Courses from NESL

Department Number Title Description Url Crosslistings
ECE M202A Embedded Systems Designed for graduate computer science and electrical engineering students. Methodologies and technologies for design of embedded systems. Topics include hardware and software platforms for embedded systems, techniques for modeling and specification of system behavior, software organization, real-time operating system scheduling, real-time communication and packet scheduling, low-power battery and energy-aware system design, timing synchronization, fault tolerance and debugging, and techniques for hardware and software architecture optimization. Theoretical foundations as well as practical design methods. CS M213A Show
ECE M202B Energy-Aware Computing and Cyber-Physical Systems System-level management and cross-layer methods for power and energy consumption in computing and communication at various scales ranging across embedded, mobile, personal, enterprise, and data-center scale. Computing, networking, sensing and control technologies and algorithms for improving energy sustainability in human-cyber-physical systems. Topics include: modeling of energy consumption, energy sources, and energy storage; dynamic power management; power-performance scaling and energy proportionality; duty-cycling; power- aware scheduling; low-power protocols; battery modeling and management; thermal management; sensing of power consumption. CS M213B Show
ECE 209AS Special Topics in Circuits and Embedded Systems: Security and Privacy for Embedded Systems, Cyber-Physical Systems, and Internet of Things The deep and pervasive embedding of computational intelligence into society has led to the emergence of computing systems that tightly couple software with the physical and human world. Driving exciting applications such as autonomous vehicles, smarthomes, and mobile health, these computing systems are variously referred to as Embedded Systems, Cyber-Physical Systems, Internet-of-Things etc. With their tight and often real-time interaction with physical spaces and human agents, these computing systems which are resource constrained, networked and remote, suffer from a much more expanded set of security and privacy vulnerabilities and consequences than traditional computers. Examples include compromise of system state via adversarial manipulation of physical sensor signals and actuator actions, and unauthorized inferencing of private inference by authorized recipients of from high-dimensional sensor data. The course will examine attacks that such systems are vulnerable to, and emerging solution techniques spanning algorithms, software, and hardware. Show
ECE 209AS Special Topics in Circuits and Embedded Systems: Artificial Intelligence and Machine Learning for IoT and HCPS Focus on emerging artificial intelligence and machine learning (ML) methods for sensing and control in Internet of Things and human-cyber-physical systems. Such systems--found in many application domains such as mobile health, smart-built environments, intelligent transportation, etc.--are characterized by multimodal time-series data, low-latency requirements, resource constraints, complex spatiotemporal dynamics, and computing distributed across edge-cloud. Through lectures, paper presentations, and application-inspired project, introduction to topics such as deep-learning methods for processing time-series and multimodal data; architectures that combine data-driven and mechanistic models; sim2real challenges in deep-reinforcement-learning-based control of physical systems; adaptation to dynamics; and systems issues, such as implementations in resource-constrained and distributed settings. Designed for students familiar with basic ML methods including deep neural networks, one ML software platforms, and software development. Show