Robust text watermarking based on line shifting

2020 ◽  
Vol 7 (1) ◽  
pp. 3-13
Author(s):  
Alexander Kozachok ◽  
Sergey Kopylov
Keyword(s):  
Block Diagram ◽  
Data Embedding ◽  
Process Data ◽  
Text Data ◽  
Data Format ◽  
Robust Watermark ◽  
Watermark Embedding ◽  
Line Spacing ◽  
On Line

 Abstract— This article presents an approach to protection of printed text data by watermark embedding in the printing process. Data protection is based on robust watermark embedding that is invariant to text data format converting into image. The choice of a robust watermark within the confines of the presented classification of digital watermark is justified. The requirements to developed robust watermark have been formed. According to the formed requirements and existing restrictions, an approach to robust watermark embedding into text data based on a steganographic algorithm of line spacing shifting has been developed. The block diagram and the description of the developed algorithm of data embedding into text data are given. An experimental estimation of the embedding capacity and perceptual invisibility of the developed data embedding approach was carried out. An approach to extract embedded information from images containing a robust watermark has been developed. The limits of the retrieval, extraction accuracy and robustness evaluation of embedded data to various transformations have been experimentally established.Tóm tắt— Bài báo trình bày cách tiếp cận để bảo vệ dữ liệu văn bản in bằng cách nhúng vào văn bản một đoạn thủy vân trong quá trình in. Bảo vệ dữ liệu dựa trên việc sử dụng thủy vân bền vững có khả năng chống lại sự chuyển đổi định dạng dữ liệu văn bản sang dữ liệu hình ảnh. Sau quá trình phân tích các hệ thống thủy vân số hiện có, nhận thấy việc lựa chọn một mô hình thủy vân bền vững là hợp lý. Do yêu cầu thực tế và các hạn chế của phương pháp nhúng thủy vân vào dữ liệu văn bản hiện có, bài báo đưa ra phương pháp nhúng mới được phát triển dựa trên một thuật toán ẩn mã sử dụng cách thay đổi khoảng cách giữa các dòng trong văn bản. Bài báo đưa ra một sơ đồ khối và mô tả thuật toán nhúng thông tin vào dữ liệu văn bản. Các thực nghiệm về khả năng nhúng và khả năng che giấu thông tin với tri giác thông thường của dữ liệu nhúng cũng được trình bày. Bài báo cũng nêu cách tiếp cận để trích xuất thông tin được nhúng từ các hình ảnh có chứa thủy vân bền vững. Bên cạnh đó, chúng tôi cũng đưa ra các giới hạn về khả năng ứng dụng của phương pháp dựa trên các thực nghiệm, các đánh giá về độ chính xác của việc trích xuất được dữ liệu và độ mạnh của phương pháp nhúng mới này đối với các phép biến đổi ảnh khác nhau. 

On-line alignment and astigmatism correction using a TV and personal computer

1993 ◽  
Vol 51 ◽  
pp. 202-203
Author(s):  
F. Hosokawa ◽  
Y. Kondo ◽  
T. Honda ◽  
Y. Ishida ◽  
M. Kersker
Keyword(s):  
High Resolution ◽  
Personal Computer ◽  
Block Diagram ◽  
Incident Beam ◽  
Ccd Camera ◽  
Alignment Procedure ◽  
Astigmatism Correction ◽  
Transmission Electron ◽  
Alignment System ◽  
On Line

High-resolution transmission electron microscopy must attain utmost accuracy in the alignment of incident beam direction and in astigmatism correction, and that, in the shortest possible time. As a method to eliminate this troublesome work, an automatic alignment system using the Slow-Scan CCD camera has been introduced recently. In this method, diffractograms of amorphous images are calculated and analyzed to detect misalignment and astigmatism automatically. In the present study, we also examined diffractogram analysis using a personal computer and digitized TV images, and found that TV images provided enough quality for the on-line alignment procedure of high-resolution work in TEM. Fig. 1 shows a block diagram of our system. The averaged image is digitized by a TV board and is transported to a computer memory, then a diffractogram is calculated using an FFT board, and the feedback parameters which are determined by diffractogram analysis are sent to the microscope(JEM- 2010) through the RS232C interface. The on-line correction system has the following three modes.

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong
Keyword(s):  
Data Collection ◽  
Pap Smear ◽  
Operative Procedures ◽  
Free Text ◽  
Collection System ◽  
Process Data ◽  
Text Data ◽  
Data Collection System ◽  
History Of ◽  
Correlation System

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

A Hybrid Methodology For On-Line Process Monitoring

2004 ◽  
Vol 2 (1) ◽  
Author(s):  
Rakesh Kumar ◽  
Avinash M. Jade ◽  
Valadi K. Jayaraman ◽  
Bhaskar D. Kulkarni
Keyword(s):  
Process Monitoring ◽  
Current Practice ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Hybrid Strategy ◽  
Vector Machines ◽  
On Line ◽  
Line Process

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

Automatic Classification of 2D Partial Discharge from Generator On-Line Measurement

2021 ◽  
Author(s):  
Claude Hudon ◽  
Melanie Levesque ◽  
Olivier Kokoko ◽  
Normand Amyot ◽  
Ryad Zemouri
Keyword(s):  
Partial Discharge ◽  
Automatic Classification ◽  
Line Measurement ◽  
On Line

scholarly journals Multiresolution analysis of vibration signals acquired from locomotive Diesel engine for classification of engine states basing on signal statistical parameters

2017 ◽  
Vol 168 (1) ◽  
pp. 68-72
Author(s):  
Piotr BOGUŚ ◽  
Mateusz CIESZYŃSKI ◽  
Jerzy MERKISZ
Keyword(s):  
Diesel Engine ◽  
Complex Analysis ◽  
Statistical Parameters ◽  
Vibration Signals ◽  
Time Frequency ◽  
Significant Difference ◽  
Before And After ◽  
On Line ◽  
Locomotive Diesel

The paper presents a method of classification of locomotive Diesel engine states basing on vibration signals taken from an engine body and using chosen statistical parameters calculated for the original signal and it wavelet multiresolution components. The researches presented in the paper concern estimation of an engine states before and after a general repair. The target application of the presented researches is an on-line diagnostic system which can complement standard OBD systems. To this purpose the applied methods should not base on complex analysis of some spectral, time-frequency or scalogram plots but rather on choosing single diagnostic parameters which are suitable for the fast on-line diagnostic. The results have showed the significant difference in distinguishing of engine work before and after a general repair using some chosen statistical parameters applied to vibration signals.

scholarly journals A Data Mining Technique for Tourist Destination Brand Image Building

2020 ◽  
Vol 8 (6) ◽  
pp. 4617-4622
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Tourism Industry ◽  
Destination Image ◽  
Data Mining Technique ◽  
Tourist Destination ◽  
Text Data ◽  
Clustering And Classification ◽  
Google Search

The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested ,in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure. In further the data is labeled based on the positive and negative words available in the collected facts. Finally the clustering and classification of text is performed. For clustering of data FCM (fuzzy c means) clustering algorithm and for classification the Bayesian classifier is used. Based on final classification of text data the decision is made for the destination visits.

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