Neural Interfaces in Digital Product Design

Volume 6 Issue 1 January - March 2018

Research Paper

Neural Interfaces in Digital Product Design

Tom Page*, Gisli Thorsteinsson**

* Associate Professor, Department of Product Design, Nottingham Trent University, England.

** Professor, Department of Design and Craft Education, University of Iceland, Iceland.

Page, T., and Thorsteinsson, G. (2018). Neural Interfaces in Digital Product Design. i-manager's Journal on Digital Signal Processing, 6(1), 1-9. https://doi.org/10.26634/jdp.6.1.15155

Abstract

Brain Computer Interfaces (BCI's) allow people to control computers and other devices by thought alone (Bogue, 2010). The technology is beginning to migrate from research laboratories to real world applications, which raises a number of design issues. The aim of this research was to identify existing and possible future applications of this new technology and explore design issues related to their development. The literature review revealed conflicting evidence surrounding the capabilities of low cost, consumer BCIs. Manufacturers of these systems suggest the technology can measure the concentration level of users whilst performing mental tasks. This study aimed to determine how accurate this measure of concentration is through a controlled experiment. In the trial, 14 participants completed a range of mental exercises which gradually increased in difficulty. During the tasks users wore a BCI headset, which measured their level of attention. Users were asked to assess the level of the concentration required to complete the task using a five point rating system. Data recorded by the headset was compared to the subjective measures and no significant correlations were found. This indicates such devices cannot currently be used to accurately measure the concentration. The report identifies limitations in the current technology, which may contribute to this inaccuracy and suggests that the increased contribution from designers may help overcome these limitations.

A Survey on Different Noise Removal Techniques in Images

Volume 5 Issue 4 October - December 2017

Survey Paper

A Survey on Different Noise Removal Techniques in Images

S. Eljin*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, India.

Eljin, S. (2017). A Survey on Different Noise Removal Techniques in Images. i-manager's Journal on Digital Signal Processing, 5(4), 27-33. https://doi.org/10.26634/jdp.5.4.14563

Abstract

The process of removing noise from the signal is known as Noise Reduction. Both the digital and analog recordable devices can be affected by noise. Noise can be of two variations, it can be either a coherent noise which could be introduced by the algorithm or they can be of non-coherent with white or random noise. Since the structure of the medium is a grained one, noise is introduced in both the photographic and magnetic taped scenarios accordingly. Noise can be reduced by different techniques with a corresponding algorithm or methodology, whereas in this paper, the author comprises the survey of different noise removal techniques from different authors’ point of view.

Removal of Noise in Speech Signal – A Review

Volume 5 Issue 4 October - December 2017

Review Paper

Removal of Noise in Speech Signal – A Review

H. Hensiba*

Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Hensiba, H. (2017). Removal of Noise in Speech Signal – A Review. i-manager's Journal on Digital Signal Processing, 5(4), 20-26.https://doi.org/10.26634/jdp.5.4.14562

Abstract

In almost all the acoustic environments, noise is always considered as a ubiquitous one. The quality of the signal gets degraded and also contaminated because of the infection, which was caused by various sources when one speaks through the microphone. Here there is a possibility, where there may be a harm caused when human to machine communication happens. The digital filtering problem is considered in this paper, which is the estimation of the clean speech from Noise detection as well as Noise reduction. The estimation is done through linear filtering of noise in the speech signal. In this paper, the author has reviewed different and various speech signal processing techniques, where the noise gets affected and also how the noise gets removed.

Frequency Based Filtering for Voice Activity Detection

Volume 5 Issue 4 October - December 2017

Research Paper

Frequency Based Filtering for Voice Activity Detection

V.Adlin Vini*

Post Graduate, Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

Vini, A, V. (2017). Frequency Based Filtering for Voice Activity Detection. i-manager's Journal on Digital Signal Processing, 5(4), 10-19. https://doi.org/10.26634/jdp.5.4.14561

Abstract

Signal Processing is used to bring out the speech in a degraded signal. Amplitude of the signal is obtained by using the SFF (Single Frequency Filtering). Spectral and Temporal resolutions are compared by using three different methods, which are discussed in this paper. Voice Activity Detection is the process in which any noise or disturbance that are made to the speech signal is detected. In this paper, the author has proposed Voice Activity Detection system with the help of Frequency based filtering method. The experimental results show that it gives better results compared to the existing systems.

Image Segmentation using Fuzzy C means Clustering with Mahalanobis Distance Norm

Volume 5 Issue 4 October - December 2017

Research Paper

Image Segmentation using Fuzzy C means Clustering with Mahalanobis Distance Norm

Jincy V. Raj *, S. Jini Mol **, Jisha G. Das***, R.S. Sajitha ****

*-**** B.E Scholars, Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, India.

Raj, J, V., Mol, J, S., Das, J, G., and Sajitha, R, S. (2017). Image Segmentation Using Fuzzy C means Clustering With Mahalanobis Distance Norm. i-manager's Journal on Digital Signal Processing, 5(4), 1-9. https://doi.org/10.26634/jdp.5.4.14560

Abstract

In order to map the image, color intensity of the image, or for detecting the object image segmentation is used. It is one of the important procedures used by many of the algorithms. Fuzzy C Means algorithm is one of the effective and powerful image segmentation algorithms compared to all other segments. To describe or explain the dissimilarity in-between Clustered Prototype and the data acquired, FCM uses Euclidean distance to resolve (Zhao et al., 2015). Since the mean information of the cluster is only characterized by the Euclidean distance, both the cluster divergence and noise is made sensitive. Mahalanobis distance is more accurate than the Euclidean distance as a dissimilarity measure when they are used for image segmentation, and they also used to define the mean and covariance of a cluster. The final experimental results show that the Mahalanobis distance is more accurate than the Euclidean distance.

Algorithms for the Analysis of MST Radar Signals - A Survey

Volume 5 Issue 3 July - September 2017

Review Paper

Algorithms for the Analysis of MST Radar Signals - A Survey

P. Suresh Babu*, G.Sreenivasulu**

* Research Scholar, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

** Professor and Head, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India.

Babu, S, P., and Sreenivasulu, G. (2017). Algorithms for the Analysis of MST Radar Signals - A Survey. i-manager's Journal on Digital Signal Processing, 5(3), 40-44. https://doi.org/10.26634/jdp.5.3.13933

Abstract

The analysis of the MST Radar Data has become more predominant in the present world. A major research on MST Radar Data was going on in NARL, Gadanki, which provides the data regarding the atmospheric movements. In order to obtain the information on the wind parameters, the signals collected from the radar are analyzed, which mainly involves the estimation of power spectrum. Analysis of the MST Radar Data involves the estimation of the Signal to Noise Ratio (SNR) of the Doppler profiles, Wind Profiles and Parameters, Temperature Profiles, etc. In NARL, the weather data or the Meteor data was collected and the data was converted into raw format. This raw data was used for processing either in time domain or frequency domain. Many techniques have been employed for processing the MST Radar data, which shows better results when compared to the GPS Data (Data Processed in NARL). This paper mainly gives survey of papers in which various methods are used for the analysis of the MST Radar data.

Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools

Volume 5 Issue 3 July - September 2017

Research Paper

Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools

Padmaja Grandhe*, E. Sreenivasa Reddy**, D. Vasumathi***

* Research Scholar, Department of Computer Science and Engineering, JNTUK, Kakinada, A.P, India.

** Dean & Professor, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, A.P, India.

*** Professor, Department of Computer Science and Engineering, JNTUH, Hyderabad, Telangana, India.

Grandhe, P., Reddy, S, E., and Vasumathi, D. (2017). Fusion Based Integrated Advance Magnetic Visualization of MRI 3D Images Using Advance Matlab Tools. i-manager's Journal on Digital Signal Processing, 5(3), 27-39. https://doi.org/10.26634/jdp.5.3.13932

Abstract

The fusion expression means to extract a sequence which is acquired in several domains. The three-dimensional (3D) images have the deep information, which is not available in the conventional 2D images. The image fusion procedure of two images aim to get a more in-depth examination of the picture. 3D Fusion of medical images are found to be useful that they are medical images containing the data with significant scientific information for doctors during their analysis. The objective of this work is to examine the subsections of the obtained 3D structure along three axes. The paper deals with the DICOM (Digital Imaging and Communication in Medicine) images restoration, which is initially extremely useful for production of customized data that are atomically implemented by using a fast prototyping technology. MRI Images provide better contrast of soft tissues than CT images. Hence it provides better results in image fusion of MRI and CT images is done by using Wavelet Transforms in MATLAB. The researchers are forever focusing on biomedical 3D imaging configuration. The image slices of the involved region in the modified image in DICOM format are preprocessed first using developed a Matlab code, which is an open source medical software used to reconstruct structures of the human body based on three-dimensional images which are acquired using CT or MRI images. The proposed algorithm FBIAMV (Fusion Based Integrated Advance Magnetic Visualization) of MRI 3D images generates the three-dimensional models equivalent to different parts of the human body. The proposed multiform method help doctors and other clinicians in diagnosis of diseases leading to a better treatment.