Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Obesity and the risk of heart failure

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.

Body Area Networks: A Survey

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

Improved Knowledge Distillation via Teacher Assistant

/pdf/improved-knowledge-distillation-via-teacher-assistant-324eqfmfzx.pdf

Despite the fact that deep neural networks are powerful models and achieve
appealing results on many tasks, they are too gigantic to be deployed on edge
devices like smart-phones or embedded sensor nodes. There has been efforts to
compress these networks, and a popular method is knowledge distillation, where
a large (a.k.a. teacher) pre-trained network is used to train a smaller (a.k.a.
student) network. However, in this paper, we show that the student network
performance degrades when the gap between student and teacher is large. Given a
fixed student network, one cannot employ an arbitrarily large teacher, or in
other words, a teacher can effectively transfer its knowledge to students up to
a certain size, not smaller. To alleviate this shortcoming, we introduce
multi-step knowledge distillation which employs an intermediate-sized network
(a.k.a. teacher assistant) to bridge the gap between the student and the
teacher. We study the effect of teacher assistant size and extend the framework
to multi-step distillation. Moreover, empirical and theoretical analysis are
conducted to analyze the teacher assistant knowledge distillation framework.
Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and
ResNet architectures substantiate the effectiveness of our proposed approach.

Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher

The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the tibialis anterior and gastrocnemius muscles, which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.

Hassan Ghasemzadeh

Papers

Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Improved Knowledge Distillation via Teacher Assistant

Improved Knowledge Distillation via Teacher Assistant

Improved Knowledge Distillation via Teacher Assistant: Bridging the Gap Between Student and Teacher

A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities