polystyrene (PS), SU8, and polydimethylsiloxane (PDMS). By interfacing the polymeric NN arrays with adherent and suspension mammalian cells, and comparing the cellular responses of each of the three polymeric substrates, the influence of substrate stiffness from topography on cell behavior is decoupled. Notably, the ability of PS, SU8, and PDMS NNs is demonstrated to facilitate mRNA delivery to GPE86 cells with 26.8% ± 3.5%, 33.2% ± 7.4%, and 30.1% ± 4.1% average transfection efficiencies, respectively. Electron microscopy reveals the intricacy of the cell–NN interactions; and immunofluorescence imaging demonstrates that enhanced endocytosis is one of the mechanisms of PS NN‐mediated intracellular delivery, involving the endocytic proteins caveolin‐1 and clathrin heavy chain. The results provide insights into the interfacial interactions between cells and polymeric NNs, and their related intracellular delivery mechanisms.
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    Role of actin cytoskeleton in cargo delivery mediated by vertically aligned silicon nanotubes
    Chen, Y ; Yoh, HZ ; Shokouhi, A-R ; Murayama, T ; Suu, K ; Morikawa, Y ; Voelcker, NH ; Elnathan, R (BMC, 2022-09-08)
    Nanofabrication technologies have been recently applied to the development of engineered nano-bio interfaces for manipulating complex cellular processes. In particular, vertically configurated nanostructures such as nanoneedles (NNs) have been adopted for a variety of biological applications such as mechanotransduction, biosensing, and intracellular delivery. Despite their success in delivering a diverse range of biomolecules into cells, the mechanisms for NN-mediated cargo transport remain to be elucidated. Recent studies have suggested that cytoskeletal elements are involved in generating a tight and functional cell-NN interface that can influence cargo delivery. In this study, by inhibiting actin dynamics using two drugs-cytochalasin D (Cyto D) and jasplakinolide (Jas), we demonstrate that the actin cytoskeleton plays an important role in mRNA delivery mediated by silicon nanotubes (SiNTs). Specifically, actin inhibition 12 h before SiNT-cellular interfacing (pre-interface treatment) significantly dampens mRNA delivery (with efficiencies dropping to 17.2% for Cyto D and 33.1% for Jas) into mouse fibroblast GPE86 cells, compared to that of untreated controls (86.9%). However, actin inhibition initiated 2 h after the establishment of GPE86 cell-SiNT interface (post-interface treatment), has negligible impact on mRNA transfection, maintaining > 80% efficiency for both Cyto D and Jas treatment groups. The results contribute to understanding potential mechanisms involved in NN-mediated intracellular delivery, providing insights into strategic design of cell-nano interfacing under temporal control for improved effectiveness.
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    Digital twins to enable better precision and personalized dementia care.
    Wickramasinghe, N ; Ulapane, N ; Andargoli, A ; Ossai, C ; Shuakat, N ; Nguyen, T ; Zelcer, J (Oxford University Press (OUP), 2022-10)
    In this perspective paper, we want to highlight the potential benefits of incorporating digital twins to support better dementia care. In particular, we assert that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization. Digital twins have been used successfully in manufacturing to enable better prediction and tailoring of solutions to meet required needs, and thereby have enabled more effective and efficient deployment of resources. We develop a model for digital twin in the healthcare domain as a clinical decision support tool by extrapolating its current uses from the manufacturing domain. We illustrate the power of the developed model in the context of dementia. Given the rapid rise of chronic conditions and the pressures on healthcare delivery to provide high quality, cost-effective care anywhere and anytime, we assert that such an approach is consistent with a value-based healthcare philosophy and thus important as the numbers of people with dementia continues to grow exponentially and this pressing healthcare issue is yet to be optimally addressed. Further research and development in this rapidly evolving domain is a strategic priority for ensuring the delivery of superior dementia care.
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    Polyvinylpyrrolidone-Capped Silver Nanoparticles for Highly Sensitive and Selective Optical Fiber-Based Ammonium Sensor
    Potdar, RP ; Khollam, YB ; Shaikh, SF ; More, PS ; Rana, AUHS (MDPI, 2022-10-01)
    Herein, aqueous ammonium sensing characteristics of polyvinylpyrrolidone (PVP) capped silver nanoparticles (Ag-NPs) coated optical fiber-based sensors are presented. The PVP-capped Ag-NPs were prepared using cold and modified polyol synthesis methods. Aqueous ammonium detection was carried out by the surface plasmon resonance (SPR) effect observed in the Ag-NPs coated optical fiber system. The effect of cold and modified polyol synthesis methods on optical sensing performance was studied. The optical fiber cladding was modified with PVP-capped Ag-NPs according to the standard protocol for sensing investigation. The probe sensing response was analyzed for varying concentrations of ammonium ions on red, green, and blue LEDs. The sensor characteristics, viz., sensing response, repeatability, calibration curve, and ambient light effect, were investigated for PVP capped Ag-NPs coated optical fiber-based sensor. The PVP capped Ag-NPs synthesized via the polyol synthesis method showed a detection limit of 48.9 mM, 1.33 mV/M sensitivity, and an excellent linear relationship (R2 = 0.9992) between voltage and ammonium ion concentration in the range of 0.054-13.4 M concentration. On the other hand, PVP capped Ag-NPs synthesized using the cold synthesis method showed a detection limit of 159.4 mM, a sensitivity of 0.06 mV/M, and a poor linear relationship (R2 = 0.4588) between voltage and ammonium ion concentration in the range of 0.054-13.4 M concentration. The results indicate that the PVP-capped Ag-NPs synthesized using the polyol synthesis method exhibit enhanced ammonium ion sensing compared to the cold synthesis method.
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    Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals.
    Zitouni, MS ; Lih Oh, S ; Vicnesh, J ; Khandoker, A ; Acharya, UR (Frontiers Media S.A., 2022)
    Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI-), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.
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    Correlation between maternal and fetal heart rate increases with fetal mouse age in typical development and is disturbed in autism mouse model treated with valproic acid.
    Widatalla, N ; Khandoker, A ; Yoshida, C ; Nakanishi, K ; Fukase, M ; Suzuki, A ; Saito, M ; Kimura, Y ; Kasahara, Y (Frontiers Media SA, 2022)
    INTRODUCTION: Autism spectrum disorder (ASD) is considered a significant behavioral problem that is characterized by impairment in social interaction and communication. It is believed that some cases of ASD originate in the intrauterine maternal environment. Therefore, we hypothesized that there might be qualitative changes in the interaction between the mother and fetus in ASD during the prenatal period, hence, we investigated the similarity patterns between maternal and fetal heart rate (HR). METHODS: In this study, we first demonstrate the presence and formation of similarities between maternal and fetal RR interval (RRI) collected from typical developmental mice at different embryonic days (EDs), ED13.5, ED15.5, ED17.5, and ED18.5. The similarities were quantified by means of cross-correlation (CC) and magnitude-squared coherence (MSC) analyses. Correlation analysis between the CC coefficients and EDs and between MSC coefficients and EDs showed that the same coefficients increase with EDs, suggesting that similarities between maternal and fetal RRI are associated with typical fetal development. Next, because maternal and fetal similarities were indicative of development, a comparison analysis between the autism mouse model (injected with valproic acid (VPA)), and the control group (injected with saline) was performed for ED15.5 and ED18.5. RESULTS: The results of the comparison showed that the CC and MSC coefficients of VPA fetuses were significantly lower than that of the control group. The lower coefficients in VPA-treated mice suggest that they could be one of the features of ASD symptoms. The findings of this study can assist in identifying potential ASD causes during the prenatal period.
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    A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.
    Saleem, S ; Khandoker, AH ; Alkhodari, M ; Hadjileontiadis, LJ ; Jelinek, HF (Springer Science and Business Media LLC, 2022-11-01)
    Artifacts in the Electrocardiogram (ECG) degrade the quality of the recorded signal and are not conducive to heart rate variability (HRV) analysis. The two types of noise most often found in ECG recordings are technical and physiological artifacts. Current preprocessing methods primarily attend to ectopic beats but do not consider technical issues that affect the ECG. A secondary aim of this study was to investigate the effect of increasing increments of artifacts on 24 of the most used HRV measures. A two-step preprocessing approach for denoising HRV is introduced which targets each type of noise separately. First, the technical artifacts in the ECG are eliminated by applying complete ensemble empirical mode decomposition with adaptive noise. The second step removes physiological artifacts from the HRV signal using a combination filter of single dependent rank order mean and an adaptive filtering algorithm. The performance of the two-step pre-processing tool showed a high correlation coefficient of 0.846 and RMSE value of 7.69 × 10-5 for 6% of added ectopic beats and 6 dB Gaussian noise. All HRV measures studied except HF peak and LF peak are significantly affected by both types of noise. Frequency measures of Total power, HF power, and LF power and fragmentation measures; PAS, PIP, and PSS are the most sensitive to both types of noise.