Multiple Data Streams Research Articles

Improved migration algorithms for uplink transmission secrecy sum rate maximization in MIMO-NOMA

The most significant technology that could assist future high-speed wireless systems in meeting their growing capacity demands is Non-Orthogonal Multiple Access (NOMA). Nevertheless, NOMA should strive to achieve a harmonious equilibrium between equitable distribution among users and optimal network performance. A wireless communication network is a system that enables connections with limited power resources, such as Internet of Things (IoT) devices. Horizontal multiplexing is a feature of MIMO networks that allows for the concurrent transmission of multiple data streams. Nevertheless, there is a correlation between space multiplexing and the advantages of diversity. The issue of Secrecy Sum Rate (SSR) is complex and challenging. To address this, a first-order Taylor estimate is employed to transform the initial problem into a more manageable convex problem. Striking the right balance to optimize network performance and maximize overall rate can be quite challenging, particularly in time-sensitive scenarios. The total rate that can be achieved is reduced due to interference from different users and the use of multiple transmitting antennas. To optimize performance and achieve the highest possible data transmission rate, it is crucial to employ efficient techniques for managing interference. These may include coding methods, adjustments to positioning, or coordinating multiple users. Thus, this work proposes the utilization of optimization to enhance SSR in the MIMONOMA uplink transmission framework. The Singular Value Decomposition (SVD) method is commonly used for the decomposition of channels in the MIMONOMA system. The primary goal of the developed MIMONOMA uplink transmission framework is to enhance the capacity of the Base Station (BS) by optimizing the power allocation measures of the near user and far user using an Improved Migration Algorithm (IMA). In addition, the SSR is optimized and multiple experimental validations are generated to appraise the suggested model. Ultimately, the suggested outcomes are contracted with conventional Orthogonal Multiple Access (OMA) and other traditional approaches to assess the efficacy of the designed model, and developed model given the SSR to be 30bit/s/Hz.

Concept of Sporadic E Monitoring Using Space-Based Low Power Multiple Beacon-Systems

Current monitoring systems to detect sporadic E use ground-based setups, ionosondes, and the network of GNSS satellites in order to assess the phenomenon of sporadic E. This paper aims to monitor sporadic E using a miniature space-based platform in an atypical way. The setup consists of multiple radio-amateur beacon systems aboard satellites, each having a specific modulation and transmission scheme. This Radio Amateur Beacon System for the Investigation of the Ionosphere (RABSII) is coupled to a GNSS receiver, revealing the location of the platform. Multiple beacon data streams are sequentially sent from a satellite platform towards the Earth. By receiving and comparing the Signal-to-Noise ratios of these streams using a dedicated ground-based radio-amateur network of receiving stations, the presence of sporadic E can be determined, and a location-based model can be built. The advantage of this miniaturized, low-power, low-cost instrument is its ability to be put on any satellite platform in the future in order to map sporadic E.

Machine learning-based intelligent localization technique for channel classification in massive MIMO

Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

Performance investigation of UOWC system based on OAM beams for various Jerlov water types

This paper introduces a novel high-speed underwater optical wireless communication (UOWC) system employing three distinct orbital angular momentum (OAM) beams. A single green laser source operating at 532 nm generates these beams: LG0,0,LG0,20,andLG0,50, each transmitting data at 10 Gbps. The paper provides a comprehensive analysis of absorption and scattering coefficients for five Jerlov water types: I (JI), IA (JIA), IB (JIB), II (JII), and III (JIII). Simulation results demonstrate the system ability to transmit multiple data streams simultaneously using distinct OAM modes, achieving an overall capacity of 30 Gbps. The longest underwater (UW) transmission distance of 22 m is achieved in JI water as it exhibits the lowest attenuation. This range decreases by 9.09 %, 31.82 %, 59.09 %, and 80.91 % in JIA, JIB, JII, and JIII, respectively, due to increased attenuation in these water types. These results are obtained with a log (BER) below −5 and a Q-factor above 4, indicating successful data reception. The findings highlight the potential of OAM multiplexing for enhancing data capacity in challenging underwater environments.

A multimodal approach to support teacher, researcher and AI collaboration in STEM+C learning environments

AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross a difficulty threshold, and (2) the point of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topic Collaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains. Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments. Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI. What this paper adds We extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration. We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom. From our discussions with the teacher, we define two types of inflection points to address students' STEM+C difficulties—the difficulty threshold and the intervention point—and discuss how the feedback latency interval separating them can inform educator interventions. We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline. Implications for practice and/or policy Our case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback. Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment. Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula.

Variegated Platform Urbanism: Social Credit and the City

The development of a digital social credit system in China has sparked debates around urban governance and citizenship performance. Although China’s social credit system is portrayed as a single technical system, the article offers a new perspective on digital social credit focused on geographical variegation. We analyze how variegation is manifested through use of multiple data streams and a diverse range of aims operationalized in various social credit systems at the municipal level. Social credit can be seen as part of algorithmic urban governance and an instrument of urban citizenship performance through scoring, ranking, incentives, and punishments. Analysis of social credit systems’ development in Chinese cities contributes to debates around the role of the urban Global East and evolution of digital and algorithmic governance in and beyond China.

Role of artificial intelligence in critical care nutrition support and research.

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

A generative adversarial network to Reinhard stain normalization for histopathology image analysis

Histopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues. This research work presents a new method that merges deep learning with Reinhardstain normalization, aiming to revolutionize histopathology image analysis. The multi-data stream attention-based generative adversarial network is an innovative architecture designed to enhance histopathological image analysis by integrating multiple data streams, attention mechanisms, and generative adversarial networks for improved feature extraction and image quality. Multi-data stream attention-based generative adversarial network capitalizes on attention mechanisms and generative adversarial networks to process multi-modal data efficiently, enhancing feature extraction and ensuring robust performance even in the presence of staining variations. This approach excels in exact disease detection and classification, emerging as an invaluable tool for both clinical diagnoses and research endeavors across diverse datasets. The obtained accuracy of the proposed method for the SCAN dataset is 97.75%, the BACH dataset is 99.50% and the Break His dataset is 99.66%. The proposed method significantly advances histopathology image analysis, offering improved diagnostic accuracy and deeper insights by integrating multi-data streams, attention mechanisms, and generative adversarial networks. This innovative approach enhances feature extraction, image quality, and overall effectiveness in medical image analysis.

A Survey of Visual Transformers.

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern convolution neural networks (CNNs). In this survey, we have reviewed over 100 of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, two promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.

From Classification to Clinical Insights

Passively collected behavioral health data from ubiquitous sensors could provide mental health professionals valuable insights into patient's daily lives, but such efforts are impeded by disparate metrics, lack of interoperability, and unclear correlations between the measured signals and an individual's mental health. To address these challenges, we pioneer the exploration of large language models (LLMs) to synthesize clinically relevant insights from multi-sensor data. We develop chain-of-thought prompting methods to generate LLM reasoning on how data pertaining to activity, sleep and social interaction relate to conditions such as depression and anxiety. We then prompt the LLM to perform binary classification, achieving accuracies of 61.1%, exceeding the state of the art. We find models like GPT-4 correctly reference numerical data 75% of the time. While we began our investigation by developing methods to use LLMs to output binary classifications for conditions like depression, we find instead that their greatest potential value to clinicians lies not in diagnostic classification, but rather in rigorous analysis of diverse self-tracking data to generate natural language summaries that synthesize multiple data streams and identify potential concerns. Clinicians envisioned using these insights in a variety of ways, principally for fostering collaborative investigation with patients to strengthen the therapeutic alliance and guide treatment. We describe this collaborative engagement, additional envisioned uses, and associated concerns that must be addressed before adoption in real-world contexts.

Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site.

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long-lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought-induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model-data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post-drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust-boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought-free conditions. Overall, allocation shifts accounted for 65% [45%-75%] of drought legacy effects in modeled NBE. In summary, drought-induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land-atmosphere CO2 exchanges and should be accounted for in ecosystem models.

Breathe Easy, an automated respiratory data pipeline for waveform characteristic analysis

Whole body barometric flow through plethysmography is used to study respiratory output in rodent genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams for respiratory and metabolic parameters. Comprehensive and accurate data analysis is crucial to modelling congenital, pathogenic, and degenerative diseases. However, such studies produce large amounts of data that are time-consuming and diffcult to analyze. The investigator may use cumbersome hand annotation subject to observer bias or a series of software products to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems limiting options for bespoke respiratory measurement systems. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. Here, we present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs, and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface (GUI) for uploading data files, setting waveform feature thresholds, and defining experimental variables. After loading the GUI a, python-based program, Signal Analysis Selection and Segmentation Integration (SASSI) takes the user-settings and selects all quantitatively defined, quality breaths from each period of interest in the signal files. After SASSI, the STatistics and Graph Generator (STAGG) takes those breaths and performs linear mixed effects modeling based on user selections for each dependent variable of interest. Furthermore, our software can integrate non-respiratory experimental outcomes including quantified behavioral or gene expression data to test for breathing related associations. To validate breath selection and generate optimized default values, results from Breathe Easy were compared to manual selection by experts, which represents the current standard in the field. We show high quality breath selection and a significant reduction in quality of breaths that were rejected by the software. We also demonstrate Breathe Easy’s utility and capacity by examining a 2-year longitudinal study of an Alzheimer’s Disease mouse model to assess contributions of forebrain pathology in disordered breathing (600+ files; 1TB data). We show that, at least with this model of AD, there does not seem to be an association between forebrain pathology and respiratory outcomes. Breathe Easy offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. NIH: 1F32HL160073-01A1, R01HL130249 44617-S4. BCM McNair Scholar Program, March of Dimes Basil O'Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A two-stage monitoring scheme for multiple high-dimensional data streams

With the rapid development of data acquisition and analysis technology, the online monitoring of data streams is particularly important in modern industries. This study proposes an efficient online monitoring scheme for multiple high-dimensional data streams. We first construct a point-wise process monitoring control chart based on statistical extreme value theory. Then we use the clustering method to roughly obtain reliable data streams and suspected abnormal data streams, within the sliding window range, further identify abnormal data streams one by one over time points, and estimate the potential change points of these abnormal streams. Finally, we conduct simulations and case studies on this method, and compare it with existing competitive methods to demonstrate its effectiveness.

Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities

Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders' values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

AbstractLow clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%.

A Statistical Model for Multisource Remote-Sensing Data Streams of Wildfire Aerosol Optical Depth

Increasingly frequent wildfires have significant impact on solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation. Atmospheric aerosols can be measured by aerosol optical depth (AOD), and multiple spatiotemporal AOD data streams are available from geostationary satellites. Although these multisource remote-sensing data streams are measurements of the same underlying AOD process, they often present heterogeneous characteristics, such as different measurement biases and errors, data missing rates, etc. It is usually not known which data source provides more accurate measurements, and simply averaging multiple data streams is not always the best practice. Hence, this paper proposes a statistical model that is capable of inferring the underlying true AOD process by simultaneously integrating heterogeneous multisource remote-sensing data streams and the advection-diffusion equation that governs the dynamics of the AOD process. A bias correction process is included in the model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data obtained from the GOES East (GOES-16) and GOES West (GOES-17) satellites operated by the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model the interpretability of the proposed approach. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This work was funded by the U.S. National Science Foundation [Grant 2143695]. Data Ethics & Reproducibility Note: Our code requires packages that are not available on CodeOcean (for example, the Rfast package in R). Hence, code and sample data are provided on GitHub: https://github.com/gz-wei/Physics-Informed-Statistical-Modeling-for-Wildfire-Aerosols-Propagation . The online appendix is available at https://doi.org/10.1287/ijds.2021.0058 .

Multichannel signal transmission and reception using compact multichannel underwater communication devices: A unified theory and experimental results from different underwater media

Simultaneous transmission of multiple signals over the same bandwidth allows for optimal utilization of the limited bandwidth in underwater systems and networks. To avoid using multiple spatially separated single-channel projectors, one compact multichannel device can be used instead [E. Zhang, R. Rashid, A. Abdi, “Particle velocity underwater data communication: Physics, channels, system and experiments,” IEEE J. Oceanic Eng. (2023); E. Zhang, R. Rashid, A. Abdi, “Underwater communication experiments for transmitting multiple data streams using a vector acoustic MIMO system: OFDM and FSK modulations,” Proc. Oceans (2023)]. Simultaneous reception of multiple signals using one compact multichannel device is also feasible and improves the communication performance, without using several spatially separated single-channel hydrophones [R. Rashid, E. Zhang, A. Abdi, “On the performance of a new wireless communication compact multichannel underwater receiver using a sphere vector sensor,” IEEE Transactions on Vehicular Technology (2023); R. Rashid, E. Zhang, A. Abdi, “Underwater acoustic signal acquisition and sensing using a ring vector sensor communication receiver: Theory and experiments,” Sensors (2023)]. In this paper, we present various experimental results using a number of compact multichannel underwater devices in different setups, and demonstrate their usefulness for underwater communication. [Work supported in part by NSF, Grant IIP-1500123].

PaCHNOC: Packet and Circuit Hybrid Switching NoC for Real-Time Parallel Stream Signal Processing.

Real-time heterogeneous parallel embedded digital signal processor (DSP) systems process multiple data streams in parallel in a stringent time interval. This type of system on chip (SoC) requires the network on chip (NoC) to establish multiple symbiotic parallel data transmission paths with ultra-low transmission latency in real time. Our early NoC research PCCNOC meets this need. The PCCNOC uses packet routing to establish and lock a transmission circuit, so that PCCNOC is perfectly suitable for ultra-low latency and high-bandwidth transmission of long data packets. However, a parallel multi-data stream DSP system also needs to transmit roughly the same number of short data packets for job configuration and job execution status reports. While transferring short data packets, the link establishment routing delay of short data packets becomes relatively obvious. Our further research, thus, introduced PaCHNOC, a hybrid NoC in which long data packets are transmitted through a circuit established and locked by routing, and short data packets are attached to the routing packet and the transmission is completed during the routing process, thus avoiding the PCCNOC setup delay. Simulation shows that PaCHNOC performs well in supporting real-time heterogeneous parallel embedded DSP systems and achieves overall latency reduction 65% compared with related works. Finally, we used PaCHNOC in the baseband subsystem of a real 5G base station, which proved that our research is the best NoC for baseband subsystem of 5G base stations, which reduce 31% comprehensive latency in comparison to related works.