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2,370 papers
The obtained results provide a holistic assessment of the state and structure of the research field on UAV and IoT applications in agriculture and can serve as a basis for shaping further research programs and educational courses in precision agriculture.
This article explores infrastructural intelligence as an emerging pathway for advancing Green Business and sustainable development by transforming conventional infrastructure into dynamic, predictive, adaptive, and resource-efficient systems. Building on the convergence of Artificial Intelligence (AI), the Internet of...
A smart fruit spoilage detection system that combines advanced gas sensing with deep learning for real-time monitoring and automated decision-making and enabling low-latency, real-time inference in IoT environments is presented.
The findings confirm that the proposed edge-based BEMS framework is both computationally feasible and effective, offering practical guidance for selecting between weighted-sum and Pareto-based optimization strategies in real-time campus energy management.
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As multimedia services evolve from conventional image/video toward immersive and interactive experiences, the traditional network-centric definition of service quality is becoming increasingly insufficient [1]–[5]. Historical network management frameworks have traditionally relied on objective, network-layer performanc...
The construction industry continues to suffer from substantial productivity losses due to inefficient operations, wasteful use of materials, broken equipment and a lack of cohesive production management. Lean Construction is a good way to think about getting rid of waste and it remains difficult to implement because in...
Experimental validation of the facial emotion recognition component on benchmark data sets along with the real-time heart rate acquisition through the embedded PPG sensor proves the efficacy of the developed sensing modules to enable the use of the suggested IoT framework for stress monitoring purposes.
Experimental results demonstrate that the proposed FMADRL with digital-twin assistance significantly outperforms existing methods in energy savings, reduced delay, improved reliability, and robustness against security threats.
This work presents EdgeAgent, a cross-layer inference system explicitly co-designed for edge UMA and multi-agent workloads, and demonstrates that the UMA-aware execution alone contributes a 1.29x speedup over batched speculative decoding and adding the agent-aware scheduling lifts the full EdgeAgent system to a 1.77x s...
Future 6G systems share a central challenge with Physical AI: combining sensing, reasoning, and action on network-edge infrastructure in rapidly changing physical environments under strict latency, compute, and energy constraints. Addressing this challenge requires multi-timescale intelligence, combining fast perceptio...
Edge intelligence requires models to sense continuously in real time and to keep adapting on-device, all under tight compute, energy, and memory budgets. Although spiking neural networks (SNNs) enable efficient event-driven inference, standard surrogate-gradient backpropagation (BP) serializes updates and blocks ongoin...
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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