Showing papers by "K. P. Soman published in 2013"

Low cost digital transceiver design for Software Defined Radio using RTL-SDR

Abstract: The field of wireless communication has become the hottest area and Software Defined Radio (SDR) is revolutionizing it. By bringing much functionality as software, SDR reduces the cost of hardware maintenance and up-gradation. Open source hardware such as USRP (Universal Software Radio Peripheral) and software called GNU Radio-Companion are commonly used to do experiments in SDR. Since the cost of USRP is high, a low cost set up is needed which is affordable by the student community. In this paper a low cost alternative to USRP is proposed using RTL-SDR (Realtek Software Defined Radio) which is only used for reception. For transmitting purpose, a mixer circuit can be used to map the baseband signal to the band that can be received by RTL-SDR on the other end on Linux / Windows platform. Initially, the experiment is done in simulation. After that, it is tested with low cost hardware such as mixer and RTL-SDR. The cost for total transceiver system can be less than USD 100 which is 10 times less than the existing one.

Exploiting GNU radio and USRP: An economical test bed for real time communication systems

Abstract: Communication experiments using normal lab setup, which includes more hardware and less software raises the cost of the total system. The method proposed here provides a new approach through which all the analog and digital experiments can be performed using a single hardware-USRP (Universal Software Radio Peripheral) and software-GNU Radio Companion (GRC). Initially, networking setup is formulated using SDR technology. Later on, one of the analog communication experiments is demonstrated in real time using the GNU Radio Companion, RTL-SDR and USRP. The entire communication system is less expensive as the system uses a single reprogrammable hardware and most of the focus of the system deals with the software part.

Ensuring Security to the Compressed Sensing Data Using a Steganographic Approach

Abstract: This paper focuses on the strength of combining cryptography and steganography methods to enhance the security of communication over an open channel. Here the data to be send are secured by using the compressive sensing method and the Singular Value Decomposition (SVD) based embedding method. The data is encrypted using the compressive measurements of the data and the resultant data is embedded in the cover object using the SVD based water mark embedding algorithm. This approach helps to send the secret data after hiding in a cover image. The compressive sensing method helps to compress and encrypt the data in a single step. The proposed system provides more security to the compressed data. This scheme significantly reduces the attacks. This method is very useful to hide the secret images. The results demonstrate that the proposed system is highly efficient and robust.

OpenBTS based microtelecom model: A socio-economic boon to rural communities

Abstract: This paper proposes a low cost, low power, reconfigurable and flexible Open BTS (Base Transceiver Station) model based on SDR (Software Defined Radio) using USRP. A microtelecom model serves people at the “bottom of the pyramid” along with ensuring the ROI (Return on Investment) for MNO's (Mobile Network Operators). Thus a new telecom revolution that clubs Microtelecom business model with Open BTS concept would positively affect the socio-economic progress of rural communities, thereby ensuring the overall growth of developing nations. The success of a proposed model of this kind would encourage government to take initiatives and confidently invest on policies that would benefit the low-waged and less-privileged rural communities.

2D Image data approximation using Savitzky Golay filter — Smoothing and differencing

Abstract: Smoothing and differencing is one of the major important and necessary step in the field of signal processing, image processing and also in the field on analytical chemistry. The search for an efficient image smoothing and edge detection method is a challenging task in image processing sector. Savitzky Golay Filters are one among the widely used filters for analytical chemistry. Even though they have exceptional features, they are rarely used in the field of image processing. The designed filter is applied for image smoothing and a mathematical model based on partial derivatives is proposed to extract the edges in images. The smoothing technique of SG filter offers an extremely simple aid in extracting the edge information. An approach using SG filter which can be applied in preserving edge information is one of the major tasks involved in the classification process in the domain of Optical Character Recognition. The paper is focused on designing the Savitzky Golay filter by using the concepts of linear algebra. The main objective of the paper is to portray a clear cut idea about Savitzky Golay filter and to study the design of Savitsky Golay filters based on the concepts of Linear Algebra.

An experimental study on application of Orthogonal Matching Pursuit algorithm for image denoising

Abstract: Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR).

Innovative use of What if analysis for visualization

Abstract: 252 Abstract—The novelty of this paper is aimed at simplifying the tasks in Spreadsheet using a feature called what if analysis, which fires up the performance of the task we are working on. The use of spreadsheet helped us save time, perform many operations such as sorting, searching, classifying and comparing easily and to solve a problem without any programming knowledge. What if or sensitivity analysis is one of the most powerful and valuable concepts in Spreadsheet, the potential of which is not well exploited. The advantage of what if analysis is that if we show one computation in excel, the remaining part of the process will be computed by its own for a given range of variable values. It can be used to solve many problems other than the conventional managerial applications. Two Dimensional function evaluation and graphing, creating Pascal triangles, enumerating Pythagorean triplets in a given range, error function evaluation are some of the real applications. These types of applications can be exploited to enhance computational thinking of children in high schools. Computational thinking brings about a neoteric approach in problem-solving and model simulation.

A cryptographic approach to video watermarking based on compressive sensing, arnold transform, sum of absolute deviation and SVD

Abstract: Video watermarking is relatively a new technology to ensure protection of intellectual property rights and to stop video piracy. The ownership information or watermark is normally hidden in the video sequences. In this paper, a novel video watermarking method is proposed to protect the ownership information in a robust way. This method encrypts the watermark by combining the strengths of Compressive Sensing and Arnold scrambling. The frames for watermark embedding are chosen by computing the Sum of Absolute Deviation between successive frames. The cipher watermark is then embedded into chosen frames based on SVD watermark embedding algorithm. The decryption stage performs SVD watermark extraction algorithm, Arnold inverse transform and L1 optimization for retrieval of watermark. Experimental results show that proposed method enhances the security of the ownership information embedded.

Compressive estimation of UWA channels for OFDM transmission using iterative sparse reconstruction algorithms

Abstract: Channel estimation is an important aspect in wireless communication, in which an estimate of the interference caused to the normal transmission is found, which is then cancelled to retrieve the original signal. In UnderWater Acoustic transmission, two main effects are delay spread and Doppler shift. It has been found[10] that while sampling in the delay - Doppler domain, the effect of the channel can be treated as sparse. Thus framing the estimation problem as an optimization problem of the form of a Basis Pursuit De Noising (BPDN)[21] and solving it using sparse reconstruction methods could be a good technique. In addition to giving good sparse solution, the technique also assures low computational complexity, (due to iterative nature of solution methodology) when compared to traditional estimation methods like Least Square Estimation (LSE) and Minimum Mean Square Error Estimation(MMSE).

A comparative study of spike and smooth separation from a signal using different overcomplete dictionary

Abstract: Most of the natural signals are complex and are highly time varying, since they are non stationary in nature. In this paper, a comparative study for separating spikes and smooth signal components from a non-stationary signal are performed based on different overcomplete dictionaries. The experiment is evaluated using the sparse representation with different bases such as the Discrete Cosine Transform (DCT), Walsh-Hadamard, Orthogonal and Biorthogonal wavelet basis. The primary focus of this paper is to use L1 minimization for retrieving the smooth and spikes component of the signal using different overcomplete dictionary. The experimental results reveals out the dictionary that delivers a better separation without distorting temporal and spectral characteristics.

A Combined Crypto-steganographic Approach for Information Hiding in Audio Signals Using Sub-band Coding, Compressive Sensing and Singular Value Decomposition

Abstract: In this paper, a new method of audio data security system is proposed, which uses the complementary services provided by steganography and cryptography. Here the audio data to be send secretly is encoded using the compressive measurements of the same and the resultant data is embedded in the perceptible band of the cover audio data using the SVD based watermarking algorithm. Thus the combination of these two methods enhances the protection against most serious attacks when audio signals are transmitted over an open channel. Decryption stage uses SVD based watermark extraction algorithm and L1 optimization. Experimental results show that the combined system enhances the security of the audio data embedded.

Sparsity based Single Object Tracking

Abstract: Object tracking has importance in various video processing applications like video surveillance, perceptual user interface driver assistance, tracking etc. This paper deals with a new tracking technique that combines the dictionary based background subtraction along with sparsity based tracking. The speed and performance challenges faced during the sparsity based tracking alone are addressed, as it is based on a background subtraction preprocessing and local compressive tracking. It also overcomes the challenges faced by the traditional techniques due to illumination variation, pose and shape change of the object. Output of the proposed technique is compared with that of compressive tracking technique.

Insight into Primal Augmented Lagrangian Multilplier Method

Abstract: We provide a simplified form of Primal Augmented Lagrange Multiplier algorithm. We intend to fill the gap in the steps involved in the mathematical derivations of the algorithm so that an insight into the algorithm is made. The experiment is focused to show the reconstruction done using this algorithm.

Understanding CDMA in linear algebra point of view and its simulation in Excel

Abstract: Code Division Multiple Access (CDMA) is one of the famous channel access method, mainly used in radio communication technologies. Unfortunately this concept is less understood by the student community due to the lack of understanding the mathematical rules behind it. This paper is intended to provide a linear algebra point of explanation of the concepts behind CDMA. The CDMA concept which was otherwise analyzed in spectral point of view is explained using the orthogonality of the bases. The Microsoft Excel Spread Sheet is used as an aid for the simulation since every one can go deep in to the basic concepts.

Binary and Color Image Edge Detection Using RicciFlow

Abstract: The novelty of this paper is edge detection in binary and color images using the Ricci curvature function is proposed. The reason for this is, if the edges in an image are determined accurately, then all of the objects can be located and basic image properties can be measured. In this paper we represent images as a manifold and make use of differential geometry in image processing, namely the Ricci curvature. Over the conventional edge detection methods Canny edge detector is the best available. So, we attempted a visual comparison of edge detection on digital image with Canny detector and Ricci curvature function. We demonstrate some features that can be added or missed in binary images by the Canny edge detector and the registration of edges in color images by the Ricci method.

Insight into Primal Augmented Lagrangian Multilplier Method

Abstract: — We provide a simplified form of Primal Augmented Lagrange Multiplier algorithm. We intend to fill the gap in the steps involved in the mathematical derivations of the algorithm so that an insight into the algorithm is made. The experiment is focused to show the reconstruction done using this algorithm. Keywords-compressive sensing; l1-minimization; sparsity; coherence I.I NTRODUCTION Compressive Sensing (CS) is one of the hot topics in the area of signal processing  1,2,3  . The conventional way to sample the signals follows the Shannon’s theorem, i.e., the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate) [4]. For practical signals using CS, the sampling or sensing goes against the Nyquist rate. Consider the sensing matrix, A () A  nxN , as the concatenation of two orthogonal matrices,  and  . A signal, b  n (non-zero vector) can be represented as the linear combination of columns of Ψ or as a linear combination of columns of . That is in and

Image Restoration Using Knowledge from the Image

Abstract: There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way.

Human Puppetry Using Microsoft Kinect

Abstract: The aim of this paper is to infuse the idea of building a novel interactive application that can control an external device using the skeletal movement of out joints employing Kinect. This paper articulates in detail about Skeletal Tracking and its share in controlling a robotic puppet that impersonates the user’s actions that the user performs using his/her body movements. The basic ideology behind the implementation of the paper is Forward Kinematics. This paper ideally discusses about the utility of Kinect and conveys the applications a user can exploit using this inevitable tool.