Limited Bandwidth Research Articles

Neural Operator for Planetary Remote Sensing Super-Resolution with Spectral Learning

High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environments. Conventional super-resolution methods typically employ separate models for different scales, treating them as independent tasks. This approach limits deployment and real-time applications in planetary remote sensing. Moreover, capturing global context is crucial in planetary remote sensing images due to their contextual similarities. To address these limitations, we propose Discrete Cosine Transform (DCT)–Global Super Resolution Neural Operator (DG-SRNO), a global context-aware arbitrary-scale super-resolution model. DG-SRNO achieves super-resolution at any scale using a single framework by learning the mapping between low-resolution (LR) and high-resolution (HR) function spaces. We mathematically prove the global receptive field of DG-SRNO. To evaluate DG-SRNO’s performance in planetary remote sensing tasks, we introduce the Ceres 800 dataset, a planetary remote sensing super-resolution dataset. Extensive quantitative and qualitative experiments demonstrate DG-SRNO’s impressive reconstruction capabilities.

"Pushed to Their Limits": Health Care Provider Perspectives on Barriers and Facilitators to Implementing a Mind-Body and Activity Program for Older Adults With Chronic Pain in a Community Clinic for the Underserved.

Older adults from underserved backgrounds experience chronic pain at a rate of 60% to 75%. Pharmacological treatments have limited efficacy and involve considerable risks. Mind-body interventions hold promise to improve pain outcomes but are typically not implemented in community clinics in which they are needed most, thus contributing to health disparities in chronic pain treatment. We conducted qualitative focus groups and interviews with 20 providers (eg, primary care doctors, nurses, administrators). We sought their perspectives on barriers and facilitators to implementing an evidence based mind-body activity program for older adults with chronic pain at an underserved community health clinic in Massachusetts. Subthemes were identified within 2 superordinate domains (barriers and facilitators) using a hybrid inductive-deductive thematic analysis approach following the Framework Method. Providers discussed facilitators (partner with clinic staff to facilitate referrals and buy-in, integrate referrals through the electronic medical record, offer groups in different languages, post and tailor advertisements) and barriers (limited staff bandwidth, scheduling challenges, inconsistent patient participation). These results will directly inform tailoring and subsequent effectiveness testing and implementation of the pain management program for older underserved adults with chronic pain in this community health care setting.

I2BODIPY as a new photoswitchable spin label for light-induced pulsed EPR dipolar spectroscopy exploiting magnetophotoselection.

Electron paramagnetic resonance (EPR) pulsed dipolar spectroscopy (PDS) using triplet states of organic molecules is a growing area of research due to the favourable properties that these transient states may afford over stable spin centers, such as switchability, increased signal intensity when the triplet is formed in a non-Boltzmann distribution and the triplet signal is used for detection, and high orientation selection, when the triplet signal is probed by microwave pulses. This arises due to the large spectral width at low fields, a result of the large zero field splitting, and limited bandwidth of microwave pulses used. Here we propose the triplet state of a substituted BODIPY moiety as a spin label in light induced PDS, coupled to a nitroxide, in a model peptide with a rigid structure. Orientation selection allows information on the relative position of the centres of the two labels to be obtained with respect to the nitroxide reference frame. Additionally, magnetophotoselection effects are employed to introduce optical selection and additional constraints for the determination of the relative orientation of the spin labels considering the reference frame of the triplet state.

HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution

In recent years, 360-degree videos have gained significant traction due to their capacity to provide immersive experiences. However, the adoption of 360-degree videos substantially escalates bandwidth demands, necessitating approximately four to ten times more bandwidth than traditional video formats do. This presents a considerable challenge in maintaining high-quality videos in environments characterized by limited bandwidth or unstable networks. A trend has emerged where client-side computational power and deep neural networks are employed to enhance video quality while mitigating bandwidth requirements within contemporary video delivery systems. These approaches segment a video into discrete chunks and apply super resolution (SR) models to each segment, streaming low-resolution (LR) chunks alongside their corresponding SR models to the client. Although these methods enhance both video quality and transmission efficiency for conventional videos, they impose greater computational resource demands when applied to 360-degree content, thereby constraining widespread implementation. This paper introduces an innovative method called HVASR for 360-degree videos that leverages viewport information for more precise segmentation and minimizes model training costs as well as bandwidth requirements. Additionally, HVASR incorporates a viewport-aware training strategy that is aimed at further enhancing performance while reducing computational expenses. The experimental results demonstrate that HVASR achieves an average utility increase ranging from 12.46% to 40.89% across various scenes.

CNNCat: categorizing high-energy photons in a Compton/Pair telescope with convolutional neural networks

A Compton/Pair telescope, designed to provide spectral resolved images of cosmic photons from sub-MeV to GeV energies, records a wealth of data in a combination of tracking detector and calorimeter. Onboard event classification can be required to decide on which data to down-link with priority, given limited data-transfer bandwidth. Event classification is also the first and one of the most crucial steps in reconstructing data. Its outcome determines the further handling of the event, i.e., the type of reconstruction (Compton, pair) or, possibly, the decision to discard it. Errors at this stage result in misreconstruction and loss of source information. We present a classification algorithm driven by a Convolutional Neural Network. It provides classification of the type of electromagnetic interaction, based solely on low-level detector data. We introduce the task, describe the architecture and the dataset used, and present the performance of this method in the context of the proposed (e-)ASTROGAM and similar telescopes.

A 2DEG‐Based GaN‐on‐Si Terahertz Modulator with Multi‐Mode Switchable Control

AbstractAs terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors.

Scalable SOA-based Banyan optical space switch on InP membrane on silicon (IMOS)

Optical switches (OS) are vital for meeting modern data centers’ capacity and latency needs, overcoming the bandwidth and speed limitations that electronic switches encounter. To this end, this work proposes a semiconductor optical amplifier (SOA)-based OS on the IMOS, a unique platform with a combination of high refractive index contrast for compactness and monolithic active-passive integration for active functionalities, thereby enabling the creation of compact SOA-based OSes. An 8 × 8 Banyan SOA-based OS is integrated on a 4 × 4 mm2 area on the standard 4 × 6 mm2 IMOS cell. This marks the first application of the IMOS technology platform for SOA-based OSes and sets the stage for compact and large-scale monolithic photonic integrated OS circuits. The basic 2 × 2 switch module delivers a high optical signal-to-noise ratio (OSNR) and an extinction ratio (ER) above 45 dB. Employing NRZ-OOK routing on the 2 × 2 basic OS module, a 15 dB input power dynamic range (IPDR) is achieved within a 1 dB power penalty at 12.5 Gb/s. Data routing at higher data rates of 25 Gb/s and 40 Gb/s incurs power penalties of 0.8 and 1.4 dB, respectively. Furthermore, data routing for a 3-stage 8 × 8 switch operating at 25 Gb/s results in a power penalty of 1.2 dB. Compared to the 1-stage 2 × 2 switch, only a 0.4 dB additional penalty is observed despite incorporating 2 additional SOAs in the cascade. This indicates that the switch can scale to higher radix to accommodate multi-stage switches with numerous ports, large bandwidth, and fast speed, which is essential in modern large-scale data centers.

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

Unified algorithms for distributed regularized linear regression model

In recent years, distributed statistical models have received increasing attention for large-scale data analysis. On the one hand, data sets come from multiple data sources, and are stored in different locations due to limited bandwidth and storage, or privacy protocols, directly centralizing all data together is impossible. On the other hand, the size of data is so large that it is difficult or inefficient to analyze data together. There are two main research aspects to using distributed statistical models to analyze large-scale data. The first one is to study the statistical convergence rate under some mild assumptions. The second one is to establish fast and efficient optimization algorithms considering the property of the loss function. There is a lot of research on the first aspect, but relatively little research on the second one. Motivated by this, we consider the construction of unified algorithms for distributed linear regression with different losses and regularizers. As a result, we designed two type methods, proximal alternating direction method of multipliers (pADMM) and distributed accelerated proximal gradient method with line-search (DAPGL). In order to demonstrate the efficiency of the proposed algorithms, we perform numerical experiments on the distributed Huber-Lasso model and Huber-Group-Lasso model. In view of the numerical results, we can observe that these two algorithms are more competitive than some of state-of-art algorithms. In particular, DAPGL algorithm performs better than pADMM in most cases.

Ultra-wide band and high efficient Polarization converting metasurface employing Bell-shaped unit cell for RCS reduction

Polarization is a dominant parameter regarding communication systems in recent times. The standard polarization techniques can be used only to a certain extent because of their huge size and limited bandwidth constraints. A design of bell-shaped polarization converting metasurface (PCMS) is presented in this paper which is highly efficient for Radar cross section (RCS reduction) and also operates in ultra-wideband. The designed PCMS attains a polarization conversion ratio of more than 90% by demonstrating the transformation of the linear polarization wave to its orthogonal counterpart. The PCMS is arranged in a triangular chessboard configuration to reduce the RCS. RCS reduction is obtained by achieving destructive interference, accomplished by two unit cells. The two unit cells, PCMS and its mirror will have a phase difference of 180 ± 37°. The phase cancellation is obtained by rotating and arranging the proposed PCMS at 90°, 180° and 270°. RCS reduction is obtained in a frequency range from 4 GHz to 24.8 GHz with 144% FBW. This PCMS is verified experimentally. A good agreement is found between the simulation and the experimental results.

Diffraction‐Driven Parallel Convolution Processing with Integrated Photonics

AbstractTraditional electronic processors often struggle with bandwidth limitations and high power consumption when executing extensive linear operations for deep learning tasks. Optical computing has emerged as a promising alternative, offering parallel and energy‐efficient computation capabilities. Yet, the development of high‐density optical computing architectures on integrated photonic platforms remains limited, hindered by constraints in neuron scalability and control engineering complexities. Addressing these challenges, this work presents a diffraction‐driven multi‐kernel optical convolution unit (MOCU) that enables on‐chip parallel convolution processing. By utilizing cascaded silica 1D metalines as pre‐trained large‐scale weights and employing spatial multiplexing at the output, MOCU allows simultaneous passive computation of diverse convolutions within a single unit. This architecture facilitates the construction of optical convolutional neural networks (OCNNs), enabling efficient machine vision processing with a streamlined design. To mitigate errors in MOCU‐embedded OCNNs, a lightweight electronic neural network operates concurrently to calibrate systematic deviations via a low‐rank adaptation (LoRA) algorithm, with minimal overhead. The fabricated MOCU chip demonstrates the highest independent 8‐kernel convolutions in parallel, each with a kernel size and occupying just 0.06 . This architecture effectively merges photonic and electronic technologies, offering a scalable design pathway for energy‐efficient, high‐density deep learning hardware.

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

Flexible Natural Language-Based Image Data Downlink Prioritization for Nanosatellites

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods.

The synergistic effects on the magnetic CoNi and Fe3O4 nanoparticles co-embedded porous carbon toward enhanced microwave absorbing performance

Abstract Lightweight and environmentally friendly three-dimensional biomass-derived porous carbon (3D BPC) holds great potential as a highly effective material for microwave absorption (MA) applications. However, the high complex permittivity and lack of magnetic loss in a single 3D BPC lead to impedance mismatching and a limited absorption bandwidth. Developing 3D BPC–based absorbers with multiple loss mechanisms and excellent impedance matching remains a notable yet challenging task. In this study, inspired by a synergistic composition and microstructure design, 3D BPC co-embedded with magnetic cobalt–nickel (CoNi) and iron(III) oxide (Fe3O4) nanoparticles (3D BPC@CoNi@Fe3O4) was developed. 3D BPC@CoNi@Fe3O4 exhibited consistently low complex permittivity, primarily due to the suppression of conductive loss, whereas the introduction of magnetic phases (CoNi and Fe3O4) enhanced the magnetic loss. Optimised impedance matching, achieved through the synergistic regulation of the electromagnetic parameters, emerged as the key mechanism for improving the MA properties. The as-synthesised 3D BPC@CoNi@Fe3O4 composite, with only 20 wt.% loading demonstrated a wide effective absorption bandwidth (reflection loss [RL] < −10 dB) of 6.08 GHz and an impressive minimum RL value of −40.08 dB. These findings offer valuable insights into the development of high-performance 3D BPC–based microwave absorbers through composition and microstructure optimisation.

A Review on Network Covert Channel Construction and Attack Detection

ABSTRACTA covert network channel is a communication channel in which the message is secretly transmitted to the recipient. Sometimes, covert network channels are vulnerable to multiple attacks. Therefore, the message must be properly secure. In most cases, the covert channel is used to ensure data protection and allow users to freely access the Internet. In this paper, several recent studies are reviewed on covert network channels and examine the existing works from 2015 to 2024. This review article also discusses the undetectability and reliability of different types of covert network channels. Furthermore, a detailed description of the covert network channel's ability to hide in containers is provided. Existing research on covert network channels explains a few techniques for detecting attacks in secret data communication. However, several machine learning and deep learning techniques have been discussed in this article. Additionally, this article describes the accuracy of detection through an overview of current technologies. In addition, various countermeasures to prevent attacks in covert channels are also discussed in detail. However, in this case, the bandwidth limitations, data set limitations, and covert channel capacity are clearly defined, which will help future researchers build covert network channels and detect attacks. Finally, this work considers the challenges faced by covert network channels and the future scope of application.

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

DCMR: Degradation compensation and multi-dimensional reconstruction based pre-processing for video coding

The rapid growth of video data poses a serious challenge to the limited bandwidth. Video coding pre-processing technology can remove coding noise without changing the architecture of the codec. Therefore, it can improve the coding efficiency while ensuring a high degree of compatibility with existing codec. However, the existing pre-processing methods have the problem of feature redundancy, and lack an effective mechanism to recover high-frequency details. In view of these problems, we propose a Degradation Compensation and Multi-dimensional Reconstruction (DCMR) pre-processing method for video coding to improve compression efficiency. Firstly, we develop a degradation compensation model, which aims at filtering the coding noise in the original video and relieving the frame quality degradation caused by transmission. Secondly, we construct a lightweight multi-dimensional feature reconstruction network, which combines residual learning and feature distillation. It aims to enhance and refine the key features related to coding from both spatial and channel dimensions while suppressing irrelevant features. In addition, we design a weighted guided image filter detail enhancement convolution module, which is specifically used to recover the high-frequency details lost in the denoising process. Finally, we introduce an adaptive discrete cosine transform loss to balance coding efficiency and quality. Experimental results demonstrate that compared with the original codec H.266/VVC, the proposed DCMR can achieve BD-rate (VMAF) and BD-rate (MOS) gains by 21.62% and 12.99% respectively on VVC, UVG, and MCL-JCV datasets.

Compact Metasurface Terahertz Spectrometer

AbstractThe terahertz frequency region of the electromagnetic spectrum is crucial for understanding the formation and evolution of galaxies and stars throughout the universe's history, as well as the process of planet formation. Detecting the unique spectral signatures of molecules and atoms requires terahertz spectrometers, which must be operated in space observatories due to water vapor absorption in the Earth's atmosphere. However, current terahertz spectrometers face challenges such as low resolution, limited bandwidth, large volume, and complexity. In this paper, the issues of size and weight are addressed by demonstrating a concept for a centimeter‐sized, low‐weight terahertz spectrometer using a metasurface. The design of the metasurface spectrometer is first discussed for the 1.85 to 2.4 THz range, followed by its fabrication. Next, an array of quantum cascade lasers operating at slightly different frequencies around 2.1 THz is utilized to characterize the spectrometer. Finally, a spectrum inversion method is applied to analyze the measured data, confirming a resolution R (λ/Δλ) of at least 273. This concept can be extended to other application areas, such as planetary observations and various wavelengths in the far‐infrared (FIR) and near‐infrared (NIR) ranges.

Field study on proactive pipe condition assessment using hydroacoustic noise

Condition assessment of water transmission pipelines is of important necessity for prioritizing rehabilitation and preventing catastrophic pipe failure. Hydraulic transient analysis has been investigated for three decades for this purpose and applied to many field studies. However, the significant pipe vibrations caused by the transient generation process and the limited energy and bandwidth of conventional discrete transient pressure waves (e.g., step and pulse waves) limit the accuracy and resolution of the analysis. This paper reports a pilot field study of noninvasive and nondestructive pipe condition assessment using small amplitude and persistent hydroacoustic noise instead of conventional large and discrete hydraulic transient waves. By opening a discharge valve installed at an existing access point, such as an air valve, on the field pipe, hydroacoustic noise was generated at the valve due to the turbulence of the discharge. The hydroacoustic noise was measured by pressure transducers at this access point as well as some other access points along the field pipe. A signal deconvolution process was then applied to the measured hydraulic pressures to transfer them to a compositional impulse response function (IRF), which is composed of IRFs of different pipe sections. A mathematical model was derived to interpret the anomaly-induced spikes on the deconvolution trace. A time-shifting process on the compositional IRFs was proposed to find the locations with pipe condition changes from a specific direction of the pipe. The field study has shown that small-amplitude persistent hydroacoustic noise can replace conventional large hydraulic transient waves for pipe condition assessment.

Highly Amplified Broadband Ultrasound in Antiresonant Hollow Core Fibers

High‐frequency broadband ultrasound in nested antiresonant hollow core fibers (NANFs) is investigated for the first time. NANFs have remarkable features enabling high‐resolution microscale optoacoustic imaging sensors and neurostimulators. Solid optical fibers have been successfully employed to measure and generate ultrasonic signals, however, they face issues concerning attenuation, limited frequency range, bandwidth, and spatial resolution. Herein, highly efficient ultrasonic propagation in NANFs from 10 to 100 MHz is numerically demonstrated. The induced pressures and sensing responsivity are evaluated in detail, and important parameters for the development of ultrasonic devices are reviewed. High pressures (up to 234 MPa) and sensing responsivities (up to −207 dB) are tuned over 90 MHz range by changing the diameters of two distinct NANF geometries. To the best of knowledge, this is the widest bandwidth reported using similar diameter fibers. The results are a significant advance for fiber‐based ultrasonic sensors and transmitters, contributing to improve their efficiency and microscale spatial resolution for the detection, diagnosis, and treatment of diseases in biomedical applications.