In recent years, AI has undergone significant changes. Firstly, there is a growing recognition of the need to deploy inference models based on Deep Neural Networks (DNNs) on edge devices. Secondly, there is an increasing demand for low-energy inferencing ...

The growing need for artificial intelligence in multitude of domains has over the past few years triggered some concerns related to latency and reliability when huge amounts of sensor data are moved from sensors to the cloud for computation. These ...

Vision-based hand gesture recognition in human-computer interface design has useful applications in virtual-reality, gaming control, communication through sign language, medical rehabilitation etc. In many scenarios, such applications are deployed on ...

We present a novel explanation of why organizations tend to lose their agility over time despite their efforts to foster worker initiative in adapting to local information. Worker initiative ensures efficiency but requires strong incentives. When ...

IoT-based automated systems require efficient online time-series analysis and forecasting and there is a growing requirement to enable such processing at the low-cost constrained edge devices. Classical approaches such as Online Autoregressive ...

Fire-related incidents continue to be reported as a leading cause of life and property destruction. Automated fire detection and localization (AFDL) systems have grown in importance with the evolution of applied robotics, especially because use of robots ...

Resource-constrained platforms such as micro-controllers are the workhorses in embedded systems, being deployed to capture data from sensors and send the collected data to cloud for processing. Recently, a great interest is seen in the research ...

Deep Neural Networks (DNN) have made machine learning accessible to a wide set of practitioners working with field deployment of analytics algorithms over sensor data. Along with it, focus on data privacy, low latency inference, and sustainability has ...

Online 3D bin packing is a challenging real-time combinatorial optimisation problem that involves packing of parcels (typically rigid cuboids) arriving on a conveyor into a larger bin for further shipment. Recent automation methods have introduced ...

Deep Learning architectures used in computer vision, natural language and speech processing, unsupervised clustering, etc. have become highly complex and application-specific in recent times. Despite existing automated feature engineering techniques, ...

Spiking neural network (SNN) are emerging as a bio-plausible AI paradigm best suited for energy constrained edge use case. However the performance of SNNs largely depends upon the information content of the spike trains generated from real valued data ...

With increased focus on in situ analytics, artificial intelligence (AI) algorithms are getting deployed on embedded devices at the network edge. Growing popularity of Deep Learning (DL) and inference largely due to minimization of feature engineering, ...

Running deep learning algorithms at the edge is a necessity in many industrial use-cases, especially in applications that use robots and drones in disaster recovery, surveillance, oil & gas operations etc. Current state of the art deep learning ...

Fog Computing has in recent times captured the imagination of industrial and research organizations working on various aspects of connected livelihood and governance of smart cities. Improvements in deep neural networks imply extensive use of such models ...

Industry 4.0 deployments involve machines, robots, Internet of Things, business processes and human participants coordinating in time constrained and safety critical environments. As these deployments make use of complex workflow patterns, accurate formal ...

Various machine learning models have so far been used for training robots to perform different tasks in the context of Industry 4.0. However, following the advances in neuroscience, new models are being pursued which are biologically inspired. One such ...

Robotic automation is being increasingly proselytized in the industrial and manufacturing sectors to increase production efficiency. Typically, complex industrial tasks cannot be satisfied by individual robots, rather coordination and information sharing ...

Of late, Cloud Robotics paradigm is being used to augment low-end robots with enhanced sensor data processing, storage and communication capabilities. In an era, where costly specialized hardware are being replaced by commodity hardware, software ...

Offloading computationally expensive Simultaneous Localization and Mapping (SLAM) task for mobile robots have attracted significant attention during the last few years. Lack of powerful on-board compute capability in these energy constrained mobile ...

Recent research on inexact computing shows promising results for improved energy utilization for resource hungry applications across different layers of the execution stack. The general philosophy of inexact computing is to trade-off correctness within ...