On-chip cross-talk analysis for multiple RF front ends of a wireless gigabit LAN system

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 169-173).In the Wireless-Gigabit-Local-Area-Network (WiGLAN) project, we proposes a system architecture that adopts multiple antennas [1, 2, 3, 4] to control the trade-off between data rate and transmission quality [5, 6] through Space-Time Coding (STC) [7, 8, 9] and Orthogonal Frequency Division Multiplexing (OFDM). However, along the multiple RF front-ends, there are multiple nodes that signal cross-talk can occur. Such signal cross-talk occurring on a silicon chip becomes more and more significant as the integration level and operating radio frequency rise, seriously degrading the system performance, the data rate and transmission quality. Most of the literature about on-chip crosstalk suppression have been focusing on adopting various process-technology techniques, such as using guard ring structures to separate the parallel RF front ends or inserting a ground plane to shield the cross-talk. In this study, we will take a different approach. We will investigate the effects of on-chip cross-talk upon the operations of the coding and modulation schemes adopted in the WiGLAN system and explore methods, other than those mentioned, to counteract them.by Jie De Jacky Liang.S.M

Code as Policies: Language Model Programs for Embodied Control

Large language models (LLMs) trained on code completion have been shown to be capable of synthesizing simple Python programs from docstrings [1]. We find that these code-writing LLMs can be re-purposed to write robot policy code, given natural language commands. Specifically, policy code can express functions or feedback loops that process perception outputs (e.g.,from object detectors [2], [3]) and parameterize control primitive APIs. When provided as input several example language commands (formatted as comments) followed by corresponding policy code (via few-shot prompting), LLMs can take in new commands and autonomously re-compose API calls to generate new policy code respectively. By chaining classic logic structures and referencing third-party libraries (e.g., NumPy, Shapely) to perform arithmetic, LLMs used in this way can write robot policies that (i) exhibit spatial-geometric reasoning, (ii) generalize to new instructions, and (iii) prescribe precise values (e.g., velocities) to ambiguous descriptions ("faster") depending on context (i.e., behavioral commonsense). This paper presents code as policies: a robot-centric formalization of language model generated programs (LMPs) that can represent reactive policies (e.g., impedance controllers), as well as waypoint-based policies (vision-based pick and place, trajectory-based control), demonstrated across multiple real robot platforms. Central to our approach is prompting hierarchical code-gen (recursively defining undefined functions), which can write more complex code and also improves state-of-the-art to solve 39.8% of problems on the HumanEval [1] benchmark. Code and videos are available at https://code-as-policies.github.i