Hyperspectral microscopy grants the ability to characterize unique properties of tissues based on their spectral fingerprint. The ability to label and measure multiple molecular probes simultaneously ...
We aim to develop a fast and accurate hyperspectral microscopy system that can be easily integrated with existing microscopes and provide flexibility for optimizing measurement time versus spectral re...
The method employs compressive sensing (CS) and a spectrally encoded illumination device integrated into the illumination path of a standard microscope. The spectral encoding is obtained using a compa...
We demonstrated the acquisition of breast cancer biopsies hyperspectral data of the whole camera area within...
CS hyperspectral microscopy was successfully demonstrated on a common lab microscope for measuring biopsies stained with the most common stains, such as hematoxylin and eosin. The high spectral resolu...
It is hard to directly deploy deep learning models on today's smartphones due to the substantial computational costs introduced by millions of parameters. To compress the model, we develop an ℓ0-based...
Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small sate...
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model...
Develop a signal quality index (SQI) to determine the quality of compressively sensed electrocardiogram (ECG) by estimating the signal-to-noise ratio (SNR)....
The SQI used random forests, with the ratio of the standard deviations of an ECG segment and a clean ECG and the Wasserstein metric between the amplitude distributions of an ECG segment and a clean EC...
The average RMSE was 3.18 dB and 3.47 dB in normal and abnormal ECG. The average Spearman correlation was 0.94 and 0.93 in normal and abnormal ECG, respectively. The average accuracy and F1 score were...
The SQI determined the quality of compressively sensed ECG and generalized across different databases. There was no consequential effect on the SQI due to abnormal ECG or compression using different s...
Without reconstruction, the SQI can inform which ECG should be analyzed to reduce false alarms due to contamination....
Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However...
With the development of 3D sensors technology, 3D point cloud is widely used in industrial scenes due to their high accuracy, which promotes the development of point cloud compression technology. Lear...
Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently ...
We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints...
While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this work has focused on ima...
