Ultra Low power DSP for Heath care Monitoring
Tinoosh Mohsenin, CSEE
Sri Harsha Konuru, CSEE
In this research, we explore a variety of ultra low-power DSP techniques for wearable biomedical devices. A blend of feature engineering and machine learning algorithms are employed and evaluated within the context of real-time classification applications. The evaluation is based on two major criteria: 1. classification accuracy and 2. algorithmic complexity (computation, memory, latency). Currently, two case studies are being explored. The first case study is the detection of seizures for epileptic patients using multi-physiological signals in an ambulatory setting. The second case study is an assistive technology that enables a user to interactive with their surroundings using a tongue-driven interface.
Accelerating Convolutional Neural Network
Tinoosh Mohsenin, CSEE
Tahmid Abtahi, CSEE
We explore the use of deep neural networks (DNN) for embedded big data applications. Deep neural networks have been demonstrated to outperform state-of-the-art solutions for a variety of complex classification tasks, such as image recognition. The ability to train networks to both perform feature abstraction and classification provides a number of key benefits. One key benefit is that it reduces the burden of the developer to produce efficient, optimal feature engineering, which typically requires expert domain-knowledge and significant time. A second key benefit is that the network’s complexity can be adjusted to achieve desired accuracy performance. Despite these benefits, DNNs have yet to be fully realized in an embedded setting. In this research, we explore novel architecture optimizations and develop optimal static mappings for neural networks onto highly parallel, highly granular hardware processors such as many-cores and embedded GPUs.
Algorithm Characterization and Implementation for Large Volume, High Resolution Multichannel Electroencephalography Data in Seizure Detection
Tinoosh Mohsenin, Computer Science and Electrical Engineering
Adam Page, Computer Science and Electrical Engineering
Amey Kulkarni, Computer Science and Electrical Engineering
Tim Oates, Computer Science and Electrical Engineering
Sid Pramod, Computer Science and Electrical Engineering
Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming
flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in large volue of high resolution, multi-channel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions. We also present the implementation of these algorithms on different hardware approaches including Virtex-7 FPGA, GPUs and 65 nm-CMOS ASIC.