Zobrazeno 1 - 10
of 664
pro vyhledávání: '"GUPTA, HARI"'
Autor:
Gupta, Hari Prabhat
The deployment of LoRaWAN (Long Range Wide Area Network) in dynamic environments, such as smart campuses, presents significant challenges in optimizing network parameters like spreading factor (SF), transmission power (TxPower), and managing mobility
Externí odkaz:
http://arxiv.org/abs/2410.09927
Autor:
Gupta, Hari Prabhat, Mishra, Rahul
Forest fires pose a significant threat to the environment, human life, and property. Early detection and response are crucial to mitigating the impact of these disasters. However, traditional forest fire detection methods are often hindered by our re
Externí odkaz:
http://arxiv.org/abs/2410.06743
Autor:
Sahu, Himanshu, Gupta, Hari Prabhat
Recent advancements in quantum computing, alongside successful deployments of quantum communication, hold promises for revolutionizing mobile networks. While Quantum Machine Learning (QML) presents opportunities, it contends with challenges like nois
Externí odkaz:
http://arxiv.org/abs/2406.14236
Autor:
Gupta, Hari Prabhat, Mishra, Rahul
This paper presents an end-to-end methodology for collecting datasets to recognize handwritten English alphabets by utilizing Inertial Measurement Units (IMUs) and leveraging the diversity present in the Indian writing style. The IMUs are utilized to
Externí odkaz:
http://arxiv.org/abs/2307.02480
Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy and minimizing communication overhead. The heterogeneity of devices and networking resources of the pa
Externí odkaz:
http://arxiv.org/abs/2306.04207
Autor:
Sahu, Himanshu, Gupta, Hari Prabhat
Quantum computing has the potential to provide exponential performance benefits in processing over classical computing. It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem.
Externí odkaz:
http://arxiv.org/abs/2302.08884
Autor:
Mishra, Rahul, Gupta, Hari Prabhat
Automated feature extraction capability and significant performance of Deep Neural Networks (DNN) make them suitable for Internet of Things (IoT) applications. However, deploying DNN on edge devices becomes prohibitive due to the colossal computation
Externí odkaz:
http://arxiv.org/abs/2209.15560
Smart sensing provides an easier and convenient data-driven mechanism for monitoring and control in the built environment. Data generated in the built environment are privacy sensitive and limited. Federated learning is an emerging paradigm that prov
Externí odkaz:
http://arxiv.org/abs/2209.01417
With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially. As household appliances are the pri
Externí odkaz:
http://arxiv.org/abs/2209.01338
Deep Neural Network (DNN) has gained unprecedented performance due to its automated feature extraction capability. This high order performance leads to significant incorporation of DNN models in different Internet of Things (IoT) applications in the
Externí odkaz:
http://arxiv.org/abs/2010.03954