Continuous crystallization as a downstream processing step of pharmaceutical proteins: A review

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

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HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

JOURNAL OF ADVANCES IN CHEMISTRY ◽

10.24297/jac.v13i9.5803 ◽

2017 ◽

Vol 13 (9) ◽

pp. 6480-6488 ◽

Cited By ~ 1

Author(s):

A.D. Jeyarani ◽

Reena Daphne ◽

Solomon Roach

Keyword(s):

Neural Network ◽

Signal Acquisition ◽

Heart Murmur ◽

Classification Techniques ◽

Classification Schemes ◽

Neural Network Classification ◽

Nonlinear Classification ◽

Processing Step ◽

System Characterization ◽

Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying systemâ€™s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

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Nifedipine Solid Dispersion Using AQOAT® HPMCAS: Preparation by HME and Downstream Processing

10.26226/morressier.57d6b2bad462b8028d88d5a7 ◽

2016 ◽

Author(s):

Andreas Sauer

Keyword(s):

Solid Dispersion ◽

Downstream Processing

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A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/3/3 ◽

2020 ◽

Vol 17 (3) ◽

pp. 299-305 ◽

Cited By ~ 1

Author(s):

Riaz Ahmad ◽

Saeeda Naz ◽

Muhammad Afzal ◽

Sheikh Rashid ◽

Marcus Liwicki ◽

...

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Data Augmentation ◽

Short Term Memory ◽

Recognition System ◽

Learning Approach ◽

Arabic Text ◽

Data Set ◽

Processing Step ◽

Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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