This paper uses the reconstruction-computation-quantization (RCQ)paradigm to decode low-density parity-check (LDPC) codes. RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision. For message-passing according to a flooding schedule, the RCQ parameters are designed by discrete density evolution. Simulation results on an IEEE 802.11 LDPC code show that for 4-bit messages, a flooding Min Sum RCQ decoder outperforms table-lookup approaches such as information bottleneck (IB) or Min-IB decoding, with significantly fewer parameters to be stored. Additionally, this paper introduces layer-specific RCQ, an extension of RCQ decoding for layered architectures. Layer-specific RCQ uses layer-specific message representations to achieve the best possible FER performance. For layer-specific RCQ, this paper proposes using layered discrete density evolution featuring hierarchical dynamic quantization (HDQ) to design parameters efficiently. Finally, this paper studies field-programmable gate array (FPGA) implementations of RCQ decoders. Simulation results for a (9472, 8192) quasi-cyclic (QC) LDPC code show that a layered Min Sum RCQ decoder with 3-bit messages achieves more than a 10% reduction in LUTs and routed nets and more than a 6% decrease in register usage while maintaining comparable decoding performance, compared to a 5-bit offset Min Sum decoder.

Non-uniform message quantization techniques such as reconstruction-computation-quantization (RCQ) improve error-correction performance and decrease hardware complexity of low-density parity-check (LDPC) decoders that use a flooding schedule. Layered MinSum RCQ (L-msRCQ) enables message quantization to be utilized for layered decoders and irregular LDPC codes. We investigate field-programmable gate array (FPGA) implementations of L-msRCQ decoders. Three design methods for message quantization are presented, which we name the Lookup, Broadcast, and Dribble methods. The decoding performance and hardware complexity of these schemes are compared to a layered offset MinSum (OMS) decoder. Simulation results on a (16384, 8192) protograph-based raptor-like (PBRL) LDPC code show that a 4-bit L-msRCQ decoder using the Broadcast method can achieve a 0.03 dB improvement in error-correction performance while using 12% fewer registers than the OMS decoder. A Broadcast-based 3-bit L-msRCQ decoder uses 15% fewer lookup tables, 18% fewer registers, and 13% fewer routed nets than the OMS decoder, but results in a 0.09 dB loss in performance.

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand assistance to wheelchair users. PAPAWs operate based on a collaborative control scheme. Therefore, they rely on accurate interpretation of the user's intent to provide effective propulsion assistance. This paper presents a learning-based approach to predict wheelchair users' intention when performing a variety of wheelchair activities. We obtained kinematic and kinetic data from manual wheelchair users when performing standard wheelchair activities such as turns and ascents. Our measurements revealed variability in physical capabilities and propulsion habits of different users, therefore, highlighting the need for the development of personalized intention inference models. We used Gaussian Mixture models to label different phases of user-pushrim interactions based on individual user's wheeling behaviour. Supervised classifiers were trained with each user's data and these models were used to predict the user's intentions during different propulsion activities. We found random forest classifiers had high accuracy (>92%) in predicting different states of individual-specific wheelchair propulsion and user intent for 2 participants. This proposed framework is computationally efficient and can be used for real-time prediction of wheelchair users' intention. The outcome of this clustering-classification pipeline provides relevant information for designing user-specific and adaptive PAPAW controllers.

Human motion capture using video-based or sensor-based methods gives animators the capability to directly translate complex human motions to create lifelike character animations. Advances in motion capture algorithms have improved their accuracy for estimating human generalized motion coordinates (joint angles and body positions). However, the traditional motion capture pipeline is not well suited to measure short duration, high acceleration impacts, such as running and jumping footstrikes. While high acceleration impacts have minimal influence on generalized coordinates, they play a big role in exciting soft tissue dynamics. Here we present a method for correcting motion capture trajectories using a sparse set of inertial measurement units (IMUs) collecting at high sampling rates to produce more accurate impact accelerations without sacrificing accuracy of the generalized coordinates representing gross motions. We demonstrate the efficacy of our method by correcting human motion captured experimentally using commercial motion capture systems with high rate IMUs sampling at 400Hz during basketball jump shots and running. With our method, we automatically corrected 185 jumping impacts and 1266 running impacts from 5 subjects. Post correction, we found an average increase of 84.6% and 91.1% in pelvis vertical acceleration and ankle dorsiflexion velocity respectively for basketball jump shots, and an average increase of 110% and 237% in pelvis vertical acceleration and ankle plantarflexion velocity respectively for running. In both activities, pelvis vertical position and ankle angle had small corrections on average below 2.0cm and 0.20rad respectively. Finally, when driving a human rig with soft tissue dynamics using corrected motions, we found a 143.4% and 11.2% increase in soft tissue oscillation amplitudes in basketball jump shots and running respectively. Our methodology can be generalized to correct impact accelerations for other body segments, and provide new tools to create realistic soft tissue animations during dynamic activities for more lifelike characters and better motion reconstruction for biomechanical analyses.

This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.