3-D Electrical Impedance Tomography Reconstruction Using ℓ1 Norms Regularization

An ℓ1 norm reconstruction algorithm which has the merits of reducing the sensitivity to data outliers and avoiding edge blurring is applied in this paper to solve a 3- D EIT problem. The iterative imaging method allows flexible choice of norms by simply choosing different norm value. A cluster analysis is implemented for labelling targets using the morphology technique

Cancer Identification during Breast Surgery Using Electrical Impedance Spectroscopy Analysis

In this paper, electrical impedance spectroscopy (EIS) analysis was employed to evaluate the dielectric property of breast tissues. The complex impedance was recorded by a bio-impedance meter in the frequency range of 3 kHz to 1000 kHz. Non-linear Least squares regression was implemented to fit the measured data into Cole-Cole model. It was observed that significant differences existed between breast cancer and benign tissues

numpywren: serverless linear algebra

Linear algebra operations are widely used in scientific computing and machine learning applications. However, it is challenging for scientists and data analysts to run linear algebra at scales beyond a single machine. Traditional approaches either require access to supercomputing clusters, or impose configuration and cluster management challenges. In this paper we show how the disaggregation of storage and compute resources in so-called "serverless" environments, combined with compute-intensive workload characteristics, can be exploited to achieve elastic scalability and ease of management. We present numpywren, a system for linear algebra built on a serverless architecture. We also introduce LAmbdaPACK, a domain-specific language designed to implement highly parallel linear algebra algorithms in a serverless setting. We show that, for certain linear algebra algorithms such as matrix multiply, singular value decomposition, and Cholesky decomposition, numpywren's performance (completion time) is within 33% of ScaLAPACK, and its compute efficiency (total CPU-hours) is up to 240% better due to elasticity, while providing an easier to use interface and better fault tolerance. At the same time, we show that the inability of serverless runtimes to exploit locality across the cores in a machine fundamentally limits their network efficiency, which limits performance on other algorithms such as QR factorization. This highlights how cloud providers could better support these types of computations through small changes in their infrastructure

Towards Practical Serverless Analytics

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. Even though many distributed computation frameworks have moved into the cloud, many users are still left to struggle with complex cluster management and configuration tools there.In this thesis, we argue that cloud stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. We first build a prototype system, PyWren, which runs on existing serverless function services, and show that this model is general enough to implement a number of distributed computing models, such as BSP. We then identify two main challenges to support truly practical and general analytics on a serverless platform. The first challenge is to facilitate communication-intensive operations, such as shuffle in the serverless setting. The second challenge is to provide an elastic cloud memory. In this thesis, we made progress on both challenges. For the first, we develop a system called Locus, that can automate shuffle operations by judiciously provisioning hybrid intermediate storage. For the second, we present an algorithm, FairRide, that achieves near-optimal memory cache efficiency in a multi-tenant setting

Towards Practical Serverless Analytics

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. Even though many distributed computation frameworks have moved into the cloud, many users are still left to struggle with complex cluster management and configuration tools there.In this thesis, we argue that cloud stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. We first build a prototype system, PyWren, which runs on existing serverless function services, and show that this model is general enough to implement a number of distributed computing models, such as BSP. We then identify two main challenges to support truly practical and general analytics on a serverless platform. The first challenge is to facilitate communication-intensive operations, such as shuffle in the serverless setting. The second challenge is to provide an elastic cloud memory. In this thesis, we made progress on both challenges. For the first, we develop a system called Locus, that can automate shuffle operations by judiciously provisioning hybrid intermediate storage. For the second, we present an algorithm, FairRide, that achieves near-optimal memory cache efficiency in a multi-tenant setting

Whole-home gesture recognition using wireless signals

Abstract – This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two-bedroom apart-ment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%

Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification

Purpose: This study aimed to assess the value of radiomic features derived from the myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) detection and hypertrophic cardiomyopathy (HCM) versus hypertensive heart disease (HHD) differentiation. Methods: There were 345 subjects who underwent cardiovascular magnetic resonance (CMR) examinations that were analyzed. After quality control and manual segmentation, the 3D radiomic features were extracted from the MYO and PM. The data were randomly split into training (70%) and testing (30%) datasets. Feature selection was performed on the training dataset. Five machine learning models were evaluated using the MYO, PM, and MYO+PM features in the detection and differentiation tasks. The optimal differentiation model was further evaluated using CMR parameters and combined features. Results: Six features were selected for the MYO, PM, and MYO+PM groups. The support vector machine models performed best in both the detection and differentiation tasks. For LVH detection, the highest area under the curve (AUC) was 0.966 in the MYO group. For HCM vs. HHD differentiation, the best AUC was 0.935 in the MYO+PM group. Comparing the radiomics models to the CMR parameter models for the differentiation tasks, the radiomics models achieved significantly improved the performance (p = 0.002). Conclusions: The radiomics model with the MYO+PM features showed similar performance to the models developed from the MYO features in the detection task, but outperformed the models developed from the MYO or PM features in the differentiation task. In addition, the radiomic models performed better than the CMR parameters’ models