Bottleneck Channel Research Articles

Effect of channel morphological changes on wetland transformation

Keeping aside the traditional approaches to investigating floodplain wetland transformation, the current study investigated various aspects of it through changes in river channel morphology and drainage pattern. The study analyzed wetland transformation using satellite image-based machine learning and intensive fieldwork. Ordinary Least Square (OLS) regression was applied to identify dominant influencing factors among 24 contributing factors under six clusters to eight dependent phenomena of transformation. The result showed that 57 % of wetland area lost since 1991, and existing wetland has also experiencing hydrological scarcity. From 1991 to 2021, the area under low water depth (<1 m.) inflated from 18.55 % to 50.54 %, the hydro-period narrowed down, and the appearance of water become inconsistent. The OLS result showed that changes in channel morphology (bottle neck channel, embankment-driven carrying capacity enhancement, etc.), interruptions in river and wetland connecting channels (source closure, breaching the continuity, conversion in to agricultural land, etc.), and changes in flood ambience (regulated by dam construction, erection of embankments, etc.) majorly contributed to wetland transformation. Very high explainability was found in the cases of rate of wetland loss, decreasing water depth under greater depth, narrowing hydro-period (R2 > 0.9). The findings of this work would be a good policy document for floodplain wetland management.

Two-layer Bottleneck Channel Track Assignment for Analog VLSI

Two-layer Bottleneck Channel Track Assignment for Analog VLSI

A Fast Three-layer One-side Bottleneck Channel Routing with Layout Constraints using ILP

A Fast Three-layer One-side Bottleneck Channel Routing with Layout Constraints using ILP

NR Coverage Enhancements for PUSCH

Coverage performance is very important for operators when commercializing NR networks due to its direct impact on service quality as well as CAPEX and OPEX. Compared to LTE, NR systems may operate at higher frequency bands, resulting in larger path loss and worse coverage performance. In this article, potential coverage issues for NR networks are discussed. Through link budget analysis, the gaps between baseline and target performance for typical NR physical channels are described; then the bottleneck channels are identified, including PUSCH, PUCCH, and PRACH. This article mainly focuses on PUSCH coverage enhancement, and potential solutions are discussed in a comprehensive manner. On top of the potential solutions, four joint schemes, which take feasibility as well as specification impact into consideration, are proposed for VoIP and eMBB services to further improve the coverage performance.

Crowded treading warning system for urban rail transit stations based on video detection technology

Based on the rapid development of rail transit, it have a greater hidden danger of congestion and trampling, this paper proposes the construction of an urban rail transit station congestion and trampling warning system based on video detection technology, which uses video detection technology to collect the passenger flow, velocity and density data of the bottleneck channel in rail transit stations, then build and train neural network model to predict the next time passenger flow density combine with the three-parameter relationship of the traffic flow, and use the SPSS software used to fit and analyze the passenger flow parameters. So the limit value of the flow-density polynomial model is predicted by pedestrian flow mutation model and used to realize the definition of the critical passenger flow density model based on the catastrophe theory. On this basis, this article compares the real-time predicted value of passenger flow density to it and get the analysis result, then set the corresponding early warning coefficient, early warning level and early warning measures, so it can truly realize the real-time warning of congestion and trampling in rail transit stations based on passenger flow density prediction.

Broadcast Approach for the Information Bottleneck Channel

This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel without transmitter channel state information (CSI), which is connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. The second approach is a non-oblivious decode-forward (DF) relay where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The bottleneck channel from relay to destination has a fixed capacity $C$ . We examine also the case where the channel capacity can dynamically change due to variable loads on the backhaul link. The broadcast approach is analyzed for cases that only the relay knows the available capacity for next block, and for the case that neither source nor relay know the capacity per block, only its capacity distribution. Fortunately, it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate. Numerical results demonstrate the achievable broadcast rates.

An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution

To nondestructive semantic segment the crack pixels in the image with high resolution, previous methods often use sliding window and the crack patches to train the FCNs, and then use the trained FCNs for crack recognition. However, the FCNs will produce a higher proportion of false crack predictions with messy distributions in the high-resolution image. A CNN-to-FCN method is proposed to solve this problem. The CNN is trained by all the patches for large-scale crack and background recognition, and the screened crack predictions are then segmented by the FCN. A real-world concrete dam surface crack image database is firstly established to verify the improved method. The results indicated that (1) the improved method can extremely avoid the higher proportion of false crack predictions and their messy distributions in the high-resolution image through the full utilization of background patches and large-scale background recognition; (2) the ResNetv2 backbone and DeepLabv3 architecture recommended by the improved method can be further modified by reducing the bottleneck channels and adding a DUC module to achieve better performance; (3) the improved method can also reduce the prediction time when the image has low proportion of crack patches, which becomes more practicable for the engineering applications.

Three-dimensional robot localization using cameras in wireless multimedia sensor networks

We consider three-dimensional (3D) localization in wireless multimedia sensor networks (WMSNs) and seek optimal localization accuracy in order to ensure real-time data fusion of mobile robots in WMSNs. To this end, we propose a real-time 3D localization algorithm realized by a distributed architecture with various smart devices to overcome network instability and the bottleneck channel at the coordinator. We then employ the recursive least squares (RLS) algorithm to fuse the 2D image coordinates from multiple views synchronously in WMSNs and determine the mobile robot's 3D location in an indoor environment. To minimize wireless data transmission, we also develop a distributed architecture that combines various smart devices by defining the data content transmitted from multiple wireless visual sensors. Moreover, we analyze the factors influencing the network instability of various smart devices, and factors influencing the localization performance of mobile robots in a multiple-view system. Experimental results show the proposed algorithm can achieve reliable, efficient, and real-time 3D localization in indoor WMSNs.

Bottleneck Channels Algorithm for Satellite Data Dimension Reduction: A Case Study for IASI

The infrared atmospheric sounder interferometer (IASI) onboard the Metop-A and Metop-B satellites is an essential instrument for numerical weather prediction centers. The IASI, with its 8461 channels, enables the observation of the surface and atmospheric geophysical variables twice a day around the world. This data volume will increase in the future, stressing the need for practical and efficient dimension reduction techniques for storage and transmission but also for inversion or assimilation. In a previous paper by Aires et al. , a new dimension reduction method, the so-called bottleneck channel (BC), has been introduced. BCs are a good compromise between compression and channel selection techniques. In this paper, we introduce several new technical configurations of the BC approach, comparing their advantages/drawbacks. Comparisons are also made with two classical methodologies widely used in the satellite community: principal component analysis (PCA) compression and entropy reduction channel selection. Beyond the compression ability, the methodologies are tested on the temperature, water vapor, and ozone retrieval application using IASI measurements. Two approaches have been tested. First, theoretical retrieval improvement has been measured when using the various data reduction techniques. The BC achieves good theoretical improvements, comparable to noise-adjusted PCA (NAPCA), for the retrieval of the temperature, water vapor, and ozone. Second, a neural network inversion scheme is tested on real IASI observations using the several data reduction techniques. It is shown that BCs provide similar or better results than the other methods. The BCs present compression, denoising, and information content results comparable to the NAPCA, while preserving the physical meaning (i.e., actual channel brightness temperatures) of a channel selection approach. This has numerous advantages for inversion and assimilation: the mixing problem, contamination effects, minor constituents representation, or radiative transfer speed, and each of these will be discussed.

Imaging Channel Connectivity in Nafion Using Electrostatic Force Microscopy.

Channel connectivity is an important material property that is considered in making higher-performance proton-exchange membranes. Our group has previously demonstrated that nearly 50% of the aqueous surface domains in Nafion films do not have a connected path to the opposite side of the membrane. These so-called "dead-end" channels lead to a loss in the conductance efficiency of the membrane. Understanding the structure of these dead-end channels is an important step in improving the conductance of the membrane. Although conductive atomic force microscopy is able to provide insight into the connected channels, it does directly report on the dead-end channels. To address this, we use electrostatic force microscopy (EFM) to probe channel connectivity in a Nafion thin film (100-300 nm) under ambient conditions. EFM provided an image of the capacitive phase shift, which is influenced by surface charge, dielectric permittivity, and tip-sample geometry. We studied several individual channels and measured the quadratic dependence of the EFM signal with the bias voltage. Applying a simple parallel plate model allowed us to assign differences in the EFM signal to particular channel shapes: connected cylindrical channels, dead-end cylinder channels, and bottleneck channels.

Dimension reduction of satellite observations for remote sensing. Part 1: A comparison of compression, channel selection and bottleneck channel approaches

With the increasing volume of satellite observations, dimension reduction techniques are more and more important for storage or transmission. Furthermore, they are essential for inversion schemes that, in practice, cannot handle the huge amount of information provided by modern instruments for near‐real‐time inversion. In this article, we compare the theoretical advantages and limitations of the two general strategies: compression (i.e. feature extraction) and channel selection (i.e. feature selection). The statistical ‘input variable selection’ framework is adopted to revisit the basis of these remote‐sensing techniques. The flexibility of these methods to specific applications is demonstrated. Special emphasis is put on the optimization of observation dimension reduction for the simultaneous retrieval of several variables (e.g. temperature and humidity). In addition to considering the signal‐to‐noise ratio for the variable to retrieve, we propose to account for contamination by other unknown variables. We also introduce a new approach named ‘bottleneck channels’ (BC), which combines compression and channel selection techniques and can therefore benefit from the advantages of both strategies. Various configurations of the BC approach can be considered: strict, linearly or nonlinearly projected, each with advantages and drawbacks. In the companion article, experiments will be conducted on microwave data to illustrate the practical advantages of each approach. BC are able to compress and suppress noise in a similar way to principal component analysis (PCA) and they can be interpreted as channels, as in channel selection.

Dimension reduction of satellite observations for remote sensing. Part 2: Illustration using hyperspectral microwave observations

In the first article of this series, the two classical strategies to reduce satellite data dimension (i.e. compression and channel selection) were presented, together with the introduction of a new method, the so‐called ‘bottleneck channels’ (BC). BC are a compromise between the two classical approaches and can benefit from the advantages of both. In this article, the three methodologies are tested using experiments on a synthetic dataset corresponding to a hyperspectral conceptual instrument in the microwave, for frequencies up to 500 GHz. As expected, principal component analysis (PCA) based methods are best to compress data, but their components lack the physical interpretability of real channels. Channel selection methods preserve this physical meaning but require a much larger number of channels in order to use the redundancy of information to reduce instrumental noise. The new BC method appears to be a good compromise. It can be seen as a PCA compression method where the components are constrained to be instrument channels, facilitating their understanding, inversion or assimilation. BC allows for an easy calibration of data based on radiative transfer simulations and also alleviates the mixing problem of the PCA technique, where various physical variabilities (e.g. temperature, humidity, clouds) can be mixed in the same extracted components. Furthermore, the BC compression rate is equivalent to that of PCA‐based methods even with a limited number of BC.

Quantum network coding for multi-unicast problem based on 2D and 3D cluster states

We mainly consider quantum multi-unicast problem over directed acyclic network, where each source wishes to transmit an independent message to its target via bottleneck channel. Taking the advantage of global entanglement state 2D and 3D cluster states, these problems can be solved efficiently. at first, a universal scheme for the generation of resource states among distant communication nodes is provided. The corresponding between cluster and bigraph leads to a constant temporal resource cost. furthermore, a new approach based on stabilizer formalism to analyze the solvability of several underlying quantum multi-unicast networks is presented. It is found that the solvability closely depends on the choice of stabilizer generators for a given cluster state. and then, with the designed measurement basis and parallel measurement on intermediate nodes, we propose optimal protocols for these multi-unicast sessions. also, the analysis reveals that the resource consumption involving spatial resources, operational resources and temporal resources mostly reach the lower bounds.

Optimized Packet Scheduling in Multiview Video Navigation Systems

We study coding and transmission strategies in multicamera systems, where correlated sources send data through a bottleneck channel to a central server, which eventually transmits views to different interactive users. We propose a dynamic navigation -path aware packet scheduling optimization under delay, bandwidth, and interactivity constraints aimed at optimizing the quality-of-experience of interactive users. In particular , the scene distortion is minimized jointly with the distortion variations along most likely navigation paths. The optimization relies both on a novel rate-distortion model, which captures the importance of each view in the scene reconstruction , and on an objective function that optimizes resources based on a client navigation model. The latter takes into account the distortion experienced by interactive clients as well as the distortion variations that might be observed by clients during multiview navigation. We solve the scheduling problem with a novel trellis-based solution, which permits to formally decompose the multivariate optimization problem, thereby significantly reducing the computation complexity. Simulation results show the PSNR quality gain offered by the proposed algorithm compared to baseline scheduling policies. Finally, we show that the best scheduling policy consistently adapts to the most likely user navigation path and that it minimizes distortion variations that can be very disturbing for users in traditional navigation systems.

Perfect Quantum Network Coding Independent of Classical Network Solutions

Quantum network coding with classical assistance can transmit quantum information with fidelity one in error-free quantum channels. However, the dependency on classical network solutions leads to higher communication complexity and transmission congestion over a large-scale network with bottleneck channels. We find that certain types of quantum operation are sufficient to support multiple unicast sessions over the extended butterfly network. Furthermore, this construction decouples classical communication (assisting decoding information) from classical network solution. Based on this design, we propose an optimal protocol for this specific network, which significantly reduces the communication cost.

Perfect K-Pair Quantum Network Coding Using Superconducting Qubits

Transferring quantum states efficiently between distant nodes of an information processing circuit is an essential primitive in quantum communication and information processing. Here, we present in a solid-state system base transmitting K quantum states crossly in the quantum networks with K-pair sender-receiver and one bottleneck channel with high fidelity only with sharing quantum entanglement between senders.

Correlation-Aware Packet Scheduling in Multi-Camera Networks

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time.

Particle and liquid motion in semi-solid aluminium alloys: A quantitative in situ microradioscopy study

Semi-solid melts exhibit a very unpredictable rheology and filling dynamics, when injected into thin-walled components. Optimization of the process requires an insight into the casting process during injection. For this purpose we injected semi-solid an Al–Ge alloy into two different thin channel geometries while recording high resolution radiographs at fast frame rates (up to 1000 images per s). Comparison of a bottleneck channel, which has previously been used for slower experiments, with a right-angle turn geometry reveals a significant influence of the channel shape on the flow behaviour of the particle–liquid mixture. While the bottleneck is quickly sealed with densified solid, turbulences in the right-angle turn apparently permit solid particles and clusters to move conjointly with the liquid and thus achieve a more complete filling. Single particle trajectories and rapid break-up of solid skeletons in such a system have been observed for the first time in situ.

Distributed Congestion Control of Scalable Video Streams

The paper addresses the problem of distributed congestion control in networks where several video stream- ing sessions share joint bottleneck channels. Based on the rate-distortion characteristics of the video streams as well as the requirements of heterogeneous streaming clients, the proposed algorithm determines the utility of rate in- crements in terms of overall quality benefits. We build on the utility-based congestion control framework initially proposed by Kelly (1), (2) and extend it such that it could efficiently handle heterogeneous delays in the network as well as the specific requirements of video streams. We then describe an original implementation of the proposed distributed congestion control algorithm using the common RTP/UDP/IP protocol stack for the efficient streaming of scalable video streams. Each video session independently adapts its streaming rate based on receiver feedbacks, which permits extension to large scale system without central coordination. Finally, we provide extensive simulation results that demonstrate the performance of the proposed solution. It is shown to successfully distribute the network resources among the different sessions such that the overall video quality is maximized. Experiments further demonstrate that the proposed scheme fairly cohabits with TCP, which is certainly an important advantage in today's network architectures.

Probabilistic quantum network coding of [formula omitted]-qudit states over the butterfly network

Following some ideas of the quantum network coding protocol [Phys. Rev. A 76 (2007) 040301], we propose an efficient protocol to transmit two 2-level quantum states crossly over the butterfly network with two non-maximally entangled qubit pairs shared only between two senders. It is shown that both of the receivers can reestablish the initial states with a certain probability. And one classical bit communication can be saved at the bottleneck channel. Moreover, this protocol is generalized to the case of transmitting two multipartite entangled states.