scholarly journals Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1247 ◽  
Author(s):  
Bobby Pappachan ◽  
Wahyu Caesarendra ◽  
Tegoeh Tjahjowidodo ◽  
Tomi Wijaya
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Robot Assisted ◽  
Event Classification

scholarly journals Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4138 ◽  
Author(s):  
Mikail Yayla ◽  
Anas Toma ◽  
Kuan-Hsun Chen ◽  
Jan Eric Lenssen ◽  
Victoria Shpacovitch ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
Domain Analysis ◽  
Haar Wavelet ◽  
Frequency Domain Analysis ◽  
Classification Performance ◽  
Mobile Systems ◽  
Resource Limited ◽  
Computationally Intensive ◽  
Classification Of Images

A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μ s per image for the Fourier features and 17 μ s for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

Real-time optical biopsy and analysis of breast cancer using clinical optical coherence tomography

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 10508-10508
Author(s):  
S. A. Boppart ◽  
A. M. Zysk ◽  
F. T. Nguyen ◽  
E. J. Chaney ◽  
F. J. Bellafiore ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Frequency Domain ◽  
Real Time ◽  
Breast Tissue ◽  
Tumor Stroma ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Optical Biopsy ◽  
Surgical Biopsy ◽  
Scan Data

10508 Background: Advances in high-resolution, real-time, optical imaging have enabled optical coherence tomography (OCT) for non-excisional optical biopsies of breast tissue. OCT is the optical analogue to ultrasound imaging, with resolution approaching that of histology. In breast tissue, regions of tumor, tumor margins, abnormal ducts, and foci of tumor cells can be identified based on increased scattering and morphological appearance. We have developed a portable clinical OCT system along with image criteria for identifying breast cancer in real-time during needle-biopsy and surgical-biopsy procedures. Methods: Over 4,000 depth-resolved OCT axial scans were extracted from over 50 cross-sectional OCT images from 9 ex vivo specimens of ductal carcinoma in situ (DCIS) of the human breast. OCT image data was acquired in real-time within 6 hours of surgical resection using a state-of-the-art OCT system with micron resolution. Imaged sites were marked for registration with hematoxylin and eosin-stained histology. Axial scan data was analyzed using frequency-domain analysis techniques to classify regions as tumor, stroma, or adipose tissue. OCT images of tissue types were compared with histological observations. Results: Real-time OCT images of microscopic breast cancer morphology showed strong correlations with corresponding histology, including regions of tumor, tumor margins, ducts, and adipose tissue. Computational frequency-domain analysis of depth-resolved axial scan data classified tissue types as tumor, stroma, and adipose with 91% sensitivity and 87% specificity. Conclusions: Optical biopsy of breast cancer with OCT is feasible for imaging at resolutions approaching histology. Frequency- domain analysis of axial scan data can be used to classify breast tissue types with a sensitivity and specificity that are comparable to or surpasses that of conventional mammography and ultrasound imaging of human DCIS. Clinical OCT imaging has the potential for real-time guidance and diagnostics in needle- or surgical-biopsy of breast cancer. No significant financial relationships to disclose.

Embedded real-time systems in cyber-physical applications: a frequency domain analysis methodology

2020 ◽  
Vol 49 (2) ◽  
pp. 201-221
Author(s):  
Claudio Aciti ◽  
Ricardo Cayssials ◽  
Edgardo Ferro ◽  
José Urriza ◽  
Javier Orozco
Keyword(s):  
Frequency Domain ◽  
Real Time ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Real Time Systems ◽  
Analysis Methodology ◽  
Time Systems
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