Traffic scene recognition based on deep CNN and VLAD spatial pyramids

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

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EczemaNet: A Deep CNN-based Eczema Diseases Classification

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS) ◽

10.1109/ipas50080.2020.9334929 ◽

2020 ◽

Author(s):

Masum Shah Junayed ◽

Abu Noman Md Sakib ◽

Nipa Anjum ◽

Md Baharul Islam ◽

Afsana Ahsan Jeny

Keyword(s):

Deep Cnn

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Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

Scientific Reports ◽

10.1038/s41598-021-92997-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Da Un Jeong ◽

Ki Moo Lim

Keyword(s):

Blood Pressure ◽

Blood Pressure Measurement ◽

Patient Specific ◽

Peak Time ◽

Simultaneous Prediction ◽

Deep Cnn ◽

Pressure Estimation ◽

The Difference ◽

Blood Pressure Estimation ◽

Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

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Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104536 ◽

2021 ◽

pp. 104536

Author(s):

Behrouz Rostami ◽

D.M. Anisuzzaman ◽

Chuanbo Wang ◽

Sandeep Gopalakrishnan ◽

Jeffrey Niezgoda ◽

...

Keyword(s):

Image Classification ◽

Deep Cnn

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