Channel Characteristics Research Articles

Foreground separation knowledge distillation for object detection

In recent years, deep learning models have become predominant methods for computer vision tasks, but the large computation and storage requirements of many models make them challenging to deploy on devices with limited resources. Knowledge distillation (KD) is a widely used approach for model compression. However, when applied in the object detection problems, the existing KD methods either directly applies the feature map or simply separate the foreground from the background by using a binary mask, aligning the attention between the teacher and the student models. Unfortunately, these methods either completely overlook or fail to thoroughly eliminate noise, resulting in unsatisfactory model accuracy for student models. To address this issue, we propose a foreground separation distillation (FSD) method in this paper. The FSD method enables student models to distinguish between foreground and background using Gaussian heatmaps, reducing irrelevant information in the learning process. Additionally, FSD also extracts the channel feature by converting the spatial feature maps into probabilistic forms to fully utilize the knowledge in each channel of a well-trained teacher. Experimental results demonstrate that the YOLOX detector enhanced with our distillation method achieved superior performance on both the fall detection and the VOC2007 datasets. For example, YOLOX with FSD achieved 73.1% mean average precision (mAP) on the Fall Detection dataset, which is 1.6% higher than the baseline. The code of FSD is accessible via https://doi.org/10.5281/zenodo.13829676.

BioRxToolbox: A computational framework to streamline genetic circuit design in molecular data communications

ABSTRACT Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules and engineered cells is challenging. Diffusing molecules may fall behind their schedule to arrive at the receiver, interfering with symbols of subsequent time slots and distorting the signal. Existing theoretical molecular communication models often focus solely on the characteristics of a communication channel and fail to provide an end-to-end system response since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. In this paper, we present a model-based and computational framework called BioRxToolbox for designing diffusion-based and end-to-end molecular communication systems coupled with synthetic genetic circuits. We describe a novel framework to encode information as a sequence of bits, each transmitted from the sender as a burst of molecules, control cellular behavior at the receiver, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. Here, BioRxToolbox is demonstrated using a biological design and computational simulations for various communication scenarios. This toolbox facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

Quantitative characterization of geomorphological and topographical features of debris-flow channels at the Alpe di Succiso mountain, Northern Apennines (Italy)

ABSTRACT This study combines the traditional method of geological and geomorphological mapping with a quantitative GIS analysis to examine landforms and debris flows channels. Using a high-resolution DEM (5 m) as a base, the geomorphological map highlights the various landforms around the Alpe di Succiso (Northern Apennines, Italy), including glacial and cryoclastic features, slope landforms and deposits due to gravity (particularly debris flow) and deposits due to running waters. The methodology applied to the channels is based on the geomorphometric parameters derived from the DEM, including Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), Slope Angle, and Elevation extracted every 10 m along each channel and grouping the channels in four classes based on their lengths. Within each class, multiple ranking criteria were employed for the identification of channels showing anomalous or common patterns. Debris flow channels exhibit anomalous patterns in the geomorphometric parameters, indicating significant variations in their topographical characteristics..

UOWC spatial diversity techniques over hostile maritime environments: an approach under imperfect CSI and per-source power constraints

Optical communication in submarine environments has emerged as a novel technology that enables high bandwidth and high data rate links. However, the unique characteristics of the underwater channel impose new challenges, such as mitigating the remarkable absorption and scattering of hostile maritime environments. For the first time, we consider a per-source optical power constraint based on eye-safety regulations, which has never before been taken into account in Multiple-Input/Single-Output (MISO) systems within underwater optical wireless communication (UOWC) scenarios. Hence, we introduce an innovative spatial repetition coding (SRC) system model, which enables the analysis of an SRC scheme operating under either a per-source or a per-transmitter power constraint. In addition, a tractable generalized transmit laser selection (GTLS) model is presented in order to consider the impact of erroneous selections of the best laser source due to imperfect channel state information (CSI) at the transmitter, prevalent in underwater scenarios with dynamic fluctuations from water currents. Novel bit error rate closed-form expressions and asymptotic results are derived. The presented results demonstrate that an SRC system, when appropriately designed under a per-source power constraint, outperforms the TLS system by effectively mitigating the adverse effects of underwater links. Conversely, in situations where compact transmitters necessitate constraints that significantly modify eye-safety, TLS schemes are superior.

Quantifying the fractal complexity of nutrient transport channels in Escherichia coli biofilms under varying cell shape and growth environment.

Recent mesoscopic characterization of nutrient-transporting channels in Escherichia coli has allowed the identification and measurement of individual channels in whole mature colony biofilms. However, their complexity under different physiological and environmental conditions remains unknown. Analysis of confocal micrographs of colony biofilms formed by cell shape mutants of E. coli shows that channels have high fractal complexity, regardless of cell phenotype or growth medium. In particular, colony biofilms formed by the mutant strain ΔompR, which has a wide-cell phenotype, have a higher fractal dimension when grown on rich medium than when grown on minimal medium, with channel complexity affected by glucose and agar concentrations in the medium. Osmotic stress leads to a dramatic reduction in the ΔompR cell size but has a limited effect on channel morphology. This work shows that fractal image analysis is a powerful tool to quantify the effect of phenotypic mutations and growth environment on the morphological complexity of internal E. coli biofilm structures. If applied to a wider range of mutant strains, this approach could help elucidate the genetic determinants of channel formation in E. coli colony biofilms.

XL-MIMO channel measurement, characterization, and modeling for 6G: a survey

XL-MIMO channel measurement, characterization, and modeling for 6G: a survey

High-performance near-substrate heat sink with Tesla-like rotor-wing microchannel for chiplet cooling application

Microchannel heat sink provides a viable solution for enhancing thermal management in high-power chiplets. However, in consideration of cooling efficiency, low pressure drop, cost-effectiveness and reliability, it’s challenging to create an effective heat sink for a million-watt chip. Inspired by Tesla structure, this study introduces a Tesla-like rotor-wing microchannel (TRWM) to boost cooling performance. Due to the features of main channels, branch channels, and micro-islands in TRWM design, fluid in microchannel is effectively mixed and stirred with varied flow patterns, high temperature gradients and significant velocity gradients, which enhances the cooling capacity through precise fluid dynamics adjustment. Three different TRWMs are designed and compared by simulations. And the simulated results show that TRWM of 135° model achieves a maximum heat flux of 1514 W cm−2 @0.1 cm2 under a 60 K temperature rise, improving cooling efficiency by 19.4 % over the traditional channel design with 1221 W cm−2 @0.1 cm2. And then, the TRWM is fabricated using Ultraviolet Lithgraphie Galvanoformung Abformung (UV-LIGA) and bonding processes. Ultimately, the fabricated 135° TRWM sample demonstrated a heat dissipation capacity of up to 1446 W cm-2 @0.1 cm2 with a temperature increase of 60 K, which corresponds well with the simulated results. Moreover, the discrepancy between the actual and simulated values is less than 6.3 % under the same conditions. This performance provides a practical design solution for chiplet cooling and highlights the potential of bioinspired microfluidics.

Global feature fusion generative adversarial network for underwater image enhancement

AbstractMost CNN‐based networks treat features in different channels and pixels similarly. However, the dispersion of light underwater results in uneven haze distribution throughout the images. To alleviate these problems, the global feature fusion generative adversarial network is proposed, to separate and enhance the global feature by channels and pixels simultaneously. The key novelty of our method is utilizing the attention mechanism to emphasize features in different channels and pixels in fusion block, which allocates higher weights to more important features. The network's reliability is further optimized through the incorporation of condition information to restrict the training of the network. Both qualitative and quantitative evaluations verify that the proposed method is capable of greater visual quality compared with other classic and state‐of‐the‐art methods.

Bearing fault diagnosis based on wavelet adaptive threshold filtering and multi-channel fusion cross-attention neural network.

In industrial applications, it is difficult to extract the fault feature directly when the rolling bearing works under strong background noise. In addition, single-channel vibration sensor data pose limitations in providing a comprehensive representation of bearing fault features; how to effectively fuse data of each channel and extract features is a challenge. To solve the above-mentioned problems, a fault diagnosis method based on wavelet adaptive threshold filtering and multi-channel fusion cross-attention neural network is proposed in this paper. First, the multi-scale discrete wavelet transform is applied to obtain the wavelet coefficients of each channel. Adaptive threshold filtering is conducted to filter out noise and extract symbolic features. The threshold updates with the training of the network. Then, the wavelet coefficients are reconstructed and the channel attention is performed to further extract the symbolic features of the fault signal. Finally, the multi-channel fault signals are fused by a cross-attention module. This module can fully extract the features of each channel and fuse multi-channel data. To improve the generalization ability of the network, residual connections are added. To verify the effectiveness of the proposed method, experiments are carried out on the rolling bearing datasets of Case Western Reserve University and Xi'an Jiaotong University. In addition, the gas turbine main bearing dataset is also applied to prove the reliability of this method.

Experimental investigation on transitional flow of supercritical hydrocarbon fuel within straight channels of Printed Circuit Heat Exchangers for hypersonic thermal management systems

The onboard supercritical CO2 closed Brayton cycle (SCBC) has established an effective thermal management pathway for hypersonic vehicles operating at high Mach numbers. A Printed Circuit Heat Exchanger (PCHE) is employed within the cycle system to facilitate heat transfer between fuel and carbon dioxide, with its thermo-hydraulic characteristics significantly impacting both power generation and propulsion performance. Given the insufficient research on the flow and heat transfer of hydrocarbon fuels in PCHEs, this study aims to experimentally address the gaps in understanding transition flow for supercritical hydrocarbon fuels, particularly within semi-circular microchannels. We conducted experiments on the flow transitions using a specially designed test piece with a diameter of 1 mm, replicating the PCHE channel characteristics. The experimental Reynolds number range from 300 to 8300 spans laminar, transition, and turbulent flow regimes. Friction factors for n-decane are obtained under adiabatic and heated conditions. The results validate the nomenclature of the quasi-turbulent region and reveal that the critical Reynolds numbers for the transitional, quasi-turbulent and hydraulic smooth turbulent regions under adiabatic conditions are 2200, 2440 and 2980, respectively. A reduction in friction drag attributed to boundary layer viscosity is observed to alter the friction factor under heating conditions, while heating influences both the onset and termination of the transition region, but does not affect its width. Classical correlations are compared with the experimental results, and new empirical correlations for both adiabatic and non-adiabatic conditions are developed, showing superior predictive accuracy compared to existing models. This provides a reliable tool for the engineering design of heat exchangers in the onboard thermal manage systems.

CI-YOLO: A lightweight foreign object detection model for inspecting transmission line

Timely detection and elimination of foreign objects on transmission lines are of significance for the secure operation of power grids. With the widespread utilization of unmanned aerial vehicles (UAVs) for inspecting transmission lines, how to effectively balance the resource consumption and accuracy of detection model presents a challenge. To tackle this problem, this study proposes a foreign object detection model based on an improved YOLOv8n, realizing both lightweight and enhanced detection performance. Firstly, a SC Block structure is devised to substitute Bottleneck in C2f module of Backbone, minimizing the spatial and channel redundancy among features, enhancing the network performance, and reducing the consumption of computational resources. Subsequently, a novel dual attention mechanism, IEMA, is added after C2f module of Backbone, strengthening the crucial spatial and channel feature expression capability and significantly increasing the detection accuracy. Lastly, a new feature fusion network, CCFF_IEMA, is designed to replace Neck network, effectively integrating detailed features and reducing the parameter count and FLOPs. Experimental results indicate that in comparison to standard YOLOv8n, the parameters of our proposed CI-YOLO model are decreased by 40 %, FLOPs are reduced by 19.8 %, while achieving improvements of 0.7 % in Precision, 3.2 % in Recall, and 3.1 % in mAP0.5, respectively. The CI-YOLO model is beneficial to deployment on UAVs and provides a novel approach for detecting the foreign objects.

Reconfigurable dual-mode meta-waveguide

With the rapid growth of information flux, integrating more channels in limited spaces has been a critical requirement for highly-integrated long-range parallel transmissions. Although meta-waveguides, such as spoof surface plasmon polariton (SSPP) waveguides, can suppress crosstalk between adjacent channels, it is still urgent to seek new methods to further increase the number of channels without occupying additional space. Here, we propose a reconfigurable dual-mode meta-waveguide (RDMMW) by integrating diodes into meta-units, which supports two independent channels using two orthogonal modes. The RDMMW can be switched among dual-mode, even-mode, odd-mode and cutoff states by controlling the diodes, which provides more freedom to manipulate the channel features. The RDMMW possess two outstanding merits in highly-integrated long-range parallel transmissions, including lower coupling between two channels than conventional single-mode waveguides and constant mode coupling in longer waveguides. Thus the proposed RDMMW has advantages of multi-mode, reconfiguration, low coupling and high integration density, providing a new avenue to realize highly-integrated multi-functional systems.

Center-Highlighted Multiscale CNN for Classification of Hyperspectral Images

Hyperspectral images (HSIs) capture a wide range of spectral features across multiple bands of light, from visible to near-infrared. Hyperspectral image classification technology enables researchers to accurately identify and analyze the composition and distribution of surface materials. Current mainstream deep learning methods typically use block sampling to capture spatial features for the model. However, this approach can affect classification results due to the influence of neighboring features within the sample block. To improve the model’s focus on the center of the sampling block, this study proposes a center highlight with multiscale CNN for hyperspectral image classification (CHMSC). The network utilizes an automatic channel selector (Auto-CHS) to fully consider every channel feature and capture the correlation between the channels. Then, CHMSC enhances the model’s ability to concentrate on the central features of the sampling block utilizing structures such as the center highlight. Finally, before outputting the prediction results, an SENet is employed to further refine the features and learn associate interactions between different scales of spatial features and spectral features. Experimental results from three hyperspectral datasets validate the effectiveness of the proposed method. Specifically, when 15 samples from each class are selected for training, CHMSC achieves the highest overall accuracy (OA) of 90.05%, 92.78%, and 90.15% on the three datasets, outperforming other methods with increases of more than 3.11%, 1.8%, and 2.01% in OA, respectively.

Cooperative Spacing Control of Vehicle Platoon via Relative Output Feedback Considering Analog Fading Networks

ABSTRACTThe influence of analog fading channels on platoon control performance is studied via relative output feedback. The main goal is to determine the existence of a distributed output feedback controller capable of achieving mean square stability (MSS) for the underlying vehicle platoon systems over analog fading channels. First, the conditions, closely associated with the statistical characteristics of fading channels, for achieving platoon MSS are derived for three cases: undirected information flow (UIF) topology with identical fading channels, balanced directed information flow (BDIF) topology with identical fading channels, and general information flow topology (IFT) with nonidentical fading channels, respectively. These conditions explicitly reveal how the statistical characteristics of fading channels and IFT jointly affect vehicular stability. Second, in order to mitigate the impact of fading channels on platoon MSS, some feasible dynamic observer‐based controllers by using the relative output feedback, are proposed for each one of the former two situations by solving the corresponding modified Riccati inequalities (MRIs); and in the last situation, by employing edge Laplacian to model the vehicle platoon dynamics, a sufficient condition based on the solution of an LMI, and a feasible state‐based controller based on the solution of an MRI, are provided. Finally, simulations are carried out to compare three distinct fading channels, namely the Nakagami channel, Rician channel, and Rayleigh channel. The outcomes substantiate the superior and effective performance of the implemented control methods in guaranteeing the platoon MSS.

Chitin Translocation Is Functionally Coupled with Synthesis in Chitin Synthase.

Chitin, an extracellular polysaccharide, is synthesized by membrane-embedded chitin synthase (CHS) utilizing intracellular substrates. The mechanism of the translocation of synthesized chitin across the membrane to extracellular locations remains unresolved. We prove that the chitin synthase from Phytophthora sojae (PsCHS) is a processive glycosyltransferase, which can rapidly produce and tightly bind with the highly polymerized chitin. We further demonstrate that PsCHS is a bifunctional enzyme, which is necessary and sufficient to translocate the synthesized chitin. PsCHS was purified and then reconstituted into proteoliposomes (PLs). The nascent chitin is generated and protected from chitinase degradation unless detergent solubilizes the PLs, showing that PsCHS translocates the newly produced chitin into the lumen of the PLs. We also attempted to resolve the PsCHS structure of the synthesized chitin-bound state, although it was not successful; the obtained high-resolution structure of the UDP/Mn2+-bound state could still assist in describing the characterization of the PsCHS's transmembrane channel. Consistently, we demonstrate that PsCHS is indispensable and capable of translocating chitin in a process that is tightly coupled to chitin synthesis.

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate the implementation of effective preventive maintenance strategies. This article proposed a novel RUL-centric data augmentation method, designated as DF-MDAGRU, for the purpose of predicting the remaining useful life (RUL) of bearings. This model is based on an encoder–decoder framework that integrates time–frequency domain feature enhancement with multidimensional dynamic attention gated recurrent units for feature extraction. This method enhances time–frequency domain features through the Discrete Wavelet Downsampling module (DWD) and Convolutional Fourier Residual Block (CFRB). This method employs a Multiscale Channel Attention Module (MS-CAM) and a Multiscale Convolutional Spatial Attention Mechanism (MSSAM) to extract channel and spatial feature information. Finally, the output predictions are processed through linear regression to achieve the final RUL estimation. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches on the FEMETO-ST and XJTU datasets.

TLCSFI: A Pose-Guided Person Re-Identification Method with Two-Level Channel–Spatial Feature Integration

Person re-identification methods currently encounter challenges in feature learning, primarily due to difficulties in expressing the correlation between local features and integrating global and local features effectively. To address these issues, a pose-guided person re-identification method with Two-Level Channel–Spatial Feature Integration (TLCSFI) is proposed. In TLCSFI, a two-level integration mechanism is implemented. At the first level, TLCSFI integrates the spatial information from local features to generate fine-grained spatial features. At the second level, the fine-grained spatial feature and the coarse-grained channel feature are integrated together to complete channel–spatial feature integration. In the method, a Pose-based Spatial Feature Integration (PSFI) module is introduced to generate the pose union feature, which calculates intra-body affinity to guide the integration of spatial information among local pose feature maps. Then, a Channel and Spatial Union Feature Integration (CSUFI) module is proposed to efficiently integrate the channel information of the global feature and the spatial information of the pose union feature. Two individual networks are designed to extract channel and spatial information, respectively, in CSUFI, which are then weighted and integrated. Experiments are conducted on three publicly available datasets to evaluate TLCSFI, and the experimental results demonstrate its competitive performance.

FEATURES OF OPERATION OF THE «HERMON-ELPIN» PRESSURE SYSTEM

Gravity canals have their own operation peculiarities. A gravity main canal is usually several times longer than the cumulative length of pumping pipelines of pump stations being replaced. Two important features of gravity channels are considered: establishing a hydraulic regime and removing air from the pipeline.

Reducing Successive Interference Cancellation Iterations in Hybrid Beamforming Multiuser Massive Multiple Input Multiple Output Systems Through Grouping Users with Symmetry Channels

This paper presents a comprehensive exploration of advanced beamforming techniques tailored for millimeter-wave (mm-Wave) communication systems. In response to the burgeoning demand for higher data rates, coupled with the constraints of power consumption and hardware complexity, this study focuses on developing a hybrid beamforming framework optimized for downlink scenarios, specifically targeting groups of users based on the approximate symmetry of their channels. The primary innovation of this research lies in leveraging the symmetry of channels among near users to develop a group-based successive interference cancellation (SIC) algorithm. Unlike traditional approaches that address interference on a per-user basis, this algorithm utilizes channel symmetry within clusters of users to reduce computational complexity and improve the efficiency of SIC. By grouping users with symmetrical channel characteristics, the algorithm simplifies the interference management process while maintaining system performance. The proposed system demonstrates notable advantages over existing non-linear algorithms through extensive simulations and performance evaluations, particularly in terms of spectral efficiency and computational complexity. In this study, we further emphasize the importance of balancing spectral efficiency improvements with reduced computational demands, offering a nuanced trade-off that accommodates various operational requirements. The flexible optimization framework provided showcases the system’s adaptability to diverse deployment scenarios and network configurations.

Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).