Govt. Engineering College Kozhikode, Dept of Applied Electronics and Instrumentation
Assistant Professor in the department of Applied Electronics and Instrumentation Engineering at Government Engineering College Kozhikode. I had been in the field of teaching under graduate engineering programmes since 2001.
My research interests inlcude image processing, computer vision, embedded systems, inverse problems in signal processing for signal recovery. I am enthusiastic in developing new systems and technologies in the broad field of electronics and computer engineering for making our life easier.
Govt. Engineering College Kozhikode, Dept of Applied Electronics and Instrumentation
Govt. College of Engineering Kannur, Dept of Electronics and Communication
Network Systems and Technolgies Private Limited, Technopark Trivandrum
MES College of Engineering,Dept. of Electronics and Communication Engineering
Ph.D. in Signal Processing(Pursuing)
National Institute of Technology Calicut
Master of Technology in Signal Processing
College of Engineering Trivandrum
University of Kerala
Bachelor of Technology in Electronics and Communication Engineering
Govt. Engineering College Kannur
University of Calicut, Kerala
1. Complex Analysis:Complex Plane -Analytic Functions -Complex Integration -Bilinear Transformation -Value distribution theory.
2. Fourier Analysis: Fourier Coefficients -Sine and Cosine series -Half range series -Convergence -Fourier Integral -Applications.
3. Special Functions:Beta and Gamma Functions -HypergeometricFunctions -Hermite, Laguerreand Jacobi Orthogonal Polynomials.
4. Outline of Mathematical Software: Outline related to the above topics in Mathematicasoftware will be provided.
Algorithm is an important mathematical idea that all students need to understand and use. An algorithm is a finite sequence of rules for solving a particular problem. It has enormous application in modern human life such as mobile, sensor, aerospace, satellite to name a few. Algorithmic study involves applying, developing, analyzing, and understanding the nature of algorithms.
Engineering students often find difficulties to understand algorithm properly due to poor teaching. The primary objective of the workshop is to bring faculty members from different institutions to share knowledge, skills and techniques of teaching and learning approach. More specifically, the aim of this workshop is to discuss (i) importance of algorithm, (ii) teaching technique to the students, (iii) motivate to do research in algorithms, (iv) some state-of-the-art algorithmic research area, etc.
Schools and colleges often focus on theory, forgetting that practical experiments teach students much more. Members of Inter University Accelerator Centre (IUAC), an autonomous research institute that provides research facilities to universities, realised the need for experiments in education when teachers and students from other universities visited their university and tried to use a particle accelerator. They were not familiar with computer-controlled experiments that use machines to collect data.
Dr Ajith Kumar, a scientist at IUAC, wished to provide teachers and students with exposure to this technology and started designing a low-cost device that measured and collected data, and displayed the results graphically on the computer screen.Along with members of the electronics department, he developed Phoenix expEYES—a combination of hardware and software framework for computer-interfaced science experiments that doesn’t require the user to get into the details of electronics or computer programming.
NVIDIA Corporation, USA has awarded IIT Bombay a CUDA Center of Excellence (CCOE). The CCOE of IIT Bombay, in association with NVIDIA, is conducting a National Level Workshop on GPU Programming and Applications (GPA-2014) during 24th – 26th February 2014 at IIT Bombay. GPA-2014 is a three day workshop on heterogeneous parallel computing. GPA-2014 is aimed at enriching the skills of students, faculty and researchers with cutting-edge techniques and hands-on experience in developing applications for many-core processors, with massively parallel computing resources like GPU accelerators.
There are revolutionary changes taking place in the paradigm of teaching-learning process brought out by the availability of new methodologies/technologies for knowledge sharing, and the new context and pressures of increase in and variety of the student population, not to talk of the gradual globalization of the education process itself. Under these circumstances it is essential for the teaching fraternity to hone their existing skills, develop new skills, and learn the nuances of the art, philosophy and science of teaching so that the teaching-learning process becomes more enjoyable, creative and productive, for both the teachers and students. There is need to connect three vital elements of education viz. faculty, student and the process for evolving right mix of solutions among the variety of approaches which significantly vary in different ambience.
A significant change required in this regard cannot happen by chance and hence a focused effort is needed from committed faculty and the administration.The core team conducts FDPs for faculty from other engineering colleges placed in and around Chennai.
A Talk on Microprocessors and microcontrollers for sixth semester EEE students of Govt. Engineering College Wayanad on 8 th February 2016.
A talk on Microcontroller programming using embedded C in the MNAF-2013 organized by Government College of Engineering Kannur on 8th October 2013.
A talk on Digital filter design using MATLAB in the Short term training programme on Signal Processing for Communication held at GEC Wayanad from 9-13 July 2012.
A talk on Microcontrollers and interfacing in the visiting faculty programme at Womens poly technic Kottakkal in the year 2012.
His research interests are in the general area of signal processing algorithms and systems and their applications.
its a low cost system intended to give warning about the nearby ships to the fishermen.
Its a software tool to convert handwritten characters for online sorting searching and recoginition..
This paper proposes methods for super resolving single noisy low resolution images. Even if single image super resolution has been a topic of research for last few decades, super resolution of noisy low resolution images is still a challenging problem. Most of the state of the art super resolution algorithms will fail to perform if significant amount of noise is present in the observed image. In this paper, we propose a denoised patch dictionary based single image super resolution algorithm. To enhance the robustness to noise performance, this method is further modified by using a wavelet based fusion algorithm which combines the result of proposed method with direct super resolved image, and super resolved image after denoising to preserve the finer details of the super resolved image. The proposed methods are applied on the commonly used test images. The results validate that the proposed methods show improvement over the existing techniques.
This paper proposes learning based approaches for single image super-resolution using sparse representation and neighbor embedding. Two learning based methods are proposed to recover the high-resolution (HR) image patches from the low resolution (LR) patches. The first method, named as LeNm-SRI, is a computationally efficient approach using neighbor embedding in a partitioned feature space. In this method, the training set is updated by including details extracted from different scales of LR input image. LeNm-SRI, which uses sparse representation invariance, gives acceptable results at low computational load. In the second approach, named as LeNm-RBM, a statistical prediction model is used to predict HR feature coefficients to obtain increased performance. Separate prediction models are trained for each cluster, and the model parameters are updated with each input image, to adapt to input test image. Experimental results validate the computational efficiency and performance of the proposed methods.
Position-patch based approaches have been proposed for single-image face hallucination. This paper models the face hallucination problem as a coefficient recovery problem with respect to an adaptive training set for improved noise robustness. The image-adaptive training set is constructed by corrupting a local training set of position-patches by adding specific amounts of noise depending on the input image noise level. In this proposed method, image denoising and super-resolution are simultaneously carried out to obtain superior results. Though the principle is general and can be extended to most super-resolution algorithms, we discuss this in context of existing locality-constrained representation (LcR) approach in order to compare their performances. It can be demonstrated that the proposed approach can quantitatively and qualitatively yield better results in high noisy environments.
This paper proposes a self-learning based dictionary training approach for single image super-resolution where the low-resolution (LR) and high-resolution (HR) dictionaries are jointly trained using training data set and different scaled versions of the input image. Local variance based salient feature identification is carried out to speed up the super-resolution algorithm. It can be demonstrated that our modified SR algorithm can qualitatively and quantitatively outperform bicubic interpolation and state-of-the-art methods..
This paper proposes a self-learning based dictionary training approach for single image super-resolution where the low-resolution (LR) and high-resolution (HR) dictionaries are jointly trained using training data set and different scaled versions of the input image. Local variance based salient feature identification is carried out to speed up the super-resolution algorithm. It can be demonstrated that our modified SR algorithm can qualitatively and quantitatively outperform bicubic interpolation and state-of-the-art methods.
Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. In applications like face recognition, face detection etc. resolution enhancement techniques are therefore generally essential. Super resolution is the process of determining and adding missing high frequency information in the image to improve the resolution. It is highly useful in the areas of recognition, identification, compression, etc. Face hallucination is a subset of super resolution. This work is intended to enhance the visual quality and resolution of a facial image. It focuses on the eigen transform based face super resolution techniques in transform domain. Advantage of eigen transformation based technique is that, it does not require iterative optimization techniques and hence comparatively faster. Eigen transform is performed in wavelet transform and discrete cosine transform domains and the results are presented. The results establish the fact that the eigen transform is efficient in transform domain also and thus it can be directly applied with slight modifications on the compressed images.
Abstract: A vision based vehicle guidance system must be able to detect and recognize traffic signs. Traffic sign recognition systems collect information about road signs and helps the driver to make timely decisions, making driving safer and easier. This paper deals with the detection and recognition of traffic signs from image sequences using the colour information. Colour based segmentation techniques are employed for traffic sign detection. In order to improve the performance of segmentation, we used the product of enhanced hue and saturation components. To obtain better shape classification performance, we used linear support vector machine with the Distance to Border features of the segmented blobs. Recognition of traffic signs are implemented using multi-classifier non-linear support vector machine with edge related pixels of interest as the feature.
Faces often appear very small in surveillance imagery because of the wide fields of view that are typically used and the relatively large distance between the cameras and the scene. In applications like face recognition, face detection etc. resolution enhancement techniques are therefore generally essential. Super resolution is the process of determining and adding missing high frequency information in the image to improve the resolution. It is highly useful in the areas of recognition, identification, compression, etc. Face hallucination is a subset of super resolution. This work is intended to enhance the visual quality and resolution of a facial image. It focuses on the Eigen transform based face super resolution techniques in transform domain. Advantage of Eigen transformation based technique is that, it does not require iterative optimization techniques and hence comparatively faster. Eigen transform is performed in wavelet transform and discrete cosine transform domains and the results are presented. The results establish the fact that the Eigen transform is efficient in transform domain also and thus it can be directly applied with slight modifications on the compressed images.
09AE7187 Optimization Techniques – for odd semester PhD course work.
09AE7117 Speech and Audio Processing – S3 - M Tech signal processing 2016-18 batch.
EC14 707(P) Communication systems Lab – S7 BTech ECE - 2014-18 batch.
EC14 501:Computer Organisation and Architecture.
EC14 702:Microwave Engineering.
AI09 702:Advanced Instrumentation.
AI09 505:Power Electronics.
AI09 601:Digital Signal Processing.
AI09 707(P):System Simulation Lab.
09AE6132:Adaptive Signal Processing.
09AE6131:Random Process for Applications.
09AE6171:DSP Systems Lab.
09AE6172:Signal Processing Lab
1.Princy N R: Multi-frame image restoration using ADMM in a tensor frame work.
1. Jesna Augastine: Learning based text image super resolution.
2. Thahira M: Neighbour embedding based face image super resolution.
3. Jesleena : Multiframe super resolution using iterative algorithms - Co-guide.
1.Junaid : SVD based single image super resolution.
2.Thanooja L: Fast matting algorithm for robust text image super resolution.
1.Rafeeque TP: Super Resolution of MRI images in spatial and wavelet domain.
2.Jabir K: Comparison of Threshold Selection Methods for Wavelet Based MRI Image De-noising
3.Steffy Maria Joseph: Online Malayalam handwritten character recognition- Co-guide.
S7 ECE – 2014-18 batch
1. Smart Aid for Blind.
1.1.1. Aneesa c.k -05
1.1.2. Anjusree b.p- 08
1.1.3. Aswani v.t -14
1.1.4. Jumana jabin m.k -27
1.1.5. Thamjeeda k.m -47
2. Sign language glove with speech synthesizer.
2.1.1. AISWARYA K.H -03
2.1.2. SACHIN K MOHAN-38
2.1.3. SIVADETH S-44
2.1.4. VISHNU PRASAD P S-51
2.1.5. DEVAPRASAD K K-56
I would be happy to talk to you if you need my assistance in your research or whether you need bussiness administration support for your company.
Department of Applied Electronics and Instrumentation.
Government Engineering College Kozhikode,
Westhill-673005, Calicut, Kerala.
You can find me at my office located at first floor of Dept. of Applied Electronics and Instrumentation GEC Kozhikode.
I am at my office every day from 9:30AM until 4:30PM, but you may consider a call to fix an appointment.