Experiments With Sensor Motes and Java-DSP

Kwon, Homin; Berisha, Visar; Atti, Venkatraman; Spanias, Andreas

doi:10.1109/te.2008.927691

Cited by 18 publications

(8 citation statements)

References 12 publications

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“…The program required students to first attend a boot camp and receive online training in signal processing, ML and sensors. To accelerate learning we started the training in signal processing and ML using the object oriented J-DSP software [21][22][23][24] which has built in modules for k-means (Fig. 4) and other clustering methods.…”

Section: Reu In Sensors Devices and ML Algorithmsmentioning

confidence: 99%

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias

2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

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This paper accompanies the keynote speech at IISA-2020 and describes federally funded workforce development research grants and supplements in the area of sensors and machine learning. These programs operate under the auspices of the Sensor Signal and Information Processing (SenSIP) center which is also an Industry University Cooperative Research Center (I/UCRC) sponsored by the National Science Foundation (NSF) and I/UCRC industry members. The first program is an NSF REU site which has trained more than 30 students working on sensor hardware design and machine learning algorithm development. The second program is the NSF IRES site which is collaborative with the University of Cyprus and is focused on sensors and machine learning for energy systems. The most recent program funded by NSF is a Research Experiences for Teachers (RET) program that started in June 2020. This program embeds teachers and community college faculty in SenSIP machine learning projects. Another state funded program in which SenSIP is a partner is MedTech ventures. Our partner MedTech works on training medical technology students, entrepreneurs and engineers to create smart medical solutions for preventive healthcare. SenSIP also received NSF supplements to train students in using machine learning for COVID-19 detection.

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Section: Reu In Sensors Devices and ML Algorithmsmentioning

confidence: 99%

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Spanias

2020

2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA

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“…The need for two-way asynchronous communication was resolved by an adjective producer-consumer model. Furthermore, a complete Java-based framework for carrying out DSP tasks for wireless sensor networks applications has been proposed in [9], [10].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Prototyping of Matlab-Java Code Integration for Water Sensor Networks Applications

Roubakis

Tzagkarakis

Tsakalides

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Industrial applications typically necessitate the interaction of heterogeneous software components, which makes the design of an integrated system a demanding task. Specifically, although Matlab ® and Java are among the most commonly used programming languages in industrial practice, with each one offering its own advantages, however, their integration for realtime code prototyping is not straightforward. Motivated by this problem, this work proposes an efficient method based on the use of sockets to integrate Matlab and Java code for designing a data processing platform tailored to smart water sensor networks scenarios. The performance of the proposed approach is evaluated on two distinct tasks, namely, the recovery of missing values and the temporal super-resolution from streaming data. Experimental evaluation with real pressure data reveals the superiority of our methodology, in terms of reduced execution times, when compared against two well-established alternatives, namely, the use of standalone applications using input-output files for executing Matlab code in Java-based environments and socket-based solutions implemented directly in a Matlab environment.Index Terms-Matlab-Java code integration, client-server model, real-time prototyping, water sensor networks

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“…The impact of computer methods on engineering education has led to the development of several tools including Java-DSP [1,2], a web-based visual programming environment with several functions enabling students to perform online DSP simulations. The emergence of powerful mobile devices has provides new opportunities for engaging students anytime/anywhere by providing interactive signal analysis capabilities with enhanced visual representations.…”

Section: Introductionmentioning

confidence: 99%

Embedding Android signal processing apps in a high school math class — An RET project

Banavar

Rajan

Strom³

et al. 2014

2014 IEEE Frontiers in Education Conference (FIE) Proceedings

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The objective of this project is to develop and design mobile content for introducing engineering technology to high school students. More specifically, we intend to work on a sequence of modules that will establish connections between high school mathematics and physics to modern technologies associated with smart phones, iPods and other high-tech products. The participants of the project will use the previously developed AJDSP (for Android devices) and iJDSP (for iPhones and iPads) apps to facilitate this process. Additionally, modules have been developed that have been embedded in math classes. Anticipated benefits of the project include creating positive attitudes towards STEM areas that will help recruit high school students and minorities in engineering, math and science fields. After an initial pilot study and assessments at CDS High School, these activities will be disseminated to other high schools. In order to obtain feedback from high school students and teachers, we will hold workshops and collect assessment results. These results will also provide us assessments about the effectiveness of the project, and allow us to make modifications to the project as necessary. The project is part of TUES Phase 3 and I/UCRC RET activities.

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Experiments With Sensor Motes and Java-DSP

Cited by 18 publications

References 12 publications

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Real-Time Prototyping of Matlab-Java Code Integration for Water Sensor Networks Applications

Embedding Android signal processing apps in a high school math class — An RET project

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Experiments With Sensor Motes and Java-DSP

Cited by 18 publications

References 12 publications

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Machine Learning Workforce Development Programs on Health and COVID-19 Research

Real-Time Prototyping of Matlab-Java Code Integration for Water Sensor Networks Applications

Embedding Android signal processing apps in a high school math class &#x2014; An RET project

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Embedding Android signal processing apps in a high school math class — An RET project