Channel Characteristics Research Articles

A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

Structural features of heteromeric channels composed of CALHM2 and CALHM4 paralogs.

The CALHM proteins constitute a family of large pore channels that contains six closely related paralogs in humans. Two family members, CALHM1 and 3, have been associated with the release of ATP during taste sensation. Both proteins form heteromeric channels that activate at positive potential and decreased extracellular Ca2+ concentration. Although the structures of several family members displayed large oligomeric organizations of different size, their function has in most cases remained elusive. Our previous study has identified the paralogs CALHM2, 4 and, 6 to be highly expressed in the placenta and defined their structural properties as membrane proteins exhibiting features of large pore channels with unknown activation properties (Drożdżyk et al., 2020). Here, we investigated whether these placental paralogs would form heteromers and characterized heteromeric complexes consisting of CALHM2 and CALHM4 subunits using specific binders as fiducial markers. Both proteins assemble with different stoichiometries with the largest population containing CALHM2 as the predominant component. In these oligomers, the subunits segregate and reside in their preferred conformation found in homomeric channels. Our study has thus revealed the properties that govern the formation of CALHM heteromers in a process of potential relevance in a cellular context.

Structural features of heteromeric channels composed of CALHM2 and CALHM4 paralogs

The CALHM proteins constitute a family of large pore channels that contains six closely related paralogs in humans. Two family members, CALHM1 and 3, have been associated with the release of ATP during taste sensation. Both proteins form heteromeric channels that activate at positive potential and decreased extracellular Ca2+ concentration. Although the structures of several family members displayed large oligomeric organizations of different size, their function has in most cases remained elusive. Our previous study has identified the paralogs CALHM2, 4 and, 6 to be highly expressed in the placenta and defined their structural properties as membrane proteins exhibiting features of large pore channels with unknown activation properties (Drożdżyk et al., 2020). Here, we investigated whether these placental paralogs would form heteromers and characterized heteromeric complexes consisting of CALHM2 and CALHM4 subunits using specific binders as fiducial markers. Both proteins assemble with different stoichiometries with the largest population containing CALHM2 as the predominant component. In these oligomers, the subunits segregate and reside in their preferred conformation found in homomeric channels. Our study has thus revealed the properties that govern the formation of CALHM heteromers in a process of potential relevance in a cellular context.

FA-VTON: A Feature Alignment-Based Model for Virtual Try-On

The virtual try-on technology based on 2D images aims to seamlessly transfer provided garments onto target person images. Prior methods mainly concentrated on warping garments and generating images, overlooking the influence of feature alignment on the try-on results. In this study, we initially analyze the distortions present by existing methods and elucidate the critical role of feature alignment in the extraction stage. Building on this, we propose a novel feature alignment-based model (FA-VTON). Specifically, FA-VTON aligns the upsampled higher-level features from both person and garment images to acquire precise boundary information, which serves as guidance for subsequent garment warping. Concurrently, the Efficient Channel Attention mechanism (ECA) is introduced to generate the final result in the try-on generation module. This mechanism enables adaptive adjustment of channel feature weights to extract important features and reduce artifact generation. Furthermore, to make the student network focus on salient regions of each channel, we utilize channel-wise distillation (CWD) to minimize the Kullback–Leibler (KL) divergence between the channel probability maps of the two networks. The experiments show that our model achieves better results in both qualitative and quantitative analyses compared to current methods on the popular virtual try-on datasets.

The Recognition of Aerobics Movements Using Bone Data Combined with ST-GCN

To solve the aerobics action recognition and promote the gradual intelligence and standardization of aerobics teaching and evaluation, a network model based on spatial temporal graph convolution and combined with attention mechanism was proposed. This model improved the extraction efficiency of spatiotemporal features and channel features by introducing spatiotemporal graph and channel attention mechanisms, respectively, thereby improving the accuracy of action recognition. And a time extension module was introduced into each basic module, and additional features between adjacent vertices were extracted by extending the time graph between frames. These experiments confirmed that this model exhibited high accuracy in identifying aerobics movements. The recognition accuracy of basic actions was above 93.6%, and the recognition accuracy of two actions had reached 98.4% and 98.6% respectively. For advanced actions, the recognition accuracy of the model had slightly decreased, but the average value was still above 95%. The accuracy of difficult motion recognition had also achieved good results, reaching a maximum of 94.5%. These data indicate that this model can achieve high accuracy in handling action recognition tasks of different difficulty levels, and can identify aerobics movements of different difficulty levels.

Hyperspectral Image Classification Based on Multi-Scale Convolutional Features and Multi-Attention Mechanisms

Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance.

Investigation on a lightweight defect detection model for photovoltaic panel

The detection of defect types of photovoltaic (PV) panel is a crucial task in PV system. Existing detection models face challenges in effectively balancing the trade-off between detection accuracy and resource consumption. To address this issue, this paper proposes a new defect detection method for PV panel based on the improved YOLOv8 model, which realizes both the high detection accuracy and the lightweight. Firstly, Reversible Column Networks (RevCol) is used as the Backbone of YOLOv8, which makes sure to preserve the feature information in the process of network transmission and also reduces the number of parameters and Giga floating-point operations per second (GFLOPs). Subsequently, a new lightweight Bottleneck fused with Efficient Multi-Scale Attention (EMA) is designed to optimize the CSPDarknet53 to 2-Stage FPN (C2f) module of Neck in YOLOv8 to enhance the robustness and further decrease network parameters. Finally, Squeeze-and-Excitation (SE) Attention is integrated into the Head of YOLOv8 to prioritize the important channel features and thus enhance the detection performance. The experimental results on the PVEL-AD dataset demonstrate that parameters and GFLOPs of the proposed model are declined by 38.46% and 34.39% respectively, and mAP0.5:0.95 is increased by 2.6% compared with the baseline model. The lightweight improved YOLOv8 model facilitates the deployment of deep learning model on edge devices and provides a novel approach for the online detection of PV panel defects.

Channel-Enhanced Contrastive Cross-Domain Sequential Recommendation

Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness problem, cross-domain sequential recommendation (CDSR) are proposed. CDSR explore rich data from a source domain to improve performance of the target domain. However, most of the existing CDSR methods are difficult to capture the temporal context of sequences and only learn user preference based on interactions of single domain, which leads to suboptimal performance. To address these shortcomings, we propose a channel-enhanced contrastive cross-domain sequential recommendation model (C3DSR). To be specific, (1) we design a feature extractor, which extends attention to the channel dimension, to extract the user’s channel feature and capture the temporal contextual relationships between sequences. Then we calculate the weights of each channel by using three SE-Res2Blocks and multiply it with the channel feature to obtain user preference. (2) We concatenate the user’s single-domain representation, the cross-domain representation, and the user features to make CDSR. Contrastive learning is leveraged to enhance mutual information between two domains. Experimental results show that the proposed model achieves the significant improvement of performance compared with other CDSR models on Amazon and HVIDEO datasets.

A Quantum Technology for Reinforcement Learning on Channel Assignment

AbstractProspective application of quantum technologies for reinforcement learning (RL) is an exciting surge in quantum fields. Quantum random number generators (QRNGs) produce high‐frequency random bits, with advantages of true randomness and device independence over pseudo‐random numbers. This work explores a new approach, called the quantum random numbers for multi‐armed bandit (QRN‐MAB) algorithm, for multi‐user access in wireless communication system upon random bits. The primary objective of the algorithm is to attain a stable assignment state without further exchange. QRN‐MAB utilizes random bits to learn channel features and conducts concurrent exchange to attain stability. This work finds that the intrinsic randomness property used in QRN‐MAB enables arrangement to maintain high accuracy and outperforms other classical algorithms. Additionally, the algorithm exhibits strong adaptability when the environment changes over time and quantum random numbers are advantageous over other pseudo‐random methods in achieving the target. This work provides an effective way for quantum technologies applications in RL and unfolds a promising avenue to stabilize assignment among multiple users.

Effects of bidirectional rib arrangements on turbulent flow structure and heat transfer characteristics of a two-pass channel for turbine blade internal cooling

The thermal efficiency of gas turbine engines depends heavily on the cooling performance of the internal channels of turbine blades. In the present study, the effects of bidirectionally arranged ribs on the flow and heat transfer mechanism in a two-pass cooling channel are investigated numerically. The channel aspect ratio is kept constant at 1:2 (W: H), and the rib pitch-to-height ratio (P/e) is 10. Comparative analysis is performed for three different cross-sectioned (Square, triangular and Curved) ribs with Reynolds numbers ranging from 10,000-50,000. The results indicate that the bidirectional ribbed channel provided 69% better thermal performance compared to horizontal-only ribs due to its potential to induce secondary flow in both vertical and horizontal directions. The square and triangular ribs show similar characteristics, but the thermal performance of curved ribs drops by 11%. The pressure loss phenomenon is also lowest in the case of the curved ribs. The obtained Nusselt number and friction factor for all cases are normalized with the Dittus-Boelter correlation, Blasius correlation and the smooth channel. The detailed analysis of area-averaged normalized Nusselt number indicates that the best thermal performance occurs at the bend region due to the combined contributions of the ribs and the 180° turn. Furthermore, in terms of the overall thermohydraulic characteristics, the curved-bidirectional ribbed channel is noticed to have the best results with values of 1.38 and 1.25 for TEFo and TEFs respectively.

Application of latent variable evolution for channel reservoir characterization using generative adversarial networks and particle swarm optimization

When characterizing channel reservoirs, ensemble-based methods may compromise channel features due to the conflict between the Gaussian distribution assumption of the methods and bimodality of channel reservoirs. To overcome this challenge, one effective approach involves leveraging generative models. Specifically, a generative model trained with channel reservoir models has ability to generate bimodally distributed values from latent vectors whose elements are intentionally designed to conform a Gaussian distribution. Therefore, it resolves the conflict, allowing the ensemble-based methods to update the latent vectors.However, generating initial latent vectors randomly poses an additional challenge and can lead to a suboptimal quality of the initial ensemble. They can significantly influence the unreliability of characterizations, primarily because their impact is strongly tied to the quality of the initial models in the ensemble-based methods.To address this challenge, we propose a novel scheme for channel reservoir characterization using Latent Variable Evolution. In this study, we adopt Generative Adversarial Networks (GAN) and Particle Swarm Optimization (PSO). PSO explores the latent space of GAN trained with channel reservoirs in order to find ones that generate models resembling a true reservoir. Validation on simple and complex reservoir cases demonstrates our proposed method can efficiently provide reliable channel reservoir characterization regardless of the quality of the initial latent vectors. These results are compared to Ensemble Smoother with Multiple Data Assimilation with GAN.

Applying seismic sedimentology to characterize the architecture of a fan delta complex: Triassic Baikouquan formation, Mahu Sag, Junggar Basin, western China

Reservoir architecture is often heterogeneous and can affect the migration and accumulation of hydrocarbons. The Triassic Baikouquan Formation in the Mahu oilfield presents a unique opportunity to use seismic data for fan delta channel and lobe belt characterization in an area with a sparse well spacing of about 800–1200 m. The Baikouquan Formation comprises coarse-grained lacustrine fan delta units deposited by traction and debris flows. The occurrence, distribution, and morphology were investigated using petrography, frequency spectrum decomposition, stratal slices, seismic forward modeling, and wireline logs. Fan-shaped lobe complexes and distributary channel complexes (sixth-order architectural units) exhibiting relatively large lateral and vertical variation were identified from 45- and 60-Hz seismic profiles. Channel and fan-shaped lobe belts (fifth-order architectural units) were investigated by integrating provenances, architectural elements from individual wells, the ratio of conglomerate to formation, cross section analysis, and seismic stratal slices. Four channel belts and three fan-shaped lobe belts were recognized, occurring in six evolutionary stages. It was discovered that these channel and lobe belts had distinct geomorphological traits. Reservoir architecture and distribution was governed by paleogeomorphology, fluctuations in accommodation space, and sediment supply. The identified fifth-order architectural units are useful for reservoir modeling and contribute to remaining oil development. This study indicates the suitability of seismic sedimentology for characterizing reservoir architecture in areas with sparse well spacing.

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

Genetic and immune identification and functional analysis of TRPM8 as a potential biomarker for pancreatic adenocarcinoma proliferation.

Pancreatic adenocarcinoma (PAAD), a member of highly lethal malignant tumors, has a poor outcome and extremely poor prognosis. The transient receptor potential (TRP) superfamily, a group of nonselective cation channels, is capable of influencing cellular functions by regulating calcium homeostasis. In addition, it has been shown that TRP channels can also affect various cellular phenotypes by regulating gene transcription levels and are involved in the development of a variety of malignant tumors. In order to find new therapeutic targets and biomarkers to improve the clinical prognosis of pancreatic cancer, we performed genetic and immunological characterization of TRP channels in PAAD, as well as related functional and prognostic analyses. We investigated the expression, genetic alterations, methylation levels, and immune infiltration levels of TRP channels in PAAD, and further also analyzed the function of TRP channels in PAAD and their prognostic value for PAAD patients. Our results suggest that TRPM8 may contribute to tumor proliferation by controlling the PI3K-AKT-mTOR signaling pathway in PAAD. After careful evaluation of the accumulated data, we concluded that TRPM8 has potential as a prognostic indicator and prospective therapeutic target in PAAD.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

Channel Measurement, Characterization, and Modeling for Terahertz Indoor Communications Above 200 GHz

Channel Measurement, Characterization, and Modeling for Terahertz Indoor Communications Above 200 GHz

14‐3: Late‐News Paper: Channel Etched Coplanar TFTs for Applying a‐IGZO to High‐resolution and IT Applications

Self‐aligned a‐IGZO coplanar TFTs have high mobility, excellent large‐area uniformity, and are structurally free of parasitic caps. Accordingly, it is being adopted and mass‐produced as the backplane of OLED TV. Currently, the scope of application of OLED displays using oxide TFT backplanes is expanding to high‐resolution monitors for gaming. With this expansion, not only the size of the TFT but also its storage capacity has become an important factor in terms of aperture ratio. Instead of using a thick Passivation (PAS) Layer for storage capacity, we developed a structure that maximizes the aperture ratio by using a thin gate insulator (GI). However, GI capacitance must be used as the gate electrode and active layer electrode, but in self‐aligned coplanar structure, the active layer overlapping the gate metal is not conductive. To solve this problem, we succeeded in developing a 27‐inch QHD OLED monitor product for gaming by applying a self‐aligned coplanar structure/process that allows the active layer to simultaneously configure channels and wiring, and controlling degradation of channel characteristics.

Model‐driven neural network based for HPO‐MIMO channel estimation

AbstractIntegrated Sensing and Communications (ISAC) need to process data streams in high‐speed sensor data acquisition or high‐speed wireless communications. To process the data can require more computing and communication resources, resulting in higher power consumption. Halved‐Phase Only Multiple Input Multiple Output (HPO‐MIMO) communication technology can solve this problem by using low‐power nonlinear detection devices. In ISAC, Channel Estimation (CE) technology can provide key channel characteristics and state information for sensing and collaborative work of perception and communication tasks. However, HPO‐MIMO system cannot realize CE using traditional receiver schemes because of the missing amplitude. In order to solve this problem, two HPO‐MIMO CE schemes based on model‐driven deep learning are proposed in this paper. The proposed schemes include a Densely Residual Network (DRN) and a Inception‐Resnet (IR), which is suitable for the case of sufficient data and insufficient data, respectively. The simulation results show that the performance of DRN based scheme is better than that of IR based scheme when the data amount is sufficient, and the performance of IR based scheme is better when the dataset is small. In addition, the proposed CE schemes work well with a range of antenna sizes and distances.

Longitudinal Recovery of Suspended Sediment Downstream of Large Dams in the US

AbstractDams disrupt the natural flow of water and sediment along rivers. Reservoirs trap a significant amount of sediment, which substantially alters downstream hydrology, channel morphology, and sediment transport capacity. The longitudinal recovery of suspended sediment concentration (SSC) along rivers is potentially a new metric for estimating downstream responses to dams over space, rather than time, but is rarely quantified due to the lack of spatial SSC data. Satellites can estimate SSC along rivers where no field data exist and provide a high enough spatial resolution for assessing downstream recovery at the scale of tens to hundreds of kilometers. Here, we use a recently published database of spatially explicit SSC observations derived from Landsat to quantify if a river recovers or not, the SSC recovery percentage, and SSC recovery length downstream of large dams across the Contiguous United States (CONUS). Rivers recover SSC downstream of most dams (71%). The chance of a river recovering and the length of river required to recover SSC is associated primarily with the size of the reservoir (mean storage, km3) and the size of the river (mean discharge, m3/s). Rivers were more likely to recover SSC downstream of run‐of‐river, navigation dams compared to large storage or hydropower dams. Our results suggest that rivers typically recover suspended sediment downstream of dams, influenced by factors like dam storage, purpose, and river channel characteristics.

Ionospheric Channel Impulse Response Measurement System for NVIS Propagation Mode Over Java Island Based on Low‐Cost SDR Platform

AbstractThe development of a digital high‐frequency (HF) radio communication system requires an ionospheric channel model from the channel impulse response (CIR) measurement. Although the Watterson ionosphere channel model has been available and used for a long time, several CIR measurements have been conducted in all regions of the Earth in an attempt to validate or replace the Watterson channel model with a suitable model for their region. However, only a few CIR measurements were conducted in low‐latitude regions, especially over Indonesia. In this study, we develop the CIR measurement system for the near vertical incidence skywave (NVIS) propagation mode over Java Island based on the software defined radio platform to meet low‐cost and simple operation requirements. The specification of the system is based on the International Telecommunication Union ionospheric channel characteristic document and increased in order to be able to capture higher values. Results from a 1‐week campaign measurement period show the ability of the system to measure the root mean square of time delay within the range of 0.2–1.3 ms and the Doppler shift within the range of 0.7–1.1 Hz in the quiet conditions of the ionosphere. Further measurements will be conducted to obtain a comprehensive ionosphere CIR that is useful for designing the NVIS‐HF digital communication in Indonesia, which is located beneath the crest region of an equatorial ionospheric anomaly.