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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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A Study on the Voice Security System Using sensor Technology

A Study on the Voice Security System Using Sensor Technology

引用

ieee international conference on Big Data and Smart computing (BigComp)

作者： Seong, Ji-won Lee, Hyo-jun Cho, Si-heon Inha Univ Dept Ind Secur Governance Incheon South Korea

ISBN: (纸本)9781728160344

A person goes through a speaking and listening process that repeats verbal, physiological, and acoustic steps for communication. Voice recognition technology utilizing a range of sensor technologies using biosignals that are measurable in these human voice activities has developed rapidly in recent years. In particular, with the development of voice recognition technology based on artificial intelligence, the related market has expanded and is being released into various services. But the problem with voice recognition still exists and there are more cases of abuse. In addition, despite advances in technology, there is a problem where the associated services are not available to patients with laryngectomy or impaired pronunciation. Therefore, this paper focuses on silent recognition, which does not require vocalization, and introduces the technologies that may be used in implementing voice security systems in the future, including sensor technology, and further discusses the need to consider issues and support policies related to privacy when using the technology.

关键词： sensor technology Biosignal Voice System

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Intelligent Management System for Industrial Sugar Hoppers in Food Manufacturing Using IoT

Intelligent Management System for Industrial Sugar Hoppers i...

引用

ieee Region 10 international conference TENCON

作者： Earl Jewel M. Arel Jenica Charlize L. Chua Matthew Joseph C. Dionela Martina A. Fortuna Alrick Wynton T. Lim Jason L. Española Dino Dominic F. Ligutan Ryan Rhay P. Vicerra Argel A. Bandala Elmer P. Dadios Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Department of Electronics and Computer Engineering De La Salle University Manila Philippines

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Food manufacturing industries utilize various bulk raw materials to produce finished goods, driving advancements in inventory management through research and innovation. Currently, the primary technology for managing bulk materials Inventory is the hopper, which feeds materials to the assembly line on demand. However, manual refilling by operators introduces safety risks and inefficiencies under lean manufacturing principles. To address these challenges, this study developed an intelligent management system for a smaller-scale sugar hopper. The system automates refilling processes triggered by low levels or empty conditions using infrared sensors to monitor the hopper status. A servo motor actuator controls a reservoir that gravity-feeds sugar into the hopper. Automation is facilitated by Arduino Cloud programming, offering real-time monitoring through a user dashboard. Results demonstrated efficient performance, initiating auto-refilling in an average of 2.706 seconds. The system halts refilling accurately when sufficient sugar is detected, maintaining a consistent flow rate of 8.7g/s with minimal deviation (1.5g/s). This study provides a functional proof-of-concept for an intelligent management system applicable to small-scale sugar hoppers, scalable for broader industrial applications.

关键词： Cloud computing Food manufacturing sensor phenomena and characterization Programming sensor systems and applications Reservoirs Sugar industry sensors Safety Time factors

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Performance Evaluation of Secure and Privacy-preserving DNS at the 5G Edge

Performance Evaluation of Secure and Privacy-preserving DNS ...

引用

ieee Internatonal conference on Mobile Adhoc and sensor systems (MASS)

作者： Yacoub Hanna Diana Pineda Kemal Akkaya Abdullah Aydeger Ricardo Harrilal-Parchment Hamdah Albalawi Advanced Wireless and Security Lab Florida International University Miami FL USA Dept. of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA

With the improved network performance and efficiency, 5G has been a very appealing alternative for various applications and devices, including but not limited to Industrial IoT (IIoT) applications. However, since IIoT applications require real-time transmission guarantees for time-critical data, optimizing the 5G network performance has attracted much research recently by pushing critical services to the 5G edge. One of such services is Domain Name System (DNS) which is typically offered by ISPs to serve 5G networks. However, due to its heavy role on Internet traffic, DNS has seen many attacks in the past in terms of its authenticity of records and exposure of user requests. Therefore, there have been many variants of DNS protocol such as DNSSEC and DNS over TLS (DoT) to address these security and privacy issues. As such these new protocols need to be integrated into 5G edge and their overhead should be investigated for their potential use in time-critical applications. In this paper, we consider deploying secure and privacy-preserving DNS to the 5G base stations and investigate the feasibility and performance of such an edge computing approach. To this end, we utilize SDN capabilities and forward packets to a local SDN controller for extracting and processing DNS queries. Thus, the DNS packets are served at the 5G edge with optional security and privacy features without being forwarded to remote ISP servers. Our approach is implemented in a virtualized 5G testbed network, and we present various DNS performance results via different metrics and scenarios.

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Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution 26

Massively Parallel Causal Inference of Whole Brain Dynamics ...

引用

26th ieee international conference on Parallel and distributed systems (ieee ICPADS)

作者： Watanakeesuntorn, Wassapon Takahashi, Keichi Ichikawa, Kohei Park, Joseph Sugihara, George Takano, Ryousei Haga, Jason Pao, Gerald M. Nara Inst Sci & Technol Nara Japan US Dept Interior Gainesville FL USA Univ Calif San Diego San Diego CA 92103 USA Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Salk Inst Biol Studies 10010 N Torrey Pines Rd La Jolla CA 92037 USA

ISBN: (纸本)9781728190747

Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to computational cost. With the growth of data collection capabilities, there is a great need to identify causal relationships in large datasets. We present mpEDM, a parallel distributed implementation of EDM optimized for modern GPU-centric supercomputers. We improve the original algorithm to reduce redundant computation and optimize the implementation to fully utilize hardware resources such as GPUs and SIMD units. As a use case, we run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an entire animal brain sampled at single neuron resolution to identify dynamical causation patterns across the brain. mpEDM is 1,530x faster than cppEDM and a dataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes. This is the largest EDM causal inference achieved to date.

关键词： Empirical Dynamic Modeling Causal Inference Parallel distributed computing GPU High-Performance computing Neuroscience

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Data-driven soft sensing towards quality monitoring of industrial pasteurization processes

Data-driven soft sensing towards quality monitoring of indus...

引用

international conference on distributed computing in sensor systems (DCOSS)

作者： Gabriel Filios Andreas Kyriakopoulos Stavros Livanios Fotis Manolopoulos Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Heineken Patras Plant Greece Department of Computer Science University of Liverpool UK

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

In the food and beverage industry many foods, beers and soft drinks usually need to get pasteurized, a process that holds a significant role in the quality and taste of the final product but is difficult to monitor due to the process nature. Soft sensing techniques, also called virtual sensing or surrogate sensing, can be leveraged to monitor the product quality, by using information available from other measurements and process parameters to calculate an estimation of the quantity of interest. In this paper, we develop a soft sensing methodology that is based on machine learning algorithms for continuous, end-to-end estimation of the temperature of products during the pasteurization process, with the vision to serve as an intermediate step towards monitoring live the final quality of the pasteurized products. This work studies a real beer pasteurization process in collaboration with Heineken’s plant in Patras, Greece and the results demonstrate notable performance in temperature prediction accuracy, with average root mean square error (RMSE) of 1.85°C in the test sets. Thus, we claim that it is possible to obtain measurements quite similar to the ones by the respective physical sensors with sufficient accuracy, and our methodology can be considered as a virtual low-cost solution for monitoring product quality in legacy pasteurizer operation.

关键词： Temperature measurement Temperature sensors Machine learning algorithms Soft sensors Estimation Product design sensor systems

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On Link Performance According to Flight Route in Drone-Based Wide Area Wireless sensor Networks 1

On Link Performance According to Flight Route in Drone-Based...

引用

ieee international conference on Space-Air-Ground computing (ieee SAGC)

作者： Rahmadya, Budi Umehira, Masahiro Wang, Xiaoyan Takeda, Shigeki Ibaraki Univ Grad Sch Sci & Engn Hitachi Ibaraki Japan Andalas Univ Comp Engn Dept Padang Indonesia

ISBN: (纸本)9781665419406

This paper proposes a system concept of drone based wireless sensor networks where the drone flies to the sensor heads distributed in wide areas and collects the sensing data from them by using short range wireless communications. It also describes link performance according to flight route in the proposed sensor networks. Received signal level at the drone is calculated by using two path model and taking account of the antenna directivity. This paper compares two drone flight route alternatives regarding the outage probability of wireless link between the sensor head and the drone. The evaluated results reveal eat that the flight route with variable flight height achieves Inner outage probability than that with a constant flight height.

关键词： drone wireless sensor network received signal level outage probability flight route

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TEEp: Supporting Secure Parallel Processing in ARM TrustZone 26

TEEp: Supporting Secure Parallel Processing in ARM TrustZone

引用

26th ieee international conference on Parallel and distributed systems (ieee ICPADS)

作者： Li, Zinan Li, Wenhao Xia, Yubin Zang, Binyu Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China

ISBN: (纸本)9781728190747

Machine learning applications are getting prevelent on various computing platforms, including cloud servers, smart phones, IoT devices, etc. For these applications, security is one of the most emergent requirements. While trusted execution environment (TEE) like ARM TrustZone has been widely used to protect critical prodecures including fingerprint authentication and mobile payment, state-of-the-art implementations of TEE OS lack the support for multi-threading and are not suitable for computing-intensive workloads. This is because current TEE OSes are usually designed for hosting security critical tasks, which are typically small and non computing-intensive. Thus, most of TEE OSes do not support multi-threading in order to minimize the size of the trusted computing base (TCB). In this paper, we propose TEEp, a system that enables multi-threading in TEE without weakening security, and supports existing multi-threaded applications to run directly in TEE. Our design includes a novel multi-threading mechanism based on the cooperation between the TEE OS and the host OS, without trusting the host OS. We implement our system based on OP-TEE and port it to two platforms: a HiKey 970 development board as mobile platform, and a Huawei Hi1610 ARM server as server platform. We run TensorFlow Lite on the development board and TensorFlow on the server for performance evaluation in TEE. The result shows that our system can improve the throughput of TensorFlow Lite on 5 models to 3.2x when 4 cores are available, with 13.5% overhead compared with Linux on average.

关键词： Multi-threading Parallel processing TEE Trust-Zone

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Towards Collaborative Intrusion Detection Enhancement against Insider Attacks with Multi-Level Trust 19

Towards Collaborative Intrusion Detection Enhancement agains...

引用

19th ieee international conference on Trust, Security and Privacy in computing and Communications (ieee TrustCom)

作者： Li, Wenjuan Meng, Weizhi Zhu, Hui Guangzhou Univ Inst Artificial Intelligence & Blockchain Guangzhou Peoples R China Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

ISBN: (纸本)9781665403924

With the speedy growth of distributed networks such as Internet of Things (IoT), there is an increasing need to protect network security against various attacks by deploying collaborative intrusion detection systems (CIDSs), which allow different detector nodes to exchange required information and data with each other. While due to the distributed architecture, insider attacks are a big threat for CIDSs, in which an attacker can reside inside the network. To address this issue, designing an appropriate trust management scheme is considered as an effective solution. In this work, we first analyze the development of CIDSs in the past decades and identify the major challenges on building an effective trust management scheme. Then we introduce a generic framework aiming to enhance the security of CIDSs against advanced insider threats by deriving multi-level trust. In the study, our results demonstrate the viability and the effectiveness of our framework.

关键词： Collaborative Intrusion Detection Insider Threat Trust Management Multi-Level Trust distributed Network

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17th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2022

17th International Conference on Soft Computing Models in In...

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17th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2022

ISBN: (纸本)9783031180491

The proceedings contain 64 papers. The special focus in this conference is on Soft computing Models in Industrial and Environmental Applications. The topics include: An Extremal Optimization Approach to the Pairwise Connectivity Critical Node Detection Problem;dimensional Reduction Applied to an Intelligent Model for Boost Converter Switching Operation;Intuitionistic Fuzzy Sets in J-CO-QL + ?;assessing the Efficient Market Hypothesis for Cryptocurrencies with High-Frequency Data Using Time Series Classification;blockchain for Supply Chain Traceability with Data Validation;compression of Clustered Ship Trajectories for Context Learning and Anomaly Detection;DR Participants’ Actual Response Prediction Using Artificial Neural Networks;Non-linear Neural Models to Predict HRC Steel Price in Spain;first Steps Predicting Execution of Civil Works from Georeferenced Infrastructure Data;predictive Maintenance for Maintenance-Effective Manufacturing Using Machine Learning Approaches;virtual sensor to Estimate Air Pollution Heavy Metals Using Bioindicators;regression Techniques to Predict the Growth of Potato Tubers;reliability-Sensitive Optimization for Provision of Ancillary Services by Tempo-Spatial Correlated distributed Energy Resources;Predictive Maintenance of ATM Machines by Modelling Remaining Useful Life with Machine Learning Techniques;the Impact of Content Deletion on Tabular Data Similarity Using Contextual Word Embeddings;deep Learning-Based Dementia Prediction Using Multimodal Data;lightweight Models in Face Attribute Recognition: Performance Under Oclussions;small Vessel Detection in Changing Seaborne Environments Using Anchor-Free Detectors on Aerial Images;Improving Malware Detection with a Novel Dataset Based on API Calls;identifying Places Using Multimodal Social Network Data;estimation of Lamb Weight Using Transfer Learning and Regression;monitoring Human Performance Through Deep Learning and Computer Vision in Industry 4.0;automatic Fish Size Estimation fr

关键词： Forestry

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Load Balanced Controller Association in Wireless distributed SDNs 18

Load Balanced Controller Association in Wireless Distributed...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Starke, Allen McNair, Janise Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781728147161

Wireless infrastructure is steadily evolving into wireless access for all humans and most devices, from 5G to Internetof-Things. This widespread access creates the expectation of custom and adaptive services from the personal network to the backbone network. In addition, challenges of scale and interoperability exist across networks, applications and services, requiring an effective wireless network management infrastructure. For this reason Software-Defined Networks (SDN) have become an attractive research area for wireless and mobile systems. SDN can respond to sporadic topology issues such as dropped packets, message latency, and/or conflicting resource management, to improved collaboration between mobile access points, reduced interference and increased security options. Until recently, the main focus on wireless SDN has been a more centralized approach, which has issues with scalability, fault tolerance, and security. In this work, we propose a state of the art WAMSDN system for large-scale network management. We discuss requirements for large scale wireless distributed WAM-SDN and provide preliminary benchmarking and performance analysis based on our hybrid distributed and decentralized architecture.

关键词： Network management

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