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Deep Learning Assists Surveillance Experts: Toward Video data prioritization

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 7946-7956页

作者： Hussain, Tanveer Ullah, Fath U. Min Khan, Samee Ullah Ullah, Amin Haroon, Umair Muhammad, Khan Baik, Sung Wook de Albuquerque, Victor Hugo C. Sejong Univ Seoul 143747 South Korea Univ Leeds Inst Transport Studies Leeds LS2 9JT England Oregon State Univ Collaborat Robot & Intelligent Syst Inst Corvallis OR 97331 USA Sungkyunkwan Univ Coll Comp & Informat Sch Convergence Dept Appl Artificial IntelligenceVisual Analyt Kn Seoul 03063 South Korea Univ Fed Ceara Dept Teleinformat Engn BR-60455970 Fortaleza Brazil

Video summarization (VS) suppresses high-dimensional (HD) video data by only extracting the important information. However, prior research has not focused on the need for surveillance VS, that is used for many applications to assist video surveillance experts, including video retrieval and data storage. In addition, mainstream techniques commonly use two-dimensional (2-D) deep models for VS, ignoring event occurrences. Accordingly, we present a two-fold 3-D deep learning-assisted VS framework. First, we employ an inflated 3-D ConvNet model to extract temporal features;these features are optimized using a proposed encoder mechanism. The input video is temporally segmented using a feature comparison technique for selecting a single frame from each video segment. The segmented shots are evaluated using our novel shot segmentation evaluation scheme and are input into a saliency computation mechanism for keyframe selection in a second fold. Qualitative and quantitative analyses over VS benchmarks and surveillance videos demonstrate the superior performance of our framework, with 0.3- and 4.2-unit increases in the F1 scores for YouTube and title-based video summarization datasets, respectively. Along with accurate VS, a key contribution of our study is the novel shot segmentation criterion prior to VS, which can be used as a benchmark in future research to effectively prioritize HD visual data.

关键词： data prioritization I3D features saliency extraction video analytics video sensing video summarization

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A computer vision-based IoT data ingestion architecture supporting data prioritization

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HEALTH AND TECHNOLOGY 2023年第3期13卷 391-411页

作者： Kiourtis, Athanasios Mavrogiorgou, Argyro Kyriazis, Dimosthenis Univ Piraeus Dept Digital Syst Piraeus Greece Univ Piraeus 80 M Karaoli & A Dimitriou St Piraeus 18534 Greece

PurposeAs Internet of Things (IoT) evolves, additional focus should be provided in aggregating and ingesting the most valuable data. Several techniques aim to identify the data portions that can offer significant impact, targeting to distinguish the IoT devices' nature, whose applicability is lacking in simultaneous *** current manuscript proposes a data Ingestion and IoT data prioritization process, supporting the connection and integration of heterogeneous IoT medical devices with healthcare platforms, to ingest IoT data of any device and store them in the preferred Cloud Storage service. In sequel, a specific prioritization process is provided, through which the ingested data of the IoT device can be filtered, providing slices of data which are more significant to the requesting stakeholders. This is constructed to firstly ingest and then perform data prioritization to the data to be provided from a citizen's IoT device to the requesting Healthcare Practitioner, in which initial steps involve the connection of the IoT device to a specifically designed middleware, for identifying its nature and the programmatic methods being responsible for ingesting the device's data, using computer vision and semantic fingerprinting. The ingested data can then be segmented, provided with scores based on their significance, and receive metadata annotation about their nature. Subsequently, the data are sliced, being forwarded to the Healthcare Practitioner that requested them, following a methodology for metadata *** evaluation was performed with several types of data, ingesting in an efficient manner healthcare data from IoT devices of low or high type of priority. For the data Ingestion process, the extracted outcomes were that the overall process could always successfully connect all the IoT Bluetooth-enabled devices through the provided Bluetooth interface. With regards to the IoT data prioritization process, the extracted outcome was that IoT gath

关键词： IoT data ingestion data prioritization Metadata annotation Attribute scoring

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Generalized Time-Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and data prioritization Using Principal Components Analysis and Unsupervised Clustering

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EARTH AND SPACE SCIENCE 2024年第9期11卷

作者： Finley, Matthew G. Martinez-Ledesma, Miguel Paterson, William R. Argall, Matthew R. Miles, David M. Dorelli, John C. Zesta, Eftyhia Univ Maryland Dept Astron College Pk MD 20742 USA NASA Goddard Space Flight Ctr Geospace Phys Lab Greenbelt MD 20771 USA Catholic Univ Amer Dept Phys Washington DC USA Univ New Hampshire Space Sci Ctr Durham NH USA Univ Iowa Dept Phys & Astron Iowa City IA USA

In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions. Measurements captured by spacecraft are necessary to our understanding the space environment near Earth and throughout our solar system. However, spacecraft can often only transmit a small portion of the data they capture back to Earth. This means that many spacecraft must prioritize intervals of data that have the highest probability of helping to furth

关键词： anomaly detection data prioritization event detection onboard processing principal components analysis unsupervised clustering

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SCOTI: Science Captioning of Terrain Images for data prioritization and local image search

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PLANETARY AND SPACE SCIENCE 2020年 188卷 104943-104943页

作者： Qiu, Dicong Rothrock, Brandon Islam, Tanvir Didier, Annie K. Sun, Vivian Z. Mattmann, Chris A. Ono, Masahiro Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

Planetary exploration is full of challenges. data bandwidth is very limited between planetary rovers and ground-based data system. What's worse, even though NASA has accumulated over 34 million images from various missions, it requires significant effort and is hardly possible for any scientist to go through all of them. In order to improve the degree of automation and the efficiency of these processes, we propose a system leveraging machine learning for planetary rovers to actively look for scientifically interesting and valuable features according to text instructions from scientists and prioritize the images captured onboard with those features for downlink. Such an image prioritization mechanism can also be naturally applied to content-based image search through text description in any local planetary image data server, allowing scientists to search for images with desired features without going through them one by one. Besides theoretical and engineering details of our proposed approach, we also present both quantitative and qualitative evaluation of the system along with some concrete examples.

关键词： Planetary exploration data prioritization Image search Image captioning Machine learning

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data prioritization aware resource allocation in internet of vehicles using multi-agent deep reinforcement learning

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107671页

作者： Cong Wang Yingshan Guan Sancheng Peng Hao Chen Guorui Li School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 Hebei China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao 066004 Hebei China. Electronic address: congw@***. School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 Hebei China. Electronic address: ***@***. Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou 510006 Guangdong China. Electronic address: psc346@***. College of Information Science and Engineering Ritsumeikan University Ibaraki 567-8570 Osaka Japan. Electronic address: ***@***. Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao 066004 Hebei China. Electronic address: lgr@***.

Intelligent transportation systems (ITS) are facing the limitation of spectral resources and stringent real time communication requirements. How to effectively allocate system resources for maximizing the performance in Internet of Vehicles (IoV) is still a substantial challenge, particularly the priority and urgency of different types of data need to be focused. To improve the allocation spectrum resources and optimize transmission power while taking into the dynamic characteristics of vehicles and data priorities account, we design a time-series-based multi-agent deep reinforcement learning framework (NL-MAPPO for short), in this paper. First, we formulate the joint optimization problem as a multi-agent Markov decision process to ensure the minimization of transmission delays and energy consumption when the total vehicle-to-vehicle (V2V) link capacity is maximized. Here, V2V link capacity refers to the maximum achievable data rate for direct communication between vehicles, which depends on factors such as signal strength, interference, and available bandwidth. Then, we design a multi-agent resource allocation algorithm based on a shared-critic mechanism to realize the global sharing of channel information and solve the optimization problem. Finally, to improve efficiency, we also introduce a time series-based channel information extraction mechanism to capture the temporal characteristics of channel information. The simulation experiments were conducted and the results demonstrated that our proposed NL-MAPPO can demonstrate superiority in multiple metrics.

关键词： data prioritization Internet of vehicles Mobile edge computing Multi-agent deep reinforcement learning Spectrum resource allocation

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DPCA: data prioritization and Capacity Assignment in Wireless Sensor Networks

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IEEE ACCESS 2017年 5卷 14991-15000页

作者： Siddiqi, Taj Rahman Ning, Huansheng Ping, Haodi Mahmood, Zahid Univ Sci & Technol Beijing Dept Comp Sci & Commun Engn Beijing 100083 Peoples R China China Univ Geosci Beijing 100083 Peoples R China

Scheduling different types of data packets, such as high or low priority data packets at the sender node, is important for reducing energy and capacity consumptions and end-to-end delay. Current scheduling schemes of wireless sensor networks use preemptive and non-preemptive scheduling algorithms, which incur relatively long end-to-end transmission delay and high processing overhead. Besides, they do not consider the path capacity, which represents the capacity of the network for transferring as much as sensory data to the sink node(s). Consequently, sensory data are routed to the sink node(s), whatever they are more or less, important for supporting domain applications. To remedy this issue, we propose a method, which differentiates between high and low priority when routing sensory data to the sink node(s). Specifically, the priority of sensory data is determined through a novel capacity assignment mechanism. When the network capacity, which depends on the capacity of routing paths, may not be sufficient for supporting the sensory data routing requirement, sensory data with a relatively high priority should be routed to the sink node(s), while that with a relatively low priority may be decreased or prohibited. Experimental evaluation has been conducted, and the result shows that congestion and packets dropping are reduced, when sensory data can be differentiated in their priority and the network traffic is relatively heavy.

关键词： Wireless sensor networks path capacity data prioritization

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Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第4期38卷

作者： Menaga, S. Vanithamani, R. Avinashilingam Inst Home Sci & Higher Educ Women Sch Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India Avinashilingam Inst Home Sci & Higher Educ Women Sch Engn Dept Biomed Instrumentat Engn Coimbatore Tamil Nadu India

Wireless body area network (WBAN), a popular radio communication technique, tracks patients' health conditions remotely using small, low-power, portable sensors positioned on the body to sense vital signs and send data to a control node. To sustain long-term health monitoring, WBANs require significant energy from the nodes. This research proposes an energy efficient and reliable data transmission using multivariate gradient divergence African buffalo optimization (EEMGDABO) to minimize the delay of critical node data transmission by identifying the optimal path to the control node. The protocol comprises two key processes: winsorized correlative segmented symbolic regression with reinforcement learning (WCS-SRRL) and the walrus optimization algorithm (WaOA) for accurate priority-based classification of health data (normal, abnormal, and critical). The second process involves transferring these priority-based data with reduced energy consumption and delay while increasing throughput by determining the best path from the critical node to the destination (hospital/doctor) using EEMGDABO, which calculates node distance and energy fitness value. Implemented in MATLAB with a health monitoring WBAN dataset, the proposed protocol demonstrates 23% less energy consumption, 12.33% less delay, and 23.44% higher network lifetime compared to existing optimization approaches.

关键词： data prioritization energy efficiency multivariate gradient Bregman divergence African buffalo optimization winsorized correlative segmented linear regression wireless body area network

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ReDeSIGN: Reuse of Debug Structures for Improvement in Performance Gain of NoC Based MPSoCs

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2023年第2期11卷 432-447页

作者： Rout, Sidhartha Sankar Badri, M. Sinha, Mitali Deb, Sujay Indraprastha Inst Informat Technol Delhi Dept Elect & Commun Engn Delhi 110020 India Indraprastha Inst Informat Technol Delhi Dept Comp Sci & Engn Delhi 110020 India

Network-on-Chip (NoC) is considered as a scalable interconnect medium for Multiprocessor System-on-Chip (MPSoC) due to its ability to provide high bandwidth and low latency communication. With the increasing intricacy of the modern-day systems, the state-of-the-art NoCs are becoming extremely complex. Design-for-Debug (DFD) structures are integrated to the system for the validation of such complex modules during post-silicon debug. However, after the system validation and mass production, the DFD hardware remains vestigial on the design. In this context, we propose ReDeSIGN, a framework to reuse the DFD infrastructure during the in-field operation for performance enhancement of the NoC-based MPSoCs. Major contributions of our work include reuse of (i) trace buffer as extended Virtual Channel (VC) for network throughput improvement, (ii) trace prioritization hardware for critical data prioritization, and (iii) packet monitor module for packet starvation control. Experimental evaluations with real benchmarks show an average of 11.46% increase in network throughput, 34.93% decrease in critical data latency, and 19.17% decrease in packet starvation for an 8x8 homogeneous system.

关键词： data prioritization debug structure reuse design-for-debug (DFD) network-on-chip (NoC) post-silicon debug system validation starvation control trace buffer virtual channel (VC)

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prioritization of Mobile IoT data Transmission Based on data Importance Extracted From Machine Learning Model

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IEEE ACCESS 2019年 7卷 93611-93620页

作者： Inagaki, Yuichi Shinkuma, Ryoichi Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

Predicting real-time spatial information from data collected by the mobile Internet of Things (IoT) devices is one solution to the social problems related to road traffic. The mobile IoT devices for real-time spatial information prediction generate an extremely high volume of data, making it impossible to collect all of it through mobile networks. Although some previous works have reduced the volume of transmitted data, the prediction accuracy of real-time spatial information is still not ensured. Therefore, this paper proposes an IoT device control system that reduces the amount of transmitted data used as input for real-time prediction while maintaining the prediction accuracy. The main contribution of this paper is that the proposed system controls data transmission from the mobile IoT devices based on the importance of data extracted from the machine learning model used for the prediction. Feature selection has been widely used for extracting the importance of data from the machine learning model. Feature selection methods were also used to reduce communication overhead in distributed learning. Unlike the conventional usage of feature selection methods, the proposed system uses them to control the data transmission of the mobile IoT devices with priority. In this paper, the proposed system is evaluated with a real-world vehicle mobility dataset in two practical scenarios using the random forest model, which is an extensively used machine learning model. The evaluation results show that the proposed system reduces the amount of transmitted input data for real-time prediction while achieving the same level of prediction accuracy as benchmark methods.

关键词： Real-time spatial information vehicular IoT data prioritization machine learning feature selection

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A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis

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NEURON 2023年第14期111卷 2126-2139页

作者： Rollo, Jennifer Crawford, John Hardy, John UCL Queen Sq Inst Neurol Dept Neurodegenerat Dis Queen Sq London WC1N 3BG England Univ Glasgow Adam Smith Business Sch Glasgow G12 8QQ Scotland

Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.

关键词： Alzheimer’s disease dementia dynamical systems modeling systems pathology systems biology multiscale data data prioritization participatory methodology open innovation ecosystem mathematical modeling

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