The technique, digital steganography is a secret data hiding scheme which involves hiding data in multimedia files. Steganographic methods embed secret data in the cover media with minimum visual and statistical distortion. This paper introduces a new steganographic method in transform domain based on contourlet PDFB (pyramidal directional filter bank) based transform. Contourlet is a multiscale and multidirectional transform domain, which provides the both spatial and frequency domain information. This method also uses the wavelet based compression technique like that of JPEG2000 on the secret data for transferring more information with fewer data. The approximation sub-band is embedded in the contourlet coefficients of the cover image. Approximation subband is multiplied by a constant factor k, which is determined experimentally. Modified values of approximation sub-band are embedded in the contourlet subband of the cover image by replacing them directly. Experimental results give that the proposed method has a good imperceptibility and to show this, the method uses four different visual quality metrics.

A new steganographic method using Contourlet Transform

The development of Information Technology is very rapid at this time, triggering developments in other fields, one of them in the field of education. Currently, Information Technology is not only viewed as a field of education, but more than that Information Technology began to be developed in order to assist the development of the field of education itself. This is because the development of Information Technology in support of information exchange progress is increasingly dominant in today's society. Information Technology is expected to not only support the development of education alone, but more than that Information Technology is expected to provide solutions on existing educational and information problems. Application of steganography in message insertion will be more valuable that can be used as a positive value tool. In this research will be made system of steganography with LSB (Least Significant Bit) method. With the aim that all the information (message) inserted into the media images, audio and video could not be opened easily by people who have no interest.

http://www.irjcs.com/volumes/vol4/iss08/02.AUCS10081.pdf

Implementation of Steganography in Image Media Using Algorithm LSB (Least Significant Bit)

ECG Steganography Using Contourlet Transform For Transmission of Secured Patient Identity.

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

/pdf/simplified-markerless-stride-detection-pipeline-smasdp-for-2un4mbq2.pdf

Edward Jero Sam Jeeva Raj

Papers

ECG Steganography Using Contourlet Transform For Transmission of Secured Patient Identity.

Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation