Feed frame: The last processing step before the tablet compaction in pharmaceutical manufacturing

2019 ◽  
Vol 572 ◽  
pp. 118728 ◽  
Author(s):  
Nobel O. Sierra-Vega ◽  
Rodolfo J. Romañach ◽  
Rafael Méndez
Keyword(s):  
Pharmaceutical Manufacturing ◽  
Processing Step ◽  
Tablet Compaction

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

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.

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

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
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 system’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.

Technology Transfer in Pharmaceutical Manufacturing - A Review

2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Amit B Patil ◽  
Bharath Kumar B ◽  
Ajay P Karnalli
Keyword(s):  
Technology Transfer ◽  
Drug Development ◽  
Research And Development ◽  
Product Quality ◽  
Pharmaceutical Product ◽  
Pharmaceutical Manufacturing ◽  
Shop Floor ◽  
R And D ◽  
Production Shop ◽  
Execution Procedures

Technology Transfer (TT) is vital action from drug development in Research and Development (R and D) Department to commercial manufacturing till the product discontinuation. This review is an attempt to give an insight about the transfer of pharmaceutical product from R and D to production including necessary documents required to review the supporting documents and execution procedures in production shop floor. TT is considered effective, if there is a documented evidence that the process and its parameters, repeatedly results in desired product quality which was established upon during TT between the transferee and transferor. For the execution of TT process, expertise from different department such as Engineering, R and D, QA, process analyst and production are teamed. the transmission comprises of arrangements procured in these flows of improvement to achieve the quality as planned throughout manufacture.

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

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
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.

FAKTOR DETERMINAN PEMBENTUK STRUKTUR MODAL PADA PERUSAHAAN SEKTOR FARMASI PERIODE 2012 - 2016

2017 ◽  
Vol 2 ◽  
pp. 241-250
Author(s):  
Neng Ria Kanita ◽  
Hendryadi Hendryadi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Firm Size ◽  
Stock Exchange ◽  
Pharmaceutical Manufacturing ◽  
Manufacturing Companies ◽  
Purposive Sampling ◽  
Panel Data Regression ◽  
Data Regression ◽  
Partial Effects

This study aims to examine the simultaneous and partial effects of profitability, liquidity, and firm size on capital structure. The sample is 10 pharmaceutical manufacturing companies listed in Indonesia Stock Exchange period 2012-2016, using purposive sampling. The technique of analysis used is panel data regression (pooled regression). The results showed that the selected model is the fixed effect. Simultaneously NPM, CR, and Firm Size have a significant effect on capital structure. Partially NPM has a negative and significant effect on capital structure. CR partially have a negative and not significant effect on capital structure. Partially Firm Size have a positive and significant effect on capital structure. Variables that have a significant effect on capital structure are NPM and Firm Size. While CR does not significantly affect the capital structure. Keywords: Capital Structure, Profitability, Liquidity, Firm Size

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals A fast $$(2 + \frac{2}{7})$$-approximation algorithm for capacitated cycle covering

2021 ◽  
Author(s):  
Vera Traub ◽  
Thorben Tröbst
Keyword(s):  
Vehicle Routing Problem ◽  
Covering Problem ◽  
Post Processing ◽  
Routing Problem ◽  
Cycle Cover ◽  
Disjoint Cycles ◽  
Processing Step ◽  
Number Of Cycles ◽  
Vertex Disjoint ◽  
Capacitated Vehicle

AbstractWe consider the capacitated cycle covering problem: given an undirected, complete graph G with metric edge lengths and demands on the vertices, we want to cover the vertices with vertex-disjoint cycles, each serving a demand of at most one. The objective is to minimize a linear combination of the total length and the number of cycles. This problem is closely related to the capacitated vehicle routing problem (CVRP) and other cycle cover problems such as min-max cycle cover and bounded cycle cover. We show that a greedy algorithm followed by a post-processing step yields a $$(2 + \frac{2}{7})$$ ( 2 + 2 7 ) -approximation for this problem by comparing the solution to a polymatroid relaxation. We also show that the analysis of our algorithm is tight and provide a $$2 + \epsilon $$ 2 + ϵ lower bound for the relaxation.

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

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