Quarter-Wave Plate Metasurfaces for Generating Multi-Channel Vortex Beams

Metasurfaces of quarter-wave plate (QWP) meta-atoms have exhibited high flexibility and versatile functionalities in the manipulation of light fields. However, the generation of multi-channel vortex beams with the QWP meta-atom metasurfaces presents a significant challenge. In this study, we propose dielectric metasurfaces composed of QWP meta-atoms to manipulate multi-channel vortex beams. QWP meta-atoms, systematically arranged in concentric circular rings, are designed to introduce the modulations via the propagation phase and geometric phase, leading to the generation of co- and cross-polarized vortex beams in distinct channels. Theoretical investigations and simulations are employed to analyze the modulation process, confirming the capability of QWP meta-atom metasurfaces for generating the multi-channel vortex beams. This study presents prospective advancements for the compact, integrated, and multifunctional nanophotonic platforms, which have potential applications in classical physics and quantum domains.

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing.

Residency Octree: A Hybrid Approach for Scalable Web-Based Multi-Volume Rendering

We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.

High-Efficiency Multi-Channel Orbital Angular Momentum Multiplexing Enabled by the Angle-Dispersive Metasurface

Orbital angular momentum (OAM) multiplexing of electromagnetic (EM) waves is of great significance for high-speed wireless communication and remote sensing. To achieve high-efficiency OAM multiplexing for multi-channel incident EM waves, this paper presents a novel angle-dispersive meta-atom structure, which can introduce the required anti-symmetric phase dispersion as well as high transmission efficiency for OAM multiplexing. These meta-atoms are then arranged delicately to form an angle-dispersive metasurface working at the X band, which enables three-channel OAM multiplexing by converting highly directional transverse-magnetic (TM) waves incident from 0 and ±45° to coaxial OAM beams with l = 0 and ±2 modes, respectively. The simulation and experimental results reveal that the proposed metasurface can convert a higher proportion of energy to the required OAM modes compared to the conventional OAM multiplexing metasurfaces, which can significantly improve the coaxial transmission efficiency of multi-channel OAM multiplexing.

A Robust Memristor-Enhanced Polynomial Hyper-Chaotic Map and Its Multi-Channel Image Encryption Application

Nowadays, the utilization of memristors to enhance the dynamical properties of chaotic systems has become a popular research topic. In this paper, we present the design of a novel 2D memristor-enhanced polynomial hyper-chaotic map (2D-MPHM) by utilizing the cross-coupling of two TiO2 memristors. The dynamical properties of the 2D-MPHM were investigated using Lyapunov exponents, bifurcation diagrams, and trajectory diagrams. Additionally, Kolmogorov entropy and sample entropy were also employed to evaluate the complexity of the 2D-MPHM. Numerical analysis has demonstrated the superiority of the 2D-MPHM. Subsequently, the proposed 2D-MPHM was applied to a multi-channel image encryption algorithm (MIEA-MPHM) whose excellent security was demonstrated by key space, key sensitivity, plaintext sensitivity, information entropy, pixel distribution, correlation analysis, and robustness analysis. Finally, the encryption efficiency of the MIEA-MPHM was evaluated via numerous encryption efficiency tests. These tests demonstrate that the MIEA-MPHM not only possesses excellent security but also offers significant efficiency advantages, boasting an average encryption rate of up to 87.2798 Mbps.

Multi-Channel Time-Domain Boring-Vibration-Enhancement Method Using RNN Networks

The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method's accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals.

Structural Design and Simulation of a Multi-Channel and Dual Working Condition Wafer Defect Inspection Prototype

Detecting and classifying defects on unpatterned wafers is a key part of wafer front-end inspection. Defect inspection schemes vary depending on the type and location of the defects. In this paper, the structure of the prototype is designed to meet the requirements of wafer surface and edge defect inspection. This prototype has four inspection channels: scattering, reflection, phase, and contour, with two working conditions: surface and edge inspection. The key structure of the prototype was simulated using Ansys. The simulation results show that the maximum deformation of the optical detection subsystem is 19.5 μm and the fundamental frequency of the prototype is 96.9 Hz; thus, these results meet the requirements of optical performance stability and structural design. The experimental results show that the prototype meets the requirements of the inspection sensitivity better than 200 nm equivalent PSL spherical defects.

A Multi-Channel Frequency Router Based on an Optimization Algorithm and Dispersion Engineering

Integrated frequency routers, which can guide light with different frequencies to different output ports, are an important kind of nanophotonic device. However, frequency routers with both a compact size and multiple channels are difficult to realize, which limits the application of these frequency routers in nanophotonics. Here, a kind of bandgap optimization algorithm, which consists of the finite element method and topology optimization, is proposed to design a multi-channel frequency router. Channels supporting photonic edge states with different frequencies are built through the synthetic dimension of translational deformation. Due to the help of the developed optimization algorithms, the number of channels and output ports can be increased up to nine while maintaining ultracompact device size. The device operates within a working band of 0.585-0.665 c/a, corresponding to 1.504-1.709 μm when the lattice constant is set as 1 μm, covering the telecom wavelength of 1.55 μm. The average crosstalk is about -11.49 dB. The average extinction ratio is around 16.18 dB. Because the bus of the device can be regarded as a part of a topological rainbow, the results show that the structure is robust to fabrication errors. This method is general, which can be used for different materials and different frequency ranges. The all-dielectric planar configuration of our router is compact, robust, and easy to integrate, providing a new method for on-chip multi-channel broadband information processing.

A Multimode 28 GHz CMOS Fully Differential Beamforming IC for Phased Array Transceivers

A 28 GHz fully differential eight-channel beamforming IC (BFIC) with multimode operations is implemented in 65 nm CMOS technology for use in phased array transceivers. The BFIC has an adjustable gain and phase control on each channel to achieve fine beam steering and beam pattern. The BFIC has eight differential beamforming channels each consisting of the two-stage bi-directional amplifier with a precise gain control circuit, a six-bit phase shifter, a three-bit digital step attenuator, and a tuning bit for amplitude and phase variation compensation. The Tx and Rx mode overall gains of the differential eight-channel BFIC are around 11 dB and 9 dB, respectively, at 27.0-29.5 GHz. The return losses of the Tx mode and Rx mode are >10 dB at 27.0-29.5 GHz. The maximum phase of 354° with a phase resolution of 5.6° and the maximum attenuation of 31 dB, including the gain control bits with an attenuation resolution of 1 dB, is achieved at 27.0-29.5 GHz. The root mean square (RMS) phase and amplitude errors are <3.2° and <0.6 dB at 27.0-29.5 GHz, respectively. The chip size is 3.0 × 3.5 mm², including pads, and Tx mode current consumption is 580 mA at 2.5 V supply voltage.

Simultaneous, Multi-Channel, Near-Infrared Fluorescence Visualization of Mesenteric Lymph Nodes Using Indocyanine Green and Methylene Blue: A Demonstration in a Porcine Model

Near-infrared fluorescence (NIRF) image-guided surgery is a useful tool that can help reduce perioperative complications and improve tissue recognition. Indocyanine green (ICG) dye is the most frequently used in clinical studies. ICG NIRF imaging has been used for lymph node identification. However, there are still many challenges in lymph node identification by ICG. There is increasing evidence that methylene blue (MB), another clinically applicable fluorescent dye, can also be useful in the intraoperative fluorescence-guided identification of structures and tissues. We hypothesized that MB NIRF imaging could be used for lymph node identification. The aim of this study was to evaluate the feasibility of intraoperative lymph node fluorescence detection using intravenously (IV) administered MB and compare it to ICG via a camera that has two dedicated near-infrared (NIR) channels. Three pigs were used in this study. ICG (0.2 mg/kg) was administered via a peripheral venous catheter followed by immediate administration of MB (0.25 mg/kg). NIRF images were acquired as video recordings at different time points (every 10 min) over an hour using the QUEST SPECTRUM^® 3 system (Quest Medical Imaging, Middenmeer, The Netherlands), which has two dedicated NIR channels for simultaneous intraoperative fluorescence guidance. The 800 nm channel was used to capture ICG fluorescence and the 700 nm channel was used for MB. The target (lymph nodes and small bowel) and the background (vessels-free field of the mesentery) were highlighted as the regions of interest (ROIs), and corresponding fluorescence intensities (FI) from these ROIs were measured. The target-to-background ratio (TBR) was then computed as the mean FI of the target minus the mean FI of the background divided by the mean FI of the background. In all included animals, a clear identification of lymph nodes was achieved at all time points. The mean TBR of ICG in lymph nodes and small bowel was 4.57 ± 1.00 and 4.37 ± 1.70, respectively for the overall experimental time. Regarding MB, the mean TBR in lymph nodes and small bowel was 4.60 ± 0.92 and 3.27 ± 0.62, respectively. The Mann-Whitney U test of the lymph node TBR/small bowel TBR showed that the TBR ratio of MB was statistically significantly higher than ICG. The fluorescence optical imaging technology used allows for double-wavelength assessment. This feasibility study proves that lymph nodes can be discriminated using two different fluorophores (MB and ICG) with different wavelengths. The results suggest that MB has a promising potential to be used to detect lymphatic tissue during image-guided surgery. Further preclinical trials are needed before clinical translation.

Ka Band Low Channel Mutual Coupling Integrated Packaged Phased Array Receiver Front-End for Passive Millimeter-Wave Imaging

This paper presents a Ka band eight-channel integrated packaged phased array receiver front-end for a passive millimeter-wave imaging system. Since multiple receiving channels are integrated in a given package, the mutual coupling issue affecting the channel will deteriorate imaging quality. Therefore, in this study, the influence of channel mutual coupling on the system array pattern and amplitude phase error is analyzed, and the design requirements are proposed according to the results. During the design implementation, the coupling paths are discussed, and passive circuits in the path are modeled and designed to reduce the level of channel mutual coupling and spatial radiation. Finally, an accurate coupling measurement method for a multi-channel integrated phased array receiver is proposed. The receiver front-end achieves a 28~31 dB single channel gain, a 3.6 dB noise figure, less than -47 dB of channel mutual coupling. Furthermore, the array pattern of the two-dimensional 1024 channel system composed of the front end of the receiver is consistent with the simulation, and the receiver's performance is verified by a human-body-imaging experiment. The proposed coupling analysis, design, and measurement methods are also applicable to other multi-channel integrated packaged devices.

One-Step Bioprinting of Multi-Channel Hydrogel Filaments Using Chaotic Advection: Fabrication of Pre-Vascularized Muscle-Like Tissues

The biofabrication of living constructs containing hollow channels is critical for manufacturing thick tissues. However, current technologies are limited in their effectiveness in the fabrication of channels with diameters smaller than hundreds of micrometers. It is demonstrated that the co-extrusion of cell-laden hydrogels and sacrificial materials through printheads containing Kenics static mixing elements enables the continuous and one-step fabrication of thin hydrogel filaments (1 mm in diameter) containing dozens of hollow microchannels with widths as small as a single cell. Pre-vascularized skeletal muscle-like filaments are bioprinted by loading murine myoblasts (C2C12 cells) in gelatin methacryloyl - alginate hydrogels and using hydroxyethyl cellulose as a sacrificial material. Higher viability and metabolic activity are observed in filaments with hollow multi-channels than in solid constructs. The presence of hollow channels promotes the expression of Ki67 (a proliferation biomarker), mitigates the expression of hypoxia-inducible factor 1-alpha , and markedly enhances cell alignment (i.e., 82% of muscle myofibrils aligned (in ±10°) to the main direction of the microchannels after seven days of culture). The emergence of sarcomeric α-actin is verified through immunofluorescence and gene expression. Overall, this work presents an effective and practical tool for the fabrication of pre-vascularized engineered tissues.

Immersion-Triggered Active Switch for Spin-Decoupled Meta-Optics Multi-Display

Meta-optics exhibits many promising applications in various fields of optical displays, imaging, and information encryption. However, heading towards next-generation intelligent displays, its broad implementation is critically restricted by the lack of practical active tuning capability. Beyond the conventional electrical/optical/mechanical/thermal tuning methods, liquid immersion has recently emerged as a facile mechanism for active spectral tuning. To further conquer the challenge in achieving active complicated optical-field manipulation, here, an environment-compliant switch for meta-optics multi-display is originally proposed and experimentally realized via the liquid immersion tuning scheme. By designing the spin-decoupled phase array for left-/right-handed circular polarizations, it flexibly presents quad-fold independent-encoded phase distributions for different medium-relevant and polarization-controlled channels, thus enabling four switchable holographic images through immersion tuning. Such a proposed immersion tuning design is quite a straightforward approach for meta-optics holographic displays, enjoying full-spatial usage, design flexibility, and large-scale facile implementation. Overall, the proposed liquid immersion tuning strategy for a meta-optics multi-display would strongly benefit the practical applications in biochemical sensing, environment-adaptive displays, and information encryption.

Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access-verification-traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of "on-chain + off-chain" are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.

T Cells Chemotaxis Migration Studies with a Multi-Channel Microfluidic Device

Immune surveillance is dependent on lymphocyte migration and targeted recruitment. This can involve different modes of cell motility ranging from random walk to highly directional environment-guided migration driven by chemotaxis. This study protocol describes a flow-based microfluidic device to perform quantitative multiplex cell migration assays with the potential to investigate in real time the migratory response of T cells at the population or single-cell level. The device also allows for subsequent in situ fixation and direct fluorescence analysis of the cells in the microchannel.

Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar

A multi-channel backscatter communication and radar sensing system is proposed and demonstrated in this paper. Frequency modulated continuous wave (FMCW) radar ranging is integrated with simultaneous uplink data transmission from a self-packaged active radio frequency (RF) tag. A novel package solution is proposed for the RF tag. With the proposed package, the RF tag can transmit a 32-QAM signal up to 2.5 Gbps and QPSK signal up to 8 Gbps. For a multi-tag scenario, we proposed using spread spectrum code to separate the data from each tag. In this case, tags can be placed at arbitrary locations without adjacent channel interference. Proof-of-concept simulations and measurements are demonstrated. A 625 Mbps data rate is achieved in a dual-tag scenario for two tags.

Research on Temperature Compensation of Multi-Channel Pressure Scanner Based on an Improved Cuckoo Search Optimizing a BP Neural Network

A multi-channel pressure scanner is an essential tool for measuring and acquiring various pressure parameters in aerospace applications. It is important to note, however, that the pressure sensor of each of these channels will drift significantly with the increase in the temperature range of the pressure measurement, and the output voltage of each of these channels will show nonlinear characteristics, which will constrain the improvements in the accuracy of the measurement. In the regression fitting process, it is difficult to fit nonlinear data with the traditional least-squares method, which leaves pressure measurement accuracy unsatisfactory. A temperature compensation method based on an improved cuckoo search optimizing a BP neural network for a multi-channel pressure scanner is proposed in this paper to improve pressure measurement accuracy in a wide temperature range. Using the chaotic simplex algorithm, we first improved the cuckoo search algorithm, then optimized the connection weights and thresholds of the BP neural network, and finally constructed an experimental calibration system to investigate the temperature compensation of the multi-channel pressure scanning valves in the -40 °C to 60 °C temperature range. The compensation test results show that the algorithm has a better compensation effect and is more suitable for the temperature compensation of multi-channel pressure scanners than the traditional least-squares method and the standard RBF and BP neural networks. The maximum full-scale error of all 32 channels is 0.02% FS (full-scale error) and below, which realizes its high-accuracy multi-point pressure measurement in a wide temperature range.

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian-Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping

OBJECTIVE

A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection.

APPROACH

In this study, a trigger-based algorithm combining multi-channel convolutional neural network and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1-second data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1-second window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel convolutional neural network and a class activation mapping algorithm. Finally, the data segmentation strategy was established based on the heat map, the basic law of falls and the real-time requirements.

MAIN RESULTS

This method was verified by the SisFall dataset. The obtained segmentation strategy (i.e., to start segmenting a small data segment with a window duration of 325 ms when the acceleration signal magnitude vector is less than 9.217 m/s2) met the real-time requirements for pre-impact fall detection. Moreover, it was suitable for various machine learning algorithms, and the accuracy of the machine learning algorithms used exceeded 94.8%, with the machine learning algorithms verifying the data segmentation strategy.

SIGNIFICANCE

The proposed method can automatically identify the class activation area, save the computing resources of wearable devices, shorten the duration of segmentation window, and ensure the real-time performance of pre-impact fall detection.

Low-Area Four-Channel Controlled Dielectric Breakdown System Design for Point-of-Care Applications

In this study, we propose a low-area multi-channel controlled dielectric breakdown (CDB) system that simultaneously produces several nanopore sensors. Conventionally, solid-state nanopores are prepared by etching or drilling openings in a silicon nitride (SiNx) substrate, which is expensive and requires a long processing time. To address these challenges, a CDB technique was introduced and used to fabricate nanopore channels in SiNx membranes. However, the nanopore sensors produced by the CDB result in a severe pore-to-pore diameter variation as a result of different fabrication conditions and processing times. Accordingly, it is indispensable to simultaneously fabricate nanopore sensors in the same environment to reduce the deleterious effects of pore-to-pore variation. In this study, we propose a four-channel CDB system that comprises an amplifier that boosts the command voltage, a 1-to-4 multiplexer, a level shifter, a low-noise transimpedance amplifier and a data acquisition device. To prove our design concept, we used the CDB system to fabricate four nanopore sensors with diameters of <10 nm, and its in vitro performance was verified using λ-DNA samples.

Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging

Multiplex immunofluorescence (MxIF) is an emerging technique that allows for staining multiple cellular and histological markers to stain simultaneously on a single tissue section. However, with multiple rounds of staining and bleaching, it is inevitable that the scarce tissue may be physically depleted. Thus, a digital way of synthesizing such missing tissue would be appealing since it would increase the useable areas for the downstream single-cell analysis. In this work, we investigate the feasibility of employing generative adversarial network (GAN) approaches to synthesize missing tissues using 11 MxIF structural molecular markers (i.e., epithelial and stromal). Briefly, we integrate a multi-channel high-resolution image synthesis approach to synthesize the missing tissue from the remaining markers. The performance of different methods is quantitatively evaluated via the downstream cell membrane segmentation task. Our contribution is that we, for the first time, assess the feasibility of synthesizing missing tissues in MxIF via quantitative segmentation. The proposed synthesis method has comparable reproducibility with the baseline method on performance for the missing tissue region reconstruction only, but it improves 40% on whole tissue synthesis that is crucial for practical application. We conclude that GANs are a promising direction of advancing MxIF imaging with deep image synthesis.

First demonstration of robust tri-gateβ-Ga2O3nano-membrane field-effect transistors

Nano-membrane tri-gateβ-gallium oxide (β-Ga₂O₃) field-effect transistors (FETs) on SiO₂/Si substrate fabricated via exfoliation have been demonstrated for the first time. By employing electron beam lithography, the minimum-sized features can be defined with the footprint channel width of 50 nm. For high-quality interface betweenβ-Ga₂O₃and gate dielectric, atomic layer-deposited 15 nm thick aluminum oxide (Al₂O₃) was utilized with tri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated devices demonstrate extremely low subthreshold slope (SS) of 61 mV dec^-1, high drain current (I_DS) ON/OFF ratio of 1.5 × 10⁹, and negligible transfer characteristic hysteresis. We also experimentally demonstrated robustness of these devices with current-voltage (I-V) characteristics measured at temperatures up to 400 °C.

Liver tumor detection based on objects as points

The automatic detection of liver tumors by computed tomography is challenging, owing to their wide variations in size and location, as well as to their irregular shapes. Existing detection methods largely rely on two-stage detectors and use CT images marked with bounding boxes for training and detection. In this study, we propose a single-stage detector method designed to accurately detect multiple tumors simultaneously, and provide results demonstrating its increased speed and efficiency compared to prior methods. The proposed model divides CT images into multiple channels to obtain continuity information and implements a bounding box attention mechanism to overcome the limitation of inaccurate prediction of tumor center points and decrease redundant bounding boxes. The model integrates information from various channels using an effective Squeeze-and-Excitation attention module. The proposed model obtained a mean average precision result of 0.476 on the Decathlon dataset, which was superior to that of the prior methods examined for comparison. This research is expected to enable physicians to diagnose tumors very efficiently; particularly, the prediction of tumor center points is expected to enable physicians to rapidly verify their diagnostic judgments. The proposed method is considered suitable for future adoption in clinical practice in hospitals and resource-poor areas because its superior performance does not increase computational cost; hence, the equipment required is relatively inexpensive.

Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability.

A Biomimetic, SoC-Based Neural Stimulator for Novel Arbitrary-Waveform Stimulation Protocols

Novel neural stimulation protocols mimicking biological signals and patterns have demonstrated significant advantages as compared to traditional protocols based on uniform periodic square pulses. At the same time, the treatments for neural disorders which employ such protocols require the stimulator to be integrated into miniaturized wearable devices or implantable neural prostheses. Unfortunately, most miniaturized stimulator designs show none or very limited ability to deliver biomimetic protocols due to the architecture of their control logic, which generates the waveform. Most such designs are integrated into a single System-on-Chip (SoC) for the size reduction and the option to implement them as neural implants. But their on-chip stimulation controllers are fixed and limited in memory and computing power, preventing them from accommodating the amplitude and timing variances, and the waveform data parameters necessary to output biomimetic stimulation. To that end, a new stimulator architecture is proposed, which distributes the control logic over three component tiers – software, microcontroller firmware and digital circuits of the SoC, which is compatible with existing and future biomimetic protocols and with integration into implantable neural prosthetics. A portable prototype with the proposed architecture is designed and demonstrated in a bench-top test with various known biomimetic output waveforms. The prototype is also tested in vivo to deliver a complex, continuous biomimetic stimulation to a rat model of a spinal-cord injury. By delivering this unique biomimetic stimulation, the device is shown to successfully reestablish the connectivity of the spinal cord post-injury and thus restore motor outputs in the rat model.

Drug Evaluation Based on a Multi-Channel Cell Chip with a Horizontal Co-Culture

We developed a multi-channel cell chip containing a three-dimensional (3D) scaffold for horizontal co-culture and drug toxicity screening in multi-organ culture (human glioblastoma, cervical cancer, normal liver cells, and normal lung cells). The polydimethylsiloxane (PDMS) multi-channel cell chip (PMCCC) was based on fused deposition modeling (FDM) technology. The architecture of the PMCCC was an open-type cell chip and did not require a pump or syringe. We investigated cell proliferation and cytotoxicity by conducting 3-(4,5-dimethylthiazol-2-yl)-2,5-dphenyltetrazolium bromide (MTT) and lactate dehydrogenase (LDH) assays and analysis of oleanolic acid (OA)-treated multi-channel cell chips. The results of the MTT and LDH assays showed that OA treatment in the multi-channel cell chip of four cell lines enhanced chemoresistance of cells compared with that in the 2D culture. Furthermore, we demonstrated the feasibility of the application of our multi-channel cell chip in various analysis methods through Annexin V-fluorescein isothiocyanate/propidium iodide staining, which is not used for conventional cell chips. Taken together, the results demonstrated that the PMCCC may be used as a new 3D platform because it enables simultaneous drug screening in multiple cells by single point injection and allows analysis of various biological processes.

Detection of Coronary Artery Disease Using Multi-Domain Feature Fusion of Multi-Channel Heart Sound Signals

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

Frequency-Division Multiplexing with Graphene Active Electrodes for Neurosensor Applications

Multielectrode arrays are used broadly for neural recording, both in vivo and for ex vivo cultured neurons. In most cases, recording sites are passive electrodes wired to external read-out circuitry, and the number of wires is at least equal to the number of recording sites. We present an approach to break the conventional N-wire, N-electrode array architecture using graphene active electrodes, which allow signal upconversion at the recording site and sharing of each interface wire among multiple active electrodes using frequency-division multiplexing (FDM). The presented work includes the design and implementation of a frequency modulation and readout architecture using graphene FET electrodes, a custom integrated circuit (IC) analog front-end (AFE), and digital demodulation. The AFE was fabricated in 0.18 μm CMOS; electrical characterization and multi-channel FDM results are provided, including GFET-based signal modulation and IC/DSP demodulation. Long-term, this approach can simultaneously enable high signal count, high spatial resolution, and high temporal precision to infer functional interactions between neurons while markedly decreasing access wires.

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an "end-to-end" reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10-5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

Multi-channel transcranial temporally interfering stimulation (tTIS): application to living mice brain

OBJECTIVE

Transcranial temporally interfering stimulation (tTIS) is a noninvasive neuromodulation method, which has been reported to be able to affect the activity of small neuronal populations. To pinpoint smaller regions of the brain, multi-channel tTIS strategy is proposed with larger numbers of electrodes and multiple sets of interfering fields.

APPROACH

First, computational model is adopted to prove the concept of multi-channel tTIS theoretically. Besides, animal experiments are implemented to activate motor cortex neurons in living mice and different frequencies are attempted. Finally, to better understand the envelope modulation properties of the two applied fields, tissue phantom measurement is conducted.

MAIN RESULTS

The focality of six-channel (six electrode pairs) tTIS is increased by 46.7% and 70.2% respectively, compared with that of single-channel tTIS when maximal amplitude value drops by 3dB and 6dB in numerical computation experiment. Furthermore, the focality of multi-channel tTIS is less sensitive to the electrode position. Confirmed with myoelectricity signal, the movement frequencies of contralateral forepaw are consistent with the corresponding difference frequencies. What's more, compared single-channel (one electrode pair) tTIS with multi-channel (three electrode pairs) tTIS, the intensity of multi-channel tTIS stimulation is decreased by 28.5% on average in animal experiment. And the c-fos-positive neurons of target region are significantly higher than that of the non-target region. Results of the modulated envelope distribute around the whole regions and its amplitude reaches a maximum at the interfering region.

SIGNIFICANCE

Both computational modeling and animal experiment validate the feasibility of the proposed multi-channel tTIS strategy and confirm that it can enhance focality and reduce scalp sensation.

Review of MAC Protocols for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) need to support the timely end-to-end transmissions of safety and non-safety messages. Medium access control (MAC) protocols can ensure fair and efficient sharing of channel resources among multiple vehicles for VANETs, which can provide timely packet transmissions and significantly improve road safety. In this paper, we review the standards of some countries for VANETs. Then, we divide the MAC protocols proposed for VANETs into single-channel MAC protocols and multi-channel MAC protocols according to the number of physical occupied spectrum resources. Both are further discussed based on their hierarchical structures, i.e., distributed and centralized structures. General design and optimization mechanisms of these commonly used MAC protocols for VANETs are reviewed. From the viewpoint of 7 aspects, we compare the advantages and disadvantages of these typical MAC protocols. Finally, we discuss the open issues to improve the MAC performance and future work on MAC design for VANETs.

An Adaptive Multi-Channel Cooperative Data Transmission Scheduling in VANETs

The Internet of Vehicle (IoV) technology is one of the most important technologies of modern intelligent transportation. The data transmission scheduling method is a research hotspot in the technology of IoV. It is a challenge to ensure the stability of data transmission due to fast network topology changes, high data transmission delays, and some other reasons. Aiming at the above problems, a multi-channel data transmission cooperative scheduling algorithm is proposed. First, construct a feasible interference map based on the data items sent and received by vehicles in the road scene. Second, assign channels to the nodes in the interference map based on the Signal-to-Interference-Noise-Ratio (SINR). Finally, the optimal multi-channel data transmission cooperative scheduling scheme is achieved through the ISing model. Simulation results show that compared with the traditional algorithm, the network service capacity is increased by about 31% and the service delay is reduced by about 20%. It ensures that emergency data is preferentially transmitted to the target vehicle and the maximum weighted service capacity of the network.

Multi-Channel Exploration of O Adatom on TiO2(110) Surface by Scanning Probe Microscopy

We studied the O₂ dissociated state under the different O₂ exposed temperatures with atomic resolution by scanning probe microscopy (SPM) and imaged the O adatom by simultaneous atomic force microscopy (AFM)/scanning tunneling microscopy (STM). The effect of AFM operation mode on O adatom contrast was investigated, and the interaction of O adatom and the subsurface defect was observed by AFM/STM. Multi-channel exploration was performed to investigate the charge transfer between the adsorbed O and the TiO₂(110) by obtaining the frequency shift, tunneling current and local contact potential difference at an atomic scale. The tunneling current image showed the difference of the tunneling possibility on the single O adatom and paired O adatoms, and the local contact potential difference distribution of the O-TiO₂(110) surface institutively revealed the charge transfer from TiO₂(110) surface to O adatom. The experimental results are expected to be helpful in investigating surface/interface properties by SPM.

Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks

In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed.

Extracellular single-unit recordings from peripheral nerve axons in vitro by a novel multichannel microelectrode array

The peripheral nervous system (PNS) is an attractive target for modulation of afferent input (e.g., nociceptive input signaling tissue damage) to the central nervous system. To advance mechanistic understanding of PNS neural encoding and modulation requires single-unit recordings from individual peripheral neurons or axons. This is challenged by multiple connective tissue layers surrounding peripheral nerve fibers that prevent electrical recordings by existing electrodes or electrode arrays. In this study, we developed a novel microelectrode array (MEA) via silicon-based microfabrication that consists of 5 parallel hydrophilic gold electrodes surrounded by silanized hydrophobic surfaces. This novel hydrophilic/hydrophobic surface pattern guides the peripheral nerve filaments to self-align towards the hydrophilic electrodes, which dramatically reduces the technical challenges in conducting single-unit recordings. We validated our MEA by recording simultaneous single-unit action potentials from individual axons in mouse sciatic nerves, including both myelinated A-fibers and unmyelinated C-fibers. We confirmed that our recordings were single units from individual axons by increasing nerve trunk electrical stimulus intensity, which did not alter the spike shape or amplitude. By reducing the technical challenges, our novel MEA will likely allow peripheral single-unit recordings to be adopted by a larger research community and thus expedite our mechanistic understanding of peripheral neural encoding and modulation.

MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

Unified Channel Management for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a novel unified channel management framework (CMF) is introduced for cognitive radio sensor networks (CRSNs), which comprises an (1) opportunity detector (ODR), (2) opportunity scheduler (OSR), and (3) opportunity ranker (ORR) to specifically address the immense and diverse spectrum requirements of CRSN-aided IoT. The unified CMF is unique for its type as it covers all three angles of spectrum management. The ODR is a double threshold based multichannel spectrum sensor that allows an IoT device to concurrently sense multiple channels to maximize spectrum opportunities. OSR is an integer linear programming (ILP) based channel allocation mechanism that assigns channels to heterogeneous IoT devices based on their minimal quality of service (QoS) requirements. ORR collects feedback from IoT devices about their transmission experience and generates special channel-sensing order (CSO) for each IoT device based on the data rate and idle-time probabilities. The simulation results demonstrate that the proposed CMF outperforms the existing ones in terms of collision probability, detection probability, blocking probability, idle-time probability, and data rate.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Highly Efficient Multi Channel Packet Forwarding with Round Robin Intermittent Periodic Transmit for Multihop Wireless Backhaul Networks

Round Robin based Intermittent Periodic Transmit (RR-IPT) has been proposed which achieves highly efficient multi-hop relays in multi-hop wireless backhaul networks (MWBN) where relay nodes are 2-dimensionally deployed. This paper newly investigates multi-channel packet scheduling and forwarding scheme for RR-IPT. Downlink traffic is forwarded by RR-IPT via one of the channels, while uplink traffic and part of downlink are accommodated in the other channel. By comparing IPT and carrier sense multiple access with collision avoidance (CSMA/CA) for uplink/downlink packet forwarding channel, IPT is more effective in reducing packet loss rate whereas CSMA/CA is better in terms of system throughput and packet delay improvement.

Hybrid Structure Multichannel All-Fiber Current Sensor

We have experimentally developed a hybrid-structure multi-channel all-fiber current sensor with ordinary silica fiber using fiber loop architecture. According to the rationale of time division multiplexing, the sensor combines parallel and serial structures. The purpose of the hybrid-structure multi-channel all-fiber current sensor is to get more information from the different measured points simultaneously. In addition, the hybrid-structure fiber current sensor exhibited a good linear response for each channel. A three-channel experiment was performed in the study and showed that the system could detect different current positions. Each channel could individually detect the current and needed a separate calibration system. Furthermore, the three channels will not affect each other.

Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.

Susceptibility-weighted imaging using inter-echo-variance channel combination for improved contrast at 7 tesla

PURPOSE

To implement and optimize a new approach for susceptibility-weighted image (SWI) generation from multi-echo multi-channel image data and compare its performance against optimized traditional SWI pipelines.

MATERIALS AND METHODS

Five healthy volunteers were imaged at 7 Tesla. The inter-echo-variance (IEV) channel combination, which uses the variance of the local frequency shift at multiple echo times as a weighting factor during channel combination, was used to calculate multi-echo local phase shift maps. Linear phase masks were combined with the magnitude to generate IEV-SWI. The performance of the IEV-SWI pipeline was compared with that of two accepted SWI pipelines-channel combination followed by (i) Homodyne filtering (HPH-SWI) and (ii) unwrapping and high-pass filtering (SVD-SWI). The filtering steps of each pipeline were optimized. Contrast-to-noise ratio was used as the comparison metric. Qualitative assessment of artifact and vessel conspicuity was performed and processing time of pipelines was evaluated.

RESULTS

The optimized IEV-SWI pipeline (σ = 7 mm) resulted in continuous vessel visibility throughout the brain. IEV-SWI had significantly higher contrast compared with HPH-SWI and SVD-SWI (P < 0.001, Friedman nonparametric test). Residual background fields and phase wraps in HPH-SWI and SVD-SWI corrupted the vessel signal and/or generated vessel-mimicking artifact. Optimized implementation of the IEV-SWI pipeline processed a six-echo 16-channel dataset in under 10 min.

CONCLUSION

IEV-SWI benefits from channel-by-channel processing of phase data and results in high contrast images with an optimal balance between contrast and background noise removal, thereby presenting evidence of importance of the order in which postprocessing techniques are applied for multi-channel SWI generation.

LEVEL OF EVIDENCE

2 J. Magn. Reson. Imaging 2017;45:1113-1124.

Optimized Sharable-Slot Allocation Using Multiple Channels to Reduce Data-Gathering Delay in Wireless Sensor Networks

The demand for event-driven real-time applications for timely and reliable data acquisition is growing in industrial sectors. However, it is challenging to satisfy the requirements since constraints such as limited available energy and bandwidth are inherent in a wireless sensor network. To deal with timely delivery, one desirable approach is to improve network throughput so that more real-time applications with tighter time constraints can be satisfied in any given network. To deal with reliable delivery, the use of a carrier sense multiple access mechanism for data transmission is preferred, along with the use of a sharable slot within which multiple nodes compete to send data. Thus, we present a method of using multiple channels and a way to optimize the size of the sharable slot. The proposed channel-slot-scheduling algorithm tries to optimize the size of a sharable slot when multiple channels are used. The algorithm also deals with situations where nodes generate multiple data packets in each round of a data-gathering period. It is shown through simulation that our approach greatly outperforms others on some selected metrics.

YSZ-Reinforced Alumina Multi-Channel Capillary Membranes for Micro-Filtration

The combined phase-inversion and sintering method not only produces ceramic hollow fibre membranes with much lower fabrication costs than conventional methods, but these membranes can also be designed to have greatly reduced transport resistances for filtration processes. The bottleneck of this technique is the weak mechanical property of the fibres, due to the small dimensions and the brittle nature of the ceramic materials. In this study, yttrium stabilised zirconia (YSZ) reinforced alumina seven-channel capillary microfiltration membranes were prepared with a pore size of ~230 nm and their mechanical property and permeation characteristics were studied. It is found that the addition of YSZ can effectively enhance the mechanical property of the membrane and also increase pure water permeation flux. The Al₂O₃-YSZ seven-channel capillary membranes could reach a fracture load of 23.4 N and a bending extension of 0.54 mm when being tested with a 6 cm span, to meet the requirements for most industrial microfiltration applications.

A novel intracavitary applicator design for the treatment of deep vaginal fornices: preliminary dose metrics and geometric analysis

Purpose

To investigate the dose distributions associated with a novel balloon sleeve placed over a standard cylinder applicator.

Material and methods

A computed tomography (CT) scan of a sleeve balloon shaped to inflate into the vaginal fornices was used to digitize 1-, 3-, and 5-catheter configurations. Point doses for rectum, apex, and fornix were calculated and compared to the values associated with a standard cylinder plan not targeting the vaginal fornices. Inflation of the sleeve balloon in the vaginal fornices and dose coverage with constraints to the rectum, bladder, and sigmoid D_2cc were evaluated.

Results

Rectum, apex, and fornix doses were respectively 76%, 119%, and 44% for a standard cylinder; 190%, 310%, and 93% for a 1-catheter configuration; 98%, 109%, and 109% for a 3-catheter configuration; and 91%, 107%, and 96% for the 5-catheter configuration. In a patient analysis, expansion of the sleeve balloon into the vaginal fornices was confirmed. The 5-catheter configurations were associated with best coverage of the fornices and acceptable doses to rectum, bladder, and sigmoid.

Conclusions

A 1-catheter configuration cannot be used clinically due to high rectal and apex dose. In theoretical analysis, the 3- and 5-catheter configurations showed > 96% coverage to the vaginal fornices with a clinically acceptable rectal dose. In a treatment simulation in a patient, a 5-catheter configuration showed 90% coverage of the fornices with acceptable doses to the organs at risk. The treatment of deep vaginal fornices results in an increased rectal dose compared to a standard cylinder plan.

Multi-channel beam-scanning imaging at kHz frame rates by Lissajous trajectory microscopy

A beam-scanning microscope based on Lissajous trajectory imaging is described for achieving streaming 2D imaging with continuous frame rates up to 1.4 kHz. The microscope utilizes two fast-scan resonant mirrors to direct the optical beam on a circuitous trajectory through the field of view. By separating the full Lissajous trajectory time-domain data into sub-trajectories (partial, undersampled trajectories) effective frame-rates much higher than the repeat time of the Lissajous trajectory are achieved with many unsampled pixels present. A model-based image reconstruction (MBIR) 3D in-painting algorithm is then used to interpolate the missing data for the unsampled pixels to recover full images. The MBIR algorithm uses a maximum a posteriori estimation with a generalized Gaussian Markov random field prior model for image interpolation. Because images are acquired using photomultiplier tubes or photodiodes, parallelization for multi-channel imaging is straightforward. Preliminary results show that when combined with the MBIR in-painting algorithm, this technique has the ability to generate kHz frame rate images across 6 total dimensions of space, time, and polarization for SHG, TPEF, and confocal reflective birefringence data on a multimodal imaging platform for biomedical imaging. The use of a multi-channel data acquisition card allows for multimodal imaging with perfect image overlay. Image blur due to sample motion was also reduced by using higher frame rates.

Cutaneous sensory nerve as a substitute for auditory nerve in solving deaf-mutes' hearing problem: an innovation in multi-channel-array skin-hearing technology

The current use of hearing aids and artificial cochleas for deaf-mute individuals depends on their auditory nerve. Skin-hearing technology, a patented system developed by our group, uses a cutaneous sensory nerve to substitute for the auditory nerve to help deaf-mutes to hear sound. This paper introduces a new solution, multi-channel-array skin-hearing technology, to solve the problem of speech discrimination. Based on the filtering principle of hair cells, external voice signals at different frequencies are converted to current signals at corresponding frequencies using electronic multi-channel bandpass filtering technology. Different positions on the skin can be stimulated by the electrode array, allowing the perception and discrimination of external speech signals to be determined by the skin response to the current signals. Through voice frequency analysis, the frequency range of the band-pass filter can also be determined. These findings demonstrate that the sensory nerves in the skin can help to transfer the voice signal and to distinguish the speech signal, suggesting that the skin sensory nerves are good candidates for the replacement of the auditory nerve in addressing deaf-mutes' hearing problems. Scientific hearing experiments can be more safely performed on the skin. Compared with the artificial cochlea, multi-channel-array skin-hearing aids have lower operation risk in use, are cheaper and are more easily popularized.

Multi-channel hyperspectral fluorescence detection excited by coupled plasmon-waveguide resonance

We propose in this paper a biosensor scheme based on coupled plasmon-waveguide resonance (CPWR) excited fluorescence spectroscopy. A symmetrical structure that offers higher surface electric field strengths, longer surface propagation lengths and depths is developed to support guided waveguide modes for the efficient excitation of fluorescence. The optimal parameters for the sensor films are theoretically and experimentally investigated, leading to a detection limit of 0.1 nM (for a Cy5 solution). Multiplex analysis possible with the fluorescence detection is further advanced by employing the hyperspectral fluorescence technique to record the full spectra for every pixel on the sample plane. We demonstrate experimentally that highly overlapping fluorescence (Cy5 and Dylight680) can be distinguished and ratios of different emission sources can be determined accurately. This biosensor shows great potential for multiplex detections of fluorescence analytes.

Multi-channel PMMA microfluidic biosensor with integrated IDUAs for electrochemical detection

A novel multi-channel poly(methyl methacrylate) (PMMA) microfluidic biosensor with interdigitated ultramicroelectrode arrays (IDUAs) for electrochemical detection was developed. The focus of the development was a simple fabrication procedure and the realization of a reliable large IDUA that can provide detection simultaneously to several microchannels. As proof of concept, five microchannels are positioned over a large single IDUA where the channels are parallel with the length of the electrode finger. The IDUAs were fabricated on the PMMA cover piece and bonded to a PMMA substrate containing the microfluidic channels using UV/ozone-assisted thermal bonding. Conditions of device fabrication were optimized realizing a rugged large IDUA within a bonded PMMA device. Gold adhesion to the PMMA, protective coatings, and pressure during bonding were optimized. Its electrochemical performance was studied using amperometric detection of potassium ferri and ferro hexacyanide. Cumulative signals within the same chip showed very good linearity over a range of 0-38 μM (R(2) = 0.98) and a limit of detection of 3.48 μM. The bonding of the device was optimized so that no cross talk between the channels was observed which otherwise would have resulted in unreliable electrochemical responses. The highly reproducible signals achieved were comparable to those obtained with separate single-channel devices. Subsequently, the multi-channel microfluidic chip was applied to a model bioanalytical detection strategy, i.e., the quantification of specific nucleic acid sequences using a sandwich approach. Here, probe-coated paramagnetic beads and probe-tagged liposomes entrapping ferri/ferro hexacyanide as the redox marker were used to bind to a single-stranded DNA sequence. Flow rates of the non-ionic detergent n-octyl-β-D-glucopyranoside for liposome lysis were optimized, and the detection of the target sequences was carried out coulometrically within 250 s and with a limit of detection of 12.5 μM. The robustness of the design and the reliability of the results obtained in comparison to previously published single-channel designs suggest that the multi-channel device offers an excellent opportunity for bioanalytical applications that require multianalyte detection and high-throughput assays.

Feasibility study and optimum loading pattern of a multi-ring inflatable intravaginal applicator

PURPOSE

A cylinder applicator is the standard treatment device for intravaginal brachytherapy. However, they are limited in their ability to simultaneously spare the organs at risk (OAR), and reduce the hot spot in the vaginal mucosa, while achieving adequate dose conformality. This study aims to compare the dosimetric characteristics of single and multi-channel cylinders, and utilizes volume point dose optimizations to investigate the feasibility and optimum loading method for a multi-ring inflatable intravaginal applicator.

MATERIAL AND METHODS

STUDIES WERE DESIGNED TO: (1) test the feasibility of multi-ring applicators, (2) compare dose distributions between different multi-channel applicators and loading patterns, (3) test non-uniform prescription depths around the multi-ring cylinder.

RESULTS

Compared to a cylinder with a single central channel, a cylinder with 6 lumina arranged around the periphery, providing the lumina had adequate distance to the cylinder surface, could reduce dose beyond the prescription depth. However, when the number of outer lumina increased from 6 to 12, no further dose reduction could be achieved and the high dose volume close to the surface of the cylinder increased. Moreover, an additional ring, with lumina further away from the surface, provided increased dose shaping capabilities, allowing for individualized dose distributions.

CONCLUSIONS

Dose could be reduced to normal tissue and the inner mucosa, and better conformity was seen to unique anatomical shapes. A modified peripheral loading pattern provided the optimum dose distribution, yielding good conformity, dose sparing at adjacent organs, and dose reduction in the high dose region of the vaginal mucosa.