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Nanophotonics Apr 2024Innovative terahertz waveguides are in high demand to serve as a versatile platform for transporting and manipulating terahertz signals for the full deployment of future...
Innovative terahertz waveguides are in high demand to serve as a versatile platform for transporting and manipulating terahertz signals for the full deployment of future six-generation (6G) communication systems. Metal-wire waveguides have emerged as promising candidates, offering the crucial advantage of sustaining low-loss and low-dispersion propagation of broadband terahertz pulses. Recent advances have opened up new avenues for implementing signal-processing functionalities within metal-wire waveguides by directly engraving grooves along the wire surfaces. However, the challenge remains to design novel groove structures to unlock unprecedented signal-processing functionalities. In this study, we report a plasmonic signal processor by engineering topological interface states within a terahertz two-wire waveguide. We construct the interface by connecting two multiscale groove structures with distinct topological invariants, i.e., featuring a π-shift difference in the Zak phases. The existence of this topological interface within the waveguide is experimentally validated by investigating the transmission spectrum, revealing a prominent transmission peak in the center of the topological bandgap. Remarkably, we show that this resonance is highly robust against structural disorders, and its quality factor can be flexibly controlled. This unique feature not only facilitates essential functions such as band filtering and isolating but also promises to serve as a linear differential equation solver. Our approach paves the way for the development of new-generation all-optical analog signal processors tailored for future terahertz networks, featuring remarkable structural simplicity, ultrafast processing speeds, as well as highly reliable performance.
PubMed: 38681677
DOI: 10.1515/nanoph-2023-0900 -
Sensors (Basel, Switzerland) Apr 2024This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the...
This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform (CWT) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of -44.8 to +44.8 deg. In the Bland-Altman plot, the measured results indicate the average difference of -1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively.
Topics: Heart Rate; Radar; Humans; Monitoring, Physiologic; Respiration; Respiratory Rate; Wavelet Analysis; Signal Processing, Computer-Assisted; Algorithms
PubMed: 38676065
DOI: 10.3390/s24082448 -
Scientific Reports Apr 2024With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM) pollution has escalated into a major global environmental crisis. This...
With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21-35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.
PubMed: 38671144
DOI: 10.1038/s41598-024-60486-9 -
Neuroscience and Biobehavioral Reviews Jun 2024Pyramidal neurons have a pivotal role in the cognitive capabilities of neocortex. Though they have been predominantly modeled as integrate-and-fire point processors,... (Review)
Review
Pyramidal neurons have a pivotal role in the cognitive capabilities of neocortex. Though they have been predominantly modeled as integrate-and-fire point processors, many of them have another point of input integration in their apical dendrites that is central to mechanisms endowing them with the sensitivity to context that underlies basic cognitive capabilities. Here we review evidence implicating impairments of those mechanisms in three major neurodevelopmental disabilities, fragile X, Down syndrome, and fetal alcohol spectrum disorders. Multiple dysfunctions of the mechanisms by which pyramidal cells are sensitive to context are found to be implicated in all three syndromes. Further deciphering of these cellular mechanisms would lead to the understanding of and therapies for learning disabilities beyond any that are currently available.
Topics: Humans; Animals; Learning Disabilities; Pyramidal Cells; Fetal Alcohol Spectrum Disorders; Neurodevelopmental Disorders; Down Syndrome; Fragile X Syndrome
PubMed: 38670298
DOI: 10.1016/j.neubiorev.2024.105688 -
Nature Communications Apr 2024Mode-division multiplexing (MDM) in optical fibers enables multichannel capabilities for various applications, including data transmission, quantum networks, imaging,...
Mode-division multiplexing (MDM) in optical fibers enables multichannel capabilities for various applications, including data transmission, quantum networks, imaging, and sensing. However, high-dimensional optical fiber systems, usually necessity bulk-optics approaches for launching different orthogonal fiber modes into the optical fiber, and multiple-input multiple-output digital electronic signal processing at the receiver to undo the arbitrary mode scrambling introduced by coupling and transmission in a multi-mode fiber. Here we show that a high-dimensional optical fiber communication system can be implemented by a reconfigurable integrated photonic processor, featuring kernels of multichannel mode multiplexing transmitter and all-optical descrambling receiver. Effective mode management can be achieved through the configuration of the integrated optical mesh. Inter-chip MDM optical communications involving six spatial- and polarization modes was realized, despite the presence of unknown mode mixing and polarization rotation in the circular-core optical fiber. The proposed photonic integration approach holds promising prospects for future space-division multiplexing applications.
PubMed: 38664412
DOI: 10.1038/s41467-024-47907-z -
PeerJ. Computer Science 2024Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art...
Deep learning approaches are generally complex, requiring extensive computational resources and having high time complexity. Transfer learning is a state-of-the-art approach to reducing the requirements of high computational resources by using pre-trained models without compromising accuracy and performance. In conventional studies, pre-trained models are trained on datasets from different but similar domains with many domain-specific features. The computational requirements of transfer learning are directly dependent on the number of features that include the domain-specific and the generic features. This article investigates the prospects of reducing the computational requirements of the transfer learning models by discarding domain-specific features from a pre-trained model. The approach is applied to breast cancer detection using the dataset curated breast imaging subset of the digital database for screening mammography and various performance metrics such as precision, accuracy, recall, F1-score, and computational requirements. It is seen that discarding the domain-specific features to a specific limit provides significant performance improvements as well as minimizes the computational requirements in terms of training time (reduced by approx. 12%), processor utilization (reduced approx. 25%), and memory usage (reduced approx. 22%). The proposed transfer learning strategy increases accuracy (approx. 7%) and offloads computational complexity expeditiously.
PubMed: 38660182
DOI: 10.7717/peerj-cs.1938 -
Scientific Reports Apr 2024Phosphorene is a unique semiconducting two-dimensional platform for enabling spintronic devices integrated with phosphorene nanoelectronics. Here, we have designed an...
Phosphorene is a unique semiconducting two-dimensional platform for enabling spintronic devices integrated with phosphorene nanoelectronics. Here, we have designed an all phosphorene lattice lateral spin valve device, conceived via patterned magnetic substituted atoms of 3d-block elements at both ends of a phosphorene nanoribbon acting as ferromagnetic electrodes in the spin valve. Through First-principles based calculations, we have extensively studied the spin-dependent transport characteristics of the new spin valve structures. Systematic exploration of the magnetoresistance (MR) of the spin valve for various substitutional atoms and bias voltage resulted in a phase diagram offering a colossal MR for V and Cr-substitutional atoms. Such MR can be directly attributed to their specific electronic structure, which can be further tuned by a gate voltage, for electric field controlled spin valves. The spin-dependent transport characteristics here reveal new features such as negative conductance oscillation and switching of the sign of MR due to change in the majority spin carrier type. Our study creates possibilities for the design of nanometric spin valves, which could enable integration of memory and logic elements for all phosphorene 2D processors.
PubMed: 38644366
DOI: 10.1038/s41598-024-58589-4 -
Science Advances Apr 2024The interference of nonclassical states of light enables quantum-enhanced applications reaching from metrology to computation. Most commonly, the polarization or spatial...
The interference of nonclassical states of light enables quantum-enhanced applications reaching from metrology to computation. Most commonly, the polarization or spatial location of single photons are used as addressable degrees of freedom for turning these applications into praxis. However, the scale-up for the processing of a large number of photons of these architectures is very resource-demanding due to the rapidly increasing number of components, such as optical elements, photon sources, and detectors. Here, we demonstrate a resource-efficient architecture for multiphoton processing based on time-bin encoding in a single spatial mode. We use an efficient quantum dot single-photon source and a fast programmable time-bin interferometer to observe the interference of up to eight photons in 16 modes, all recorded only with one detector, thus considerably reducing the physical overhead previously needed for achieving equivalent tasks. Our results can form the basis for a future universal photonics quantum processor operating in a single spatial mode.
PubMed: 38640248
DOI: 10.1126/sciadv.adj0993 -
Journal of Biomedical Optics Jun 2024Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell...
SIGNIFICANCE
Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms.
AIM
We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images.
APPROACH
The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process.
RESULTS
The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools.
CONCLUSION
Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.
Topics: Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Quantitative Phase Imaging; Algorithms; Optical Imaging
PubMed: 38638450
DOI: 10.1117/1.JBO.29.S2.S22706 -
Advanced Science (Weinheim,... Jun 2024Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current...
Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current molecular detection methods typically rely on extensive nucleic acid sample preparation and expensive instrumentation. Here, a simple, fully integrated, lab-in-a-magnetofluidic tube (LIAMT) platform is presented for "sample-to-result" molecular detection of virus. By leveraging magnetofluidic transport of micro/nano magnetic beads, the LIAMT device integrates viral lysis, nucleic acid extraction, isothermal amplification, and CRISPR detection within a single engineered microcentrifuge tube. To enable point-of-care molecular diagnostics, a palm-sized processor is developed for magnetofluidic separation, nucleic acid amplification, and visual fluorescence detection. The LIAMT platform is applied to detect SARS-CoV-2 and HIV viruses, achieving a detection sensitivity of 73.4 and 63.9 copies µL, respectively. Its clinical utility is further demonstrated by detecting SARS-CoV-2 and HIV in clinical samples. This simple, affordable, and portable LIAMT platform holds promise for rapid and sensitive molecular diagnostics of infectious diseases at the point-of-care.
Topics: SARS-CoV-2; Humans; Nucleic Acid Amplification Techniques; COVID-19; Lab-On-A-Chip Devices; Point-of-Care Systems; Sensitivity and Specificity; Molecular Diagnostic Techniques; Equipment Design; HIV Infections; HIV
PubMed: 38634211
DOI: 10.1002/advs.202310066