Power Efficient Data Compression Hardware for Wearable and Wireless Biomedical Sensing Devices

This thesis aims to verify a possible benefit lossless data compression and reduction techniques can bring to a wearable and wireless biomedical device, which is anticipated to be system power saving. A wireless transceiver is one of the main contributors to the system power of a wireless biomedical sensing device, and reducing the data transmitted by the transceiver with a minimum hardware cost can therefore help to save the power. This thesis is going to investigate the impact of the data compression and reduction on the system power of a wearable and wireless biomedical device and trying to find a proper compression technique that can achieve power saving of the device. The thesis first examines some widely used lossy and lossless data compression and reduction techniques for biomedical data, especially EEG data. Then it introduces a novel lossless biomedical data compression technique designed for this research called Log2 sub-band encoding. The thesis then moves on to the biomedical data compression evaluation of the Log2 sub-band encoding and an existing 2-stage technique consisting of the DPCM and the Huffman encoding. The next part of this thesis explores the signal classification potential of the Log2 sub-band encoding. It was found that some of the signal features extracted as a by-product during the Log2 sub-band encoding process could be used to detect certain signal events like epileptic seizures, with a proper method. The final section of the thesis focuses on the power analysis of the hardware implementation of two compression techniques referred to earlier, as well as the system power analysis. The results show that the Log2 sub-band is comparable and even superior to the 2-stage technique in terms of data compression and power performance. The system power requirement of an EEG signal recorder that has the Log2 sub-band implemented is significantly reduced

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.Comment: Accepted by BIBM 2019 (Regular

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.Comment: 7 pages, 4 figure

A 65nm CMOS lossless bio-signal compression circuit with 250 femtoJoule performance per bit.

A 65nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel `xor-log2-sub-band' data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the `simplest useful compression algorithm', the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250fJ/bit at continuous conversion data-rates of 5MSa/sec. This is achieved with a diminutive circuit area of 155 um2. Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization

Adoption of blended learning: Chinese university students’ perspectives

Abstract Against the backdrop of the deep integration of the Internet with learning, blended learning offers the advantages of combining online and face-to-face learning to enrich the learning experience and improve knowledge management. Therefore, the objective of this present study is twofold: a. to fill a gap in the literature regarding the adoption of blended learning in the post-pandemic era and the roles of both the technology acceptance model (TAM) and the theory of planned behavior (TPB) in this context and b. to investigate the factors influencing behavioral intention to adopt blended learning. For that purpose, the research formulates six hypotheses, incorporates them into the proposed conceptual model, and validates them using model-fit indices. Based on data collected from Chinese university students, the predicted model’s reliability and validity are evaluated using structural equation modeling (SEM). The results of SEM show that (a) the integrated model based on the TAM and the TPB can explain 67.6% of the variance in Chinese university students’ adoption of blended learning; (b) perceived usefulness (PU), perceived ease of use (PEU), and subjective norms (SN) all have positive impacts on learning attitudes (LA); (c) PEU has a positive influence on PU, and SN has a positive influence on perceived behavioral control (PBC); and (d) both PU and LA have a positive influence on the intention to adopt blended learning (IABL). However, PEU, SN, and PBC have little effect on IABL; e. LA mediates the effect of PU on IABL, and PU mediates the effect of PEU on IABL. This study demonstrated that an integrated conceptual framework based on the TAM and the TPB as well as the characteristics of blended learning offers an effective way to understand Chinese university students’ adoption of blended learning

Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data

1967-2012 IEEE. Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods