Higher-order Channels Research Articles

Meander Morphologic Characteristics in an Alluvial Channel

Meander bends are one of the most common morphological features of rivers. Their formation is the result of interactions between bank erosion and several other river characteristics, such as the bed topography resulting from bank erosion, sinuosity, channel flow, and riffle-pool sequencing. Many studies have emphasized proportional relationships between channel widths and different aspects of meander morphology, such as wavelength, curvature of the radius, and belt width. These relationships have been studied for bankfull level width in high-order channels. It is not clear how such relationships vary in low-order headwater streams. This research addresses those gaps in low-order streams of which little is known. Field surveys were conducted in nine meander bends of a low-order alluvial channel in Virginia. The results suggest that in a low-order channel, the ratios of width with wavelength, the radius of curvature, and belt width are mostly similar between the meanders’ beginning and tail endpoints. The stream also has similar meander morphologic characteristics and ratios as anticipated from the literature on high-order streams.

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation.

Automatic segmentation of corneal stromal cells can assist ophthalmologists to detect abnormal morphology in confocal microscopy images, thereby assessing the virus infection or conical mutation of corneas, and avoiding irreversible pathological damage. However, the corneal stromal cells often suffer from uneven illumination and disordered vascular occlusion, resulting in inaccurate segmentation. In response to these challenges, this study proposes a novel approach: a nnUNet and nested Transformer-based network integrated with dual high-order channel attention, named U-NTCA. Unlike nnUNet, this architecture allows for the recursive transmission of crucial contextual features and direct interaction of features across layers to improve the accuracy of cell recognition in low-quality regions. The proposed methodology involves multiple steps. Firstly, three underlying features with the same channel number are sent into an attention channel named gnConv to facilitate higher-order interaction of local context. Secondly, we leverage different layers in U-Net to integrate Transformer nested with gnConv, and concatenate multiple Transformers to transmit multi-scale features in a bottom-up manner. We encode the downsampling features, corresponding upsampling features, and low-level feature information transmitted from lower layers to model potential correlations between features of varying sizes and resolutions. These multi-scale features play a pivotal role in refining the position information and morphological details of the current layer through recursive transmission. Experimental results on a clinical dataset including 136 images show that the proposed method achieves competitive performance with a Dice score of 82.72% and an AUC (Area Under Curve) of 90.92%, which are higher than the performance of nnUNet. The experimental results indicate that our model provides a cost-effective and high-precision segmentation solution for corneal stromal cells, particularly in challenging image scenarios.

Probing Synergistic High-Order Interaction for Multi-modal Image Fusion.

Multi-modal image fusion aims to generate a fused image by integrating and distinguishing the cross-modality complementary information from multiple source images. While the cross-attention mechanism with global spatial interactions appears promising, it only captures second-order spatial interactions, neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap, we introduce a Synergistic High-order Interaction Paradigm (SHIP), designed to systematically investigate spatial fine-grained and global statistics collaborations between the multi-modal images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication, mathematically equivalent to global interactions, and then foster high-order formats by iteratively aggregating and evolving complementary information, enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean), we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. We further introduce an enhanced version of the SHIP model, called SHIP++ that enhances the cross-modality information interaction representation by the cross-order attention evolving mechanism, cross-order information integration, and residual information memorizing mechanism. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies, leading in superior performance over state-of-the-art alternatives, as shown through comprehensive experiments across various benchmarks in two significant multi-modal image fusion tasks: pan-sharpening, and infrared and visible image fusion. The source code is publicly available at https://github.com/manman1995/HOIF.

Functionally Distinct Circuits Are Linked by Heterocellular Electrical Synapses in the Thalamic Reticular Nucleus.

The thalamic reticular nucleus (TRN) inhibits sensory thalamocortical relay neurons and is a key regulator of sensory attention as well as sleep and wake states. Recent developments have identified two distinct genetic subtypes of TRN neurons, calbindin-expressing (CB) and somatostatin-expressing (SOM) neurons. These subtypes differ in localization within the TRN, electrophysiological properties, and importantly, targeting of thalamocortical relay channels. CB neurons send inhibition to and receive excitation from first-order thalamic relay nuclei, while SOM neurons send inhibition to and receive excitation from higher-order thalamic areas. These differences create distinct channels of information flow. It is unknown whether TRN neurons form electrical synapses between SOM and CB neurons and consequently bridge first-order and higher-order thalamic channels. Here, we use GFP reporter mice to label and record from CB-expressing and SOM-expressing TRN neurons. We confirm that GFP expression properly differentiates TRN subtypes based on electrophysiological differences, and we identified electrical synapses between pairs of neurons with and without common GFP expression for both CB and SOM types. That is, electrical synapses link both within and across subtypes of neurons in the TRN, forming either homocellular or heterocellular synapses. Therefore, we conclude that electrical synapses within the TRN provide a substrate for functionally linking thalamocortical first-order and higher-order channels within the TRN.

Blind super-resolution model based on unsupervised degenerate indication learning

Blind super-resolution of images based on contrast learning has achieved better performance, which can distinguish between different degraded information. However, there is no significant improvement in the processing of image super-resolution. To further improve the performance of image blind super-resolution, a new degenerate higher-order attention network (DHAN) is designed for image super-resolution, in which a higher-order channel attention mechanism is introduced to the framework. Meanwhile, it also introduces normalized variance and global covariance pooling, which can provide higher-order feature information and more discriminative feature information. Comparing the experimental results, it can be found that the proposed higher-order attention network (DHAN) is able to distinguish various complex degradation patterns and thus obtain accurate degradation information. Numerous experiments have shown that higher-order attention network (DHAN) achieves good visual results in the image blind super-resolution task.

Guiding of Laguerre–Gaussian pulses in high-order plasma channels

In laser wakefield accelerators, guiding of drive laser pulses in preformed plasma channels plays a key role to overcome laser diffraction for effective acceleration. Different from guiding schemes studied previously, where a Gaussian laser pulse and a parabolic plasma channel were investigated, here we investigate the guiding of Laguerre–Gaussian (LG) pulses in plasma channels. Analytical studies and three-dimensional particle-in-cell simulations show that the matched conditions still exist for high order laser pulses and high order plasma channels. For usual Gaussian and high order LG pulses, the second order parabolic channel gives the best guiding. Although the laser pulse can also be guided in even higher order channels, its envelope deforms during propagation. For laser pulses with combined multi-LG modes, determined by their azimuthal orbit angular momenta, there is axisymmetric or non-axisymmetric evolution for the transverse laser intensity profile. The preformed plasma channel can guide the combined pulses but the transverse intensity profile of the laser pulses always evolves.

Non-linear perceptual multi-scale network for single image super-resolution

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and achieved remarkable progress. However, most of the existing CNN-based SISR networks with a single-stream structure fail to make full use of the multi-scale features of low-resolution (LR) image. While those multi-scale SR models often integrate the information with different receptive fields by means of linear fusion, which leads to the redundant feature extraction and hinders the reconstruction performance of the network. To address both issues, in this paper, we propose a non-linear perceptual multi-scale network (NLPMSNet) to fuse the multi-scale image information in a non-linear manner. Specifically, a novel non-linear perceptual multi-scale module (NLPMSM) is developed to learn more discriminative multi-scale feature correlation by using high-order channel attention mechanism, so as to adaptively extract image features at different scales. Besides, we present a multi-cascade residual nested group (MC-RNG) structure, which uses a global multi-cascade mechanism to organize multiple local residual nested groups (LRNG) to capture sufficient non-local hierarchical context information for reconstructing high-frequency details. LRNG uses a local residual nesting mechanism to stack NLPMSMs, which aims to form a more effective residual learning mechanism and obtain more representative local features. Experimental results show that, compared with the state-of-the-art SISR methods, the proposed NLPMSNet performs well in both quantitative metrics and visual quality with a small number of parameters.

Experiments on the deterministic and non-deterministic responses of lacustrine deltaic channels to water inflow

ABSTRACT The stochastic characteristics of river-delta systems pose large uncertainties for the environmental management of deltas. This study explored the use of two sets of individually repeated physical modelling experiments, to tentatively investigate the deterministic and non-deterministic responses of lacustrine deltaic channels to upstream water inflow. Results indicated that the single-main channel length increased positively with discharge, and was inevitably accompanied by the non-positive changing of both the fluctuation characteristics of the relative position of the bifurcating node for different-order branching channels, and the average period of all the geometrical indicators of higher-order branching channels in the middle delta; the major non-deterministic highlights were the positive and negative correlations of average period and average value for the relative position of the bifurcating node and the branching channel length, respectively, in the upper delta under the low-discharge conditions. These findings refresh our understanding of bifurcation instability and lay the foundation for stochastic modelling of lacustrine-delta morphology.

Debris flow initiation from ravel-filled channel bed failure following wildfire in a bedrock landscape with limited sediment supply

AbstractSteep, rocky landscapes often produce large sediment yields and debris flows following wildfire. Debris flows can initiate from landsliding or rilling in soil-mantled portions of the landscape, but there have been few direct observations of debris flow initiation in steep, rocky portions of the landscape that lacdk a thick, continuous soil mantle. We monitored a steep, first-order catchment that burned in the San Gabriel Mountains, California, USA. Following fire, but prior to rainfall, much of the hillslope soil mantle was removed by dry ravel, exposing bedrock and depositing ∼0.5 m of sandy sediment in the channel network. During a one-year recurrence rainstorm, debris flows initiated in the channel network, evacuating the accumulated dry ravel and underlying cobble bed, and scouring the channel to bedrock. The channel abuts a plowed terrace, which allowed a complete sediment budget, confirming that ∼95% of sediment deposited in a debris flow fan matched that evacuated from the channel, with a minor rainfall-driven hillslope contribution. Subsequent larger storms produced debris flows in higher-order channels but not in the first-order channel because of a sediment supply limitation. These observations are consistent with a model for post-fire ravel routing in steep, rocky landscapes where sediment was sourced by incineration of vegetation dams—following ∼30 years of hillslope soil production since the last fire—and transported downslope by dry processes, leading to a hillslope sediment-supply limitation and infilling of low-order channels with relatively fine sediment. Our observations of debris flow initiation are consistent with failure of the channel bed alluvium due to grain size reduction from dry ravel deposits that allowed high Shields numbers and mass failure even for moderate intensity rainstorms.

Water flow regulator for irrigation canals

A distinctive feature of the irrigated systems of the Russian Federation is a large area of irrigated areas and a significant length of irrigation canals. With a large area of the irrigated massif on small canals, the use of electricity to regulate the throughput of network hydraulic structures is economically ineffective. Preference is given to means of hydraulic automation of water supply. Regulation of the throughput of a hydraulic structure is based on the laws of fluid flow within it. On irrigation canals in Russia, as in world practice, water-operated gates are widely used. One of the disadvantages of such gates is moving metal parts and sensors, which reduce the operational reliability of structures. A new regulator of throughput is proposed, the action of which is based on the injection effect. Compression of the flow by physical elements was replaced by the circulation of surplus water supply between the outlet section of the water supply structure and the downstream. The regulator is built into the pressure drop between the high and low order channels. Regulating the throughput of the tubular water outlet automatically begins after shutting down one or more sprinklers that take water from the lower order canal. After turning the sprinklers into operation, the regulator automatically restores the original throughput. Using the theory of jet pumps, a new method has been developed for the theoretical determination of the main hydraulic characteristics of the regulator. These include the size of the nozzle and the velocities of the injection and injection streams. The derivation of theoretical dependencies was based on the classical equations of fluid mechanics; the flow within the structure was considered quasi-one-dimensional. The obtained calculated dependencies were verified using numerical and physical modeling. The data of the physical and numerical experiment were in good agreement with the theoretical dependences. Further optimization of the controller can be performed by changing its geometric parameters.

How Does Spartina alterniflora Invade in Salt Marsh in Relation to Tidal Channel Networks? Patterns and Processes

Rapid invasion of Spartina alterniflora in coastal wetlands throughout the world has attracted much attention. Some field and imagery evidence has shown that the landward invasion of S. alterniflora follows the tidal channel networks as the main pathway. However, the specific patterns and processes of its invasion in salt marshes in relation to tidal channel networks are still unclear. Based on yearly satellite images from 2010 to 2018, we studied the patterning relationship between tidal channel networks and the invasion of S. alterniflora at the south bank of the Yellow River Estuary (SBYRE). At the landscape (watershed and cross-watershed) scale, we analyzed the correlation between proxies of tidal channel network drainage efficiency (unchanneled flow lengths (UFL), overmarsh path length (OPL), and tidal channels density (TCD)) and spatial distribution of S. alterniflora. At the local (channel) scale, we examined the area and number of patches of S. alterniflora in different distance buffer zones outward from the tidal channels. Our results showed that, overall, the invasion of S. alterniflora had a strong association with tidal channel networks. Watershed with higher drainage efficiency (smaller OPL) attained larger S. alterniflora area, and higher-order (third-order and above) channels tended to be the main pathway of S. alterniflora invasion. At the local scale, the total area of S. alterniflora in each distance buffer zones increased with distance within 15 m from the tidal channels, whereas the number of patches decreased with distance as expansion stabilized. Overall, the S. alterniflora area within 30 m from the tidal channels remained approximately 14% of its entire distribution throughout the invasion. The results implicated that early control of S. alterniflora invasion should pay close attention to higher-order tidal channels as the main pathway

Channel Equalization With Expectation Propagation at Smoothing Level

In this paper we propose a novel turbo equalizer based on the expectation propagation (EP) algorithm. Optimal equalization is computationally unfeasible when high-order modulations and/or large memory channels are used. In these scenarios, low-cost and suboptimal equalizers, such as those based on the linear minimum mean square error (LMMSE), are commonly used. The LMMSE-based equalizer can be efficiently implemented with a Kalman smoother (KS), i.e., a forward and backward Kalman filtering whose predictions are merged in a posterior smoothing step. Recently, it was shown that applying EP at the forward and backward stages of a KS equalizer could significantly improve its performance. In this paper, we investigate applying EP at the smoothing level instead. Also, we propose some further modifications to better exploit the information coming from the channel decoder in turbo equalization schemes. Overall, we remarkably reduce the computational complexity while highly improving the performance in terms of bit error rate.

Interaction and Entanglement of a Pair of Quantum Emitters near a Nanoparticle: Analysis beyond Electric-Dipole Approximation.

In this paper, we study the collective effects which appear as a pair of quantum emitters is positioned in close vicinity to a plasmonic nanoparticle. These effects include multipole–multipole interaction and collective decay, the strengths and rates of which are modified by the presence of the nanoparticle. As a result, entanglement is generated between the quantum emitters, which survives in the stationary state. To evaluate these effects, we exploit the Green’s tensor-based quantization scheme in the Markovian limit, taking into account the corrections from light–matter coupling channels higher than the electric dipole. We find these higher-order channels to significantly influence the collective rates and degree of entanglement, and in particular, to qualitatively influence their spatial profiles. Our findings indicate that, apart from quantitatively modifying the results, the higher-order interaction channels may introduce asymmetry into the spatial distribution of the collective response.

Rainfall-induced landslides and debris flows in Mengdong Town, Yunnan Province, China

On 2 September 2018, an intense rainstorm swept Mengdong Town in Yunnan Province of China, inducing a serious landslide and debris flow disaster with 10 deaths and 11 missing. Image interpretation, field survey, and slope stability analysis were used to examine the characteristics and initiation mechanism of this hazard. A total of 1774 landslide scars were identified, the area of which occupied 8.26% of the study region. Due to the spatial inhomogeneity of precipitation, these scars mainly concentrated along valleys in the lower part of the study area. Besides, well-forested hillslopes were more prone to landslides, indicating the limited role of trees in stabilizing slopes in extreme rainfall events. The initiation of landslides is mainly attributed to the week cohesion of the saturated clayey sand beneath the root zone, where the tensile resistance of roots was absent, and the increase of positive pore water pressure. Additionally, 288 landslide scars were situated adjacent to roads, demonstrating that the road construction activity had intensified the landslide disaster. Owing to the relatively low mobility of landslides, a considerable portion of landslide debris deposited on the valley floor in smaller watersheds (< 6.03 hm(2)), while the remaining portion entered high-order channels. In these channels, where stream power was relatively large, the woody debris and soil carried by landslides were entrained by streamflow, and debris flows were formed. Moreover, the magnitude of debris flow was amplified by the vertical and lateral erosion of the stream channel.

Design of the equaliser with lower-order Chebyshev polynomial for bandwidth expansion of transmission channel of logging cable

ABSTRACTThis paper introduces the logging cable equaliser. In logging engineering, the seven-core cable has been widely used as transmission medium, which has a narrower effective bandwidth. In order to improve the data transfer rate, it is necessary to expand the effective bandwidth. We have proposed a design method of equaliser based on Chebyshev polynomial in this paper. This scheme could compensate the high-order channel attenuation and expand the effective width with lower-order Chebyshev polynomials. The equaliser could also compensate the phase shift of the transfer data which is caused by the non-linear characteristic of logging cable. We have also simulated the scheme with Matlab and produced the circuit board to demonstrate the practical application effect. It is proved that for the non-linear logging transmission channel, the proposed scheme has a good effect on the expansion of the effective bandwidth. It could provide enough bandwidth to enhance the data transmission rate.

Single‐channel blind source separation for paired carrier multiple access signals

Paired carrier multiple access (PCMA) is one of the most common single-channel mixtures. It is still a great challenge to recover the transmitted bits from non-cooperative received PCMA signals, due to the high complexity of existing single-channel blind source separation (SCBSS) algorithms. Hence, a double-direction delayed-decision-feedback sequence estimation (DD-DDFSE) algorithm for non-causal high-order channel is proposed to realise the separation of PCMA signals. The proposed algorithm employs the Viterbi algorithm (VA) with low-order channel twice instead of conventional VA with high-order channel once. The symbols estimated by the initial VA are utilised to equalise the causal and non-causal taps of the channel beyond the trellis state in the second VA. The relationship between the decision-feedback error from the initial VA and the performance of the second VA is also derived. Compared with state-of-the-art maximum likelihood sequence estimation algorithms, DD-DDFSE algorithm can not only decrease the computation complexity but also improve the separation performance.

Reflectionless Adaptive RF Filters: Bandpass, Bandstop, and Cascade Designs

A class of frequency-reconfigurable input-reflectionless/absorptive RF/microwave filters is presented. They consist of tunable complementary-duplexer architectures that are composed of a main and an auxiliary channel with opposite filtering transfer functions. By loading the auxiliary channel with a reference-impedance resistor and by taking the output node of the main channel as the output terminal of the overall circuit, a filtering network of the same type of the main channel with theoretically perfect input-reflectionless behavior at all frequencies can be realized. This technique can be applied to design spectrally agile completely input-reflectionless filters with any kind of transfer function, such as low-pass, high-pass, and single/multiband bandpass/bandstop filters. The theoretical analysis of the first-order absorptive bandpass/bandstop filtering sections based on a coupling-matrix formulation is detailed. Furthermore, the synthesis of high-selectivity reflectionless filters either by cascading multiple first-order cells or using high-order channels in a single complementary duplexer is also described. For practical-demonstration purposes, frequency-tunable lumped-element and microstrip prototypes are manufactured and characterized. They correspond to first- and second-order bandpass/bandstop filters. In addition, their in-series cascade connection is used to implement a bandpass filter with spectrally controllable passband and out-of-band notches.

Air-photo based change in channel width in the Minnesota River basin: Modes of adjustment and implications for sediment budget

The Minnesota River and major tributaries have experienced large increases in discharge over the past century. Aerial photograph-based measurements of channel width were made for the 1938–2015 period at 16 multibend subreaches by digitizing the area between vegetation lines and dividing by centerline length. Results show considerable increases in width for the main stem (0.62±0.10%/y) and major tributaries (0.31±0.08%/y) but are inconclusive for smaller channels (width<25m). Width change for a 146.5-km reach of the lower Minnesota River between 1938 and 2008 is similar to that from the subreach-scale analysis. Widening was associated with lateral centerline movement and temporal change in at-a-station hydraulic geometry for water surface width, indicating that widening is associated with cross-sectional change and not simply upward movement of the vegetation line. Digital elevation model analysis and regional hydraulic geometry show that the main stem and larger tributaries account for the vast majority (~85%) of bankfull channel volume. High-order channels are thus disproportionately responsible for sediment production through cross section enlargement, although floodplains or off-channel water bodies adjacent to these channels likely represent important sediment sinks. Because channel enlargement can play an important role in sediment production, it should be considered in sediment reduction strategies in the Minnesota River basin and carefully evaluated in other watersheds undergoing long-term increases in discharge.

On the Spectral Efficiency Limits of an OAM-Based Multiplexing Scheme

As reported in several recent publications, a spatial multiplexing involving the transmission of orthogonal waves carrying Orbital Angular Momentum (OAM) is unable to provide spectral efficiency improvements with respect to the conventional techniques. In this work we emphasize how the limits of an OAM multiple transmission between antenna arrays can be derived from the Shannon capacity formula, taking as a reference the performance of a multiplexing method based on the higher-order channel modes. Our approach clearly indicates that the two techniques offer the same on-axis performance. Conversely, small misalignments in the arrays positions affect the OAM scheme, highlighting the greater robustness of a traditional multiplexing method in the context of radio communications.

Supervised Local High-Order Differential Channel Feature Learning for Pedestrian Detection

In this paper, a novel supervised local high-order differential channel feature is proposed for fast pedestrian detection. This method is motivated by the recent successful use of filtering on the multiple channel maps, which can improve the performance. This method firstly compute the multiple channel maps for the input RGB image, and average pooling is acted on the channel maps in order to reduce the effect of noise and sample misalignment. Then, each of the pooled channel maps is convolved with our proposed local high-order filter bank, which can enhance the discriminative information in the feature space. Finally, due to the increasing memory consumption incurred by the higher dimension of resulting feature, we have proposed a local structure preserved supervised dimension reduction method which aims to keep the manifold structure of samples in the feature space. This method is formulated as a classical spectral graph embedding problem which can be solved by the LPP algorithms. Thorough experiments and comparative studies show that our method can achieve very competitive result compared with many state-of-art methods on the INRIA and Caltech datasets. Besides, our detector can run about 20 fps in 480 $$\times $$× 640 resolution images.