High-performance computing is a prime area for many applications. Majorly, weather and climate forecast applications use the HPC system because it needs to give a good result with low latency. In recent years machine ...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
High-performance computing is a prime area for many applications. Majorly, weather and climate forecast applications use the HPC system because it needs to give a good result with low latency. In recent years machine learning and deep learning models have been widely used to forecast the weather. However, to the best of the author's knowledge, many applications do not effectively utilise the HPC system for training, testing, validation, and inference of weather data. Our experiment is to conduct performance modeling and benchmark analysis of weather and climate forecast machine learning models and determine the characteristics between the application, model and the underlying HPC system. Our results will help the researchers improvise and optimise the weather forecast system and use the HPC system efficiently.
Wireless sensor network (WSN), is a collection of sensors that is used for monitoring the activities in a given environment. They are used in the forests for fire detection wherea large number of sensor nodes are plac...
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distributed and central control are two complementary paradigms to establish self-adaptation in software systems. Both approaches have their individual benefits and drawbacks, which lead to significant trade-offs rega...
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ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
distributed and central control are two complementary paradigms to establish self-adaptation in software systems. Both approaches have their individual benefits and drawbacks, which lead to significant trade-offs regarding certain software qualities when designing such systems. The significance of these trade-offs even increases the more complex the target system becomes. In this paper, we present our work-in-progress towards an integrated control approach, which aims at providing the best of both control paradigms. We present the basic concepts of this multi-paradigm approach and outline its inherent support for complex system hierarchies. Further, we illustrate the vision of our approach using application scenarios from the smart energy grid as an example for self-adaptive systems of systems.
At present, there is a notable focus on Parallel and distributedcomputing (PDC) initiatives within the realm of undergraduate engineering education in India. Owing to differences in education systems across borders, ...
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ISBN:
(纸本)9798350383782;9798350383799
At present, there is a notable focus on Parallel and distributedcomputing (PDC) initiatives within the realm of undergraduate engineering education in India. Owing to differences in education systems across borders, along with variations in university policies, these efforts must be curated to cater to specific stakeholders, ensuring the achievement of the desired outcomes. Understanding such scenarios is crucial for the landscape of Indian undergraduate PDC education. This paper unveils a success story of implementing PDC at the undergraduate level for the past decade and a half, offering valuable insights gathered along this extended journey. Reflecting the idea that "every master was once a beginner," the narrative unfolds to inspire and empower educators who are just starting out. Whether introducing or already incorporating PDC education into the curriculum, this account is crafted to uplift and guide. Amidst the ongoing initiatives across the country, the time has come to progress and elevate PDC education beyond its current status. This paper presents a summary of potential efforts that the PDC community in India can explore for such initiatives.
Neuromorphic sensors using piezoelectric MEMS resonators developed by adapting physical reservoir computing (PRC) algorithm. The device has two resonators with a resonant frequency of about 1 kHz, which is in the audi...
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The advancement of wireless communication and networking technologies has simplified the design of IoT systems. Wireless sensor networks are made up of groupings of sensors at remote locations that cause problems when...
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ISBN:
(纸本)9798350333398
The advancement of wireless communication and networking technologies has simplified the design of IoT systems. Wireless sensor networks are made up of groupings of sensors at remote locations that cause problems when their batteries die. Energy-efficient pathways must be devised to extend the networks lifetime. The Self Adapting Low Energy Adaptive Clustering Hierarchy (SA-LEACH) algorithm is offered as a solution, which employs clustering to balance network load and reduce energy usage. This algorithm takes into account parameters such as energy levels and the distance between sensors and a base station. When compared to current methods, the simulation results reveal that SA-LEACH enhances system dependability, longevity, and load balancing.
This paper presents a high-resolution capacitive stress sensor array to precisely characterize the distributed MEMS packaging stress, for the first time. The unit stress measurement cell utilizes a bridge-type mechani...
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ISBN:
(纸本)9781665493086
This paper presents a high-resolution capacitive stress sensor array to precisely characterize the distributed MEMS packaging stress, for the first time. The unit stress measurement cell utilizes a bridge-type mechanical amplifier that converts the substrate strain into capacitance variations. We have measured and compared the MEMS die stress over temperature for different die attaches with a custom designed PCB housing an on-chip heater. The proposed approach significantly simplifies the evaluation and selection of packaging materials. Comparing the temperature responses of a soft silicone-based and stiffer silver-filled epoxy reveals difficult to predict results.
The proceedings contain 21 papers. The topics discussed include: enhancing the performance of photonic crystal and gates with machine learning optimization;bitcoin price prediction based on financial data, technical i...
ISBN:
(纸本)9798350381580
The proceedings contain 21 papers. The topics discussed include: enhancing the performance of photonic crystal and gates with machine learning optimization;bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms;promoting cybersecurity knowledge via gamification: an innovative intervention design;modeling and verification of the causal broadcast algorithm using colored Petri Nets;GPU-based parallel technique for solving n-similarity problem in textual data mining;a novel approach for specification and verification of symmetric distributed algorithms using spin;optimizing geophysical workloads in high-performance computing: leveraging machine learning and transformer models for enhanced parallelism and processor allocation;and rational Jacobi kernel functions: a novel massively parallelizable orthogonal kernel for support vector machines.
The emergence of new storage like persistent memory (PM) and zoned namespaces SSDs (ZNS-SSDs) introduces new challenges and opportunities for distributed key-value stores. Since LSM-tree has been widely adopted in dis...
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ISBN:
(数字)9781665471770
ISBN:
(纸本)9781665471770
The emergence of new storage like persistent memory (PM) and zoned namespaces SSDs (ZNS-SSDs) introduces new challenges and opportunities for distributed key-value stores. Since LSM-tree has been widely adopted in distributed key-value stores, such as RocksDB and HBase, it is necessary to revisit the LSM-tree to make it adapt to new storage. In this paper, we first analyze the challenges of adapting the LSM-tree for new storage. Then, we propose a high-level architecture for a new-storage-aware LSM-tree-based key-value store called Hybrid-LSM. We explain the key structural issues of different storage layers in Hybrid-LSM and present some preliminary design ideas.
This paper presents a vision-based approach for detecting speed bumps, which is crucial for enabling safe and efficient speed control in autonomous vehicles. Given the diverse range of speed bump sizes and characteris...
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ISBN:
(纸本)9798350339024
This paper presents a vision-based approach for detecting speed bumps, which is crucial for enabling safe and efficient speed control in autonomous vehicles. Given the diverse range of speed bump sizes and characteristics encountered in Indian scenarios, a robust detection algorithm is required. To this end, we evaluate two state-of-the-art deep learning-based object detection models, Faster R-CNN and YOLOv5, and compare their performance. Our study specifically focuses on detecting both marked and unmarked speed bumps in real-world environments. However, we also address the challenge of misclassifying pedestrian crosswalks, which can be mistaken for speed bumps due to their similar features. To enhance the accuracy of detecting marked speed bumps, we employ the Negative Sample Training (NST) method. The results show that training with NST improved the detection performance of both Faster R-CNN and YOLOv5 models, achieving an average precision increase of 5.58% and 2.3%, respectively, for marked speed bump detection. Furthermore, we conduct real-time testing of the proposed model on the NVIDIA Jetson platform, which yields an inference speed of 18.5ms per frame.
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