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i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Cited

Volume 4 Issue 3 July - September 2016

Research Paper

Design of an Adaptive Noise Cancellation System in a Time Varying, Non-Linear Automobile Environment

S. Thilagam* , P. Karthigaikumar**

* Assistant Professor, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India.

** Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India.

Thilagam.S., and Karthigaikumar.P. (2016). Design of An Adaptive Noise Cancellation System in A Time Varying, Non-Linear Automobile Environment. i-manager’s Journal on Digital Signal Processing, 4(3), 1-5. https://doi.org/10.26634/jdp.4.3.8141

Abstract

There are various types of noises in an automobile. It includes tire noises, belt noises, noises due to braking systems, and engine. The engine noise reduction techniques play a major role to improve the life and efficiency of the engine. However, these noises need to be reduced and cancelled in a real-time environment using statistical Digital Processing techniques. This paper proposes the use of adaptive filters using LMS and RLS algorithms to cancel the engine noise. The performance analysis is done using MATLAB through proven simulation results and by comparing the parameters like Mean Square Error, Convergence speed, and Stability of the system.

References

[1]. Robert Bosch, (2007). Automotive Electrics and Automotive Electronics, 5^th Edition, Springer.

[2]. General Motors, (2011). Popular GMC Terrain Aims for Greater Segment Territory in 2011.

[3]. Klaus Genuit, and Wade R. Bray, (2002). “Prediction of Sound and Vibration in a virtual Automobile”. Sound and Vibration.

[4]. Haeng-woo Lee, (2006). “An Echo Cancellation Algorithm for Reducing the Hardware Complexities and Analysis on its Convergence Characteristics”. Journal of Applied Mathematics, Vol. 20.

[5]. S. Haykin, (2002). Adaptive Filter Theory. 4 Edition, Pearson Education.

[6]. P.S.R. Diniz, (2008). Adaptive Filtering Algorithms and Practical Implementation, 3^rd Edition, Springer.

[7]. S. Thilagam, and P. Karthigaikumar, (2016). “A Study of An Engine Management System for Noise Cancellation Using Electronic Control Unit for Modern Automobiles”. i-manager's Journal on Embedded Systems, Vol. 4, No. 4, pp. 1-4.

[8]. K.R. Borisagar and G.R. Kulkarni, (2012). “Simulation and Performance Analysis of Adaptive Filter in Real-time Noise Over Conventional Fixed Filter”. International Conference on Communication Systems and Network Technologies, pp. 621-624.

[9]. M. Hamidia, and A. Amrouche, (2013). “Double-talk Detection Using the Singular Value Decomposition for Acoustic Echo Cancellation”. IEEE International Conference on Communications, ICC.

[10]. Gale, Z., (2011). “GMC Terrain Crossover uses Noise Cancelling Tech to Quiet Cabin, Help Efficiency”. Truck Trend Magazine.

[11]. Tienan, L. Limo, W. Baochang, X. Aihua, X. and Hang Z., (2002). “Adaptive Noise Canceller and its Applications for Systems with Time-variant Correlative Noise”. IEEE Proceedings of the 4^th World Congress on Intelligent Control and Automation.

[12]. Wang Zhen-li, Zhang Xiong-wei, Yang ji-bin, and Chen Gong, (2006). “Study of a New Fast Adaptive Filtering Algorithm”. Front.Electr.Electron.Eng., China.

[13]. O. Sharifi-Tehrani, (2012). “Design, Simulation and Implementation of an Active Sound-Noise Cancellation System for use in a Cockpit Intercommunication System”. Journal of Applied Research and Technology, Vol. 10, No. 5, pp. 731-736.

[14]. Lloyd Trammell, (2015). “Active Noise Cancellation Method for Automobiles”. US Patent Application Publication.

[15]. S.V. Vaseghi, (2000). Advanced Digital Signal Processing and Noise Reduction, Second Edition, John Wiley and Sons Ltd.,

[16]. B.S. Ryu, J.K. Lee J. Kim and C.W., (2008). “The performance of an adaptive Noise Canceller with DSP Processor”. 40^th South Eastern Symposium on System Theory, University of New Orleans.

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Research Paper

Smart Antennas for Next Generation Mobile Communication

Veerendra Dakulagi* , MD. Bakhar**

* Research Scholar, Department of Electronics and Communication Engineering, Viswesavaraya Technological University, Guru Nanak Dev Engineering College, Bidar, Karnataka, India.

** Professor, Department of Electronics and Communication Engineering, Viswesavaraya Technological University, Guru Nanak Dev Engineering College, Bidar, Karnataka, India.

Veerendra., and MD. Bakhar. (2016). Smart Antennas for Next Generation Mobile Communication. i-manager’s Journal on Digital Signal Processing, 4(3), 6-11. https://doi.org/10.26634/jdp.4.3.8142

Abstract

In this paper, the authors analyzed the performances of well known array signal processing algorithms for adaptive beam forming namely, Sample Matrix Inversion (SMI), and Least Mean Square (LMS) algorithms. From the simulation results, it is observed that, for SMI as the number of user increases, the main lobe spreads in all the directions. As a result of this, beam forming in the user direction may not be accurate. To overcome this problem, LMS algorithm can be used. The LMS algorithm is a best choice in most of the commercial wireless applications due to its low complexity. 5G and beyond 5G networks require beam forming algorithms with high directivity and fast converengence rate. Hence, in this work, they have modified the LMS algorithm to improve the converengence rate and then they applied improved LMS algorithms to horizontal-vertical Uniform Linear Array (ULA). This is also called as two dimensional ULA (2D-ULA). Hence these algorithms are called as 2D-LMS1 and 2D-LMS2. Simulation results show that the proposed algorithms have improved convergence rate, directivity as compared to conventional LMS algorithm. Hence these algorithms are best suited for 5G and beyond mobile communications.

References

[1]. L.C. Godara, (1997). “Application of Antenna Arrays to Mobile Communication. II. Beam-forming and Directionof- arrival Considerations”. Proceedings of IEEE, Vol. 85, No. 8, pp. 1195-1245.

[2]. Frank B. Gross, (2005). Smart Antennas for Wireless Communications. Tata McGraw-Hill.

[3]. Jeng, S., and H. Lin, (1999). “Smart Antenna System and Its Application in Low-Earth-Orbit Satellite Communication Systems”. IEEE Proceedings on Microwaves, Antennas, and Propagation, Vol. 146, No. 2, pp. 125–130.

[4]. Durgin, G., (2003). Space-Time Wireless Channels. Prentice Hall, New York.

[5]. Doufexi, A., S. Armour, A. Nix, P. Karlsson, and D. Bull, (2004). “Range and Throughput Enhancement of Wireless Local Area Networks Using Smart Sectorised Antennas”. IEEE Transactions on Wireless Communications, Vol. 3, No. 5, pp. 1437–1443.

[6]. Skolnik, M., (2001). Introduction to Radar Systems. McGraw-Hill, 3rd edition, New York.

[7]. Skolnik, M., (2003). “Attributes of the Ubiquitous Phased Array Radar”. IEEE Phased Array Systems and Technology Symposium, Boston.

[8]. Lal, D., T. Joshi, and D. Agrawal, (2004). “Localized Transmission Scheduling for Spatial Multiplexing Using Smart th Antennas in Wireless Adhoc Networks”. 13 IEEE Workshop on Local and Metropolitan Area Networks, pp. 175-180.

[9]. Weisman, C., (2002). The Essential Guide to RF and nd Wireless, 2 Edition, Prentice Hall, New York.

[10]. Ertan, S., H. Griffiths, M. Wicks, et al., (2002). “Bistatic Radar Denial by Spatial Waveform Diversity”. IEEE Radar, pp. 17–21, Oct. 15–17.

[11]. K. Jang, M. K. Khattak, J. Jeon, H. Kim, S. Kahng, (2015). “Handheld Beamforming Antennas Adoptable to 5G Wireless Connectivity”. The 2015 International Workshop on Antenna Technology, pp. 245-246.

[12]. Chanhong Kim, et al., (2014). “On the Hybrid Beamforming with Shared Array Antenna for mmWave MIMO-OFDM Systems”. IEEE WCNC.

[13]. Veerendra, Md.Bakhar and Vani.R.M, (2016). “Robust Blind Beam formers for Smart Antenna System using Window Techniques”. Elsevier Procedia Computer Science, Vol. 93, pp. 713-720.

[14]. Veerendra, Md. Bakhar and Vani. R.M, (2016). “Smart Antennas for Inter ference Rejection in Mobile Communications”. i-manager’s Journal on Mobile Applications and Technologies, Vol. 2, No. 4, pp. 11-18.

[15]. Veerendra, Md. Bakhar and Vani R.M., (2015). “Robust 2D-Novel Smart Antenna Array for MIMO Applications”. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 9, pp. 271-274, doi 10.17148/IJARCCE.2015.4958

[16]. Veerendra, Md. Bakhar and Vani R.M., (2014). “Implementation and Optimization of Modified MUSIC algorithm for High Resolution DOA Estimation”. Proc. in IEEE Int. Microwave and RF Conf., pp. 190-193.

[17]. Wonil Roh, et al., (2014). “Millimeter-Wave Beamforming as an Enabling Technology for 5G Cellular Communications: Theoretical Feasibility and Prototype Results”. IEEE Communications Magazine, Vol. 52, No. 2, pp. 106-113.

[18]. T.M. Slock, (1993). “On the convergence Behavior of the LMS and the Normalized LMS Algorithms”. IEEE Trans. Signal Processing, Vol. 41, pp. 2811–2825.

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Research Paper

Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering

Ruchika Sharma* , Pankaj Mohindru, Pooja Mohindru*

* PG Scholar, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India.

-* Assistant Professor, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India.

Sharma,R., Mohindru,P., and Pooja. (2016). Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering. i-manager’s Journal on Digital Signal Processing, 4(3), 12-20. https://doi.org/10.26634/jdp.4.3.8143

Abstract

The malignant melanoma is a deadly form of the skin cancer in humans. It develops quickly, and effortlessly metastasizes. Late identification of the dangerous melanoma is in charge of 75% of deaths connected with skin growths. Early diagnosis is an important factor that increases chances of successful cure as there is a rapid course of the disease. Computer analysis and image processing are efficient tools supporting quantitative medical diagnosis. Therefore it is relevant to develop computer based methods for dermatological images. So, in order to get the effective results and information about distinctive stages of the infected portion, one needs the corresponding features of that particular area in order to decide the stage. So, the feature extraction phase is hugely dependent on the region detected which has the disease. So appropriate segmentation algorithm is required which can affectivity detect the skin melanoma pixels in the information image. In this work, an algorithm is presented which can adequately detect the pixels having melanoma region and ordinary skin. The proposed work uses a hybrid technique in which space complexity of intensity values is reduced by taking pre-segmentation results from Gaussian mixtures posterior algorithm. The algorithm first chooses some candidates from different regions of the images having distinctive intensity values and then Gaussian models are built from the chosen places by taking their neighborhood pixels. After this, posterior testing is carried out to get pre-segmented results. In the end neural network based training and testing is implemented to get final segmentation results. Experimental results show that the proposed algorithm gives 98% accuracy results on the tested database images.

References

[1]. R. Marks, (2000). “Epidemiology of Melanoma”, Clin Exp Dermatol, Vol. 25, pp. 459-63.

[2]. R.J Pariser, and D.M Pariser, (1987). “Primary Care Physicians Errors in Handling Cutaneous Disorders”. J. Am. Acad Dermatol, Vol. 17, pp. 239-245.

[3]. A. Blum, HLuedtke, U. Ellwanger, et al., (2004). “Digital Image Analysis for Diagnosis of Cutaneous Melanoma: Development of a Highly Effective Computer Algorithm based on Analysis of 837 Melanocytic Lesions”. Br. J. Dermatol, Vol. 151, No. 5, pp. 1029–38.

[4]. F. Nachbar, W. Stolz, T. Merkle, A. B. Cognetta, T. Vogt, M. Landthaler, P. Bilek, O. Braun-Falco, and G. Plewig, (1994). “The ABCD Rule of Dermatoscopy: High Prospective Value in the Diagnosis of Doubtful Melanocytic Skin Lesions”. J. Amer. Acad. Dermatol., Vol. 30, No. 4, pp. 551–559.

[5]. W.V. Stoecker, W.W. Li, and R.H. Moss, (1992). “Automatic Detection of Asymmetry in Skin Tumors”. Computerized Med. Imag. Graph., Vol.16, No.3, pp. 191- 197.

[6]. W. Stolz., A. Riemann, Cognetta, et al., (1994). “ABCD Rule of Dermatoscopy: A New Practical Method for Early Recognition of Malignant Melanoma”. Eur. J. Dermatol, Vol. 4, pp. 521–527.

[7]. Isasi et al., (2011). “Melanomas Non-Invasive Diagnosis Application based on the ABCD Rule and Pattern Recognition Image Processing Algorithms”. Comput. Biol. Med., Vol. 41, No. 9, pp. 742–755.

[8]. Amir Reza Sadri, Maryam Zekri, Niloofar Gheissari and Saeid Sadri, “Impulse Noise cancellation of Medical Images using Wavelet Networks and Median Filters”. J. Med. Signals Sens, Vol. 2, No. 1, pp. 25-37.

[9]. Maryam Sadeghi, David McLean, Harvey Lui, and M. Stella Atkins, (2013). “Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions”. IEEE Transactions on Medical Imaging, Vol. 32, No. 5.

[10]. J. Glaister, R. Amelard, A. Wong and D.A. Clausi, (2013). “MSIM: Multistage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin lesion Analysis”. IEEE Trans. Biomed. Eng., Vol. 60, No. 7, pp.1873 -1883.

[11]. Shubhangi and Nagaraj, (2013). “Human Skin Cancer Recognition and Classification by Unified Skin Texture and Color Features”. IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, p- ISSN: 2278- 8727, Vol. 12, No. 4, pp. 42-49.

[12]. Nilkamal S. Ramteke, and Shweta V. Jain, (2013). “Analysis of Skin Cancer Using Fuzzy and Wavelet Technique–Review & Proposed New Algorithm”. International Journal of Engineering Trends and Technology (IJETT), Vol. 4, No. 6.

[13]. Amelio Alessia, and Clara Pizzuti, (2013). “Skin Lesion Image Segmentation using a Color Genetic Algorithm”. In Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, ACM, pp. 1471-1478.

[14]. A. Mehta, A.S Parihar, and N. Mehta, (2015). “Supervised Classification of Dermoscopic Images using Optimized Fuzzy Clustering based Multi-Layer Feed-forward Neural Network”. Computer, Communication and Control (IC4) International Conference, pp. 1 - 6.

[15]. Jeffrey Glaister, Alexander Wong, and David Clausi, (2014). “Segmentation of Skin Lesion from Digital Images using Joint Statistical Texture Distinctiveness”. IEEE Transactions on Biomedical Engineering, Vol. 61, No. 4.

[16]. Ruchika Sharma, et al., (2016). “Review of Segmentation Technique for Melanoma Detection”. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, No. 7, pp. 18 – 22.

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Research Paper

Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers

P. Vinay Mallik* , G. Hemachandra**

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

Mallik,V.P., and Hemachandra.G. (2016). Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers. i-manager’s Journal on Digital Signal Processing, 4(3), 21-26. https://doi.org/10.26634/jdp.4.3.8144

Abstract

In this generation of Internet of Things (IoT), a lot of image processing algorithms are applied on high resolution displays which are used in mobile devices of various sizes. It becomes vital to design a high speed and low-power image processing algorithm for high speed transmission and processing of data. This paper proposes a progressive design of 2D-DCT and quantization which is one of the abundantly used image processing algorithm and is realized using Dadda and Vedic multipliers which work in real time exceptionally in both parallel and pipelined process for calculating 2D-DCT. The high speed, accuracy and less hardware complexity of the proposed systems outclass with those of other presentday systems. The frequency of proposed system is increased to 185.048 which is 19% when compared to the prevailing systems. The proposed system architecture can be easily modified to compute 2D-IDCT which decompress the coefficients to get the image value.

References

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[5]. Soumik Ghosh, Soujanya Venigalla, and Magdy Bayoumi, (2005). “Design and Implementaion of a 2D-DCT Architecture using Coefficient Distributed Arithmetic”. Proceedings of the IEEE Computer Society Annual Symposium on VLSI, pp.162 - 166, 11-12 May 2005.

[6]. Payman Samadi, Huappeng Wu, and Majid Ahmadi, (2006). “The 2-D quantized DCT with Distributed Arithmetic”. IEEE CCECE/CCGEI, Ottawa, pp. 2049-2052.

[7]. R.E. Atani, M. Baboli, S. Mirzakuchaki, S.E. Atani, and B. Zamanlooy, (2008). “Design and Implementation of a 118 MHz 2D DCT Processor”. IEEE International Symposium on Industrial Electronics, pp. 1076-1081.

[8]. M. Jridi and A. Alfalou, (2010). “A Low-power, Highspeed DCT Architecture for Image Compression: Principle and implementation”. VLSI System on Chip Conference (VLSI-SoC), pp. 304-309.

[9]. H.L.P.A. Madanayake, R.J. Cintra, D. Onen, V.S. Dimitrov, and L.T. Bruton, (2011). “Algebraic Integer based 8×8 2-D DCT Architecture for Digital Video Processing”. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1247-1250.

[10]. V. Dimitrov, K. Wahid, and G. Jullien, (2004). “Multiplication-free 8×8 2D-DCT Architecture using Algebraic Integer Encoding”. IEE Electronics Letters, Vol. 40, No. 20, pp. 1310–1311.

[11]. Yogesh M. Jain, Aviraj R. Jadhav, Harish V. Dixit, Akshay S. Hindole, Jithin R. Vadakoott, and Devendra Bilaye, (2015). “A Novel VLSI Design of DCTQ Processor for th FPGA implementation”. 19 International symposium on VLSI Design and Test (VDAT), IEEE, pp. 1-5.

[12]. P. Vinay Mallik, and G. Hemachandra, (2016). “Design of 1D-DCT Using 8x8 Vedic Multiplier”. International Journal of Engineering Science and Computing, Vol. 6, No. 6, pp. 6345-6348.

[13]. V. Satyakishore, J.E.N. Abhilash and G.N.V. Ratnakishor, (2014). “ FPGA Implementation & Performance Comparision of Various High Speed unsigned Binary Multipliers using VHDL”. International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 4, No. 11, pp. 3936–3940.

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Review Paper

A Critical Review of Various Peak Detection Techniques of ECG Signals

Desh Deepak Gautam* , V. K. Giri**

* Research Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Gautam, D.D., and Giri, V.K. (2016). A Critical Review of Various Peak Detection Techniques of ECG Signals. i-manager’s Journal on Digital Signal Processing, 4(3), 27-36. https://doi.org/10.26634/jdp.4.3.8145

Abstract

ECG is an important signal which is most commonly used for the diagnosing of various heart diseases. The analysis of an ECG signal includes preprocessing and feature extraction. Signal processing of an ECG wave, which includes noise reduction and R-Peak detection of the signal, is one of the most important part for its analysis. The presented paper discusses several techniques of noise reduction and R-Peak detection which were proved effective in last few decades. Efficiency of various methods can be defined in terms of detection error rate. Latest research has shown very effective results with error rate less than 0.3%.

References

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[3]. M. Ibrahim Sezan, (1990). “A Peak Detection Algorithm and its Application to Histogram-Based Image Data Reduction”. Computer Vision, Graphics and Image Processing, Vol. 49, No. 1, pp. 36-51.

[4]. A.M. Bianchi, L. Mainardi, E. Petrucci, and M.G. Signorini, (1993). “Time- Variant Power Spectrum Analysis for the Detection of Transient Episodes in HRV Signal I”. IEEE Trans.Biomed.Eng., Vol. 40, No. 2, pp. 136–144.

[5]. R.S. Anand and V. Kumar, (1995). “Efficient and Reliable th Detection of QRS Segment in ECG Signals”. 14 Conference of the Biomedical Engineering Society of India, Vol. No. 10, pp. 56–57.

[6]. W. Zhang, X. Wang, L. Ge, and Z. Zhang, (2005). “Noise Reduction in ECG Signal Based on Adaptive Wavelet Transform”. Proc. IEEE/EMBS Annual Conference, pp. 2699–2702.

[7]. G. Vijaya, V. Kumar, and H.K. Verma, (2007). “Artificial Neural Network Based Wave Complex Detection in Electrocardiograms”. International Journal of Systems Science, Vol. 28, No. 2, pp. 125-132.

[8]. D. Widjaja, S. Vandeput, J. Taelman, M.A.K.A. Braeken, A. Otte, R. H. Van Den Bergh, and S. Van Huffel, (2010). “Accurate R Peak Detection and Advanced Preprocessing of Normal ECG for Heart Rate Variability Analysis”. Computers in Cardiology, pp. 533-536.

[9]. Jo Lynn Tan, (2010). “Signal Analysis and Classification of Digital Communication Signal using Time- Frequency Analysis Techniques in the Multipath Fading Environment”. 10^th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010). pp. 320–323.

[10]. G. Ranganathan, et al., (2010). “Signal Processing of Heart Rate Variability using Wavelet Transform for Mental Stress Measurement”. Journal of Theoretical and Applied Info.Tech, pp. 124-129.

[11]. P. Sasikala, (2010). “Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform”. International Journal of Advanced Computer Science and Applications, Vol. 1, No. 6, pp. 48–53.

[12]. Y. Li and X. Chen, (2011). “A Robust R-Wave Detection algorithm in ECG Signal”. Proc.Intl.Conf. on Transportation, Mechanical, and Electrical Engineering, pp. 2433–2436.

[13]. H. Rabbani, M.P. Mahjoob, E. Farahabadi, and A. Farahabadi, (2011). “R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding”. J.Med. Signals Sens, Vol. 1, No. 2, pp. 91–98.

[14]. Z.S. Wang and J.D.Z. Chen, (2011). “Robust ECG R-R Wave Peak Detection Using Evolutionary-Programmingbased Fuzzy Inference System (EPFIS) and It % -Application to Assessing Brain-Gut Interaction”. Advances in Medical Signal and Information Processing, pp. 265–274.

[15]. H. Kew and D. Jeong, (2011). “Variable Threshold Method for ECG R-peak Detection”. J.Med.Syst., Vol. 35, No. 5, pp. 1085–1094.

[16]. D. Sadhukhan and M. Mitra, (2012). “R-peak Detection Algorithm for ECG using Double Difference and RR Interval Processing”. Procedia Technol., Vol. 4, pp. 873–877.

[17]. F. Scholkmann, J. Boss, and M. Wolf, (2012). “An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals”. Algorithms, Vol. 5, No. 4, pp. 588–603.

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i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 4 Issue 3 July - September 2016

Design of an Adaptive Noise Cancellation System in a Time Varying, Non-Linear Automobile Environment

S. Thilagam* , P. Karthigaikumar**

* Assistant Professor, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India. ** Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India.

Thilagam.S., and Karthigaikumar.P. (2016). Design of An Adaptive Noise Cancellation System in A Time Varying, Non-Linear Automobile Environment. i-manager’s Journal on Digital Signal Processing, 4(3), 1-5. https://doi.org/10.26634/jdp.4.3.8141

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Smart Antennas for Next Generation Mobile Communication

Veerendra Dakulagi* , MD. Bakhar**

Veerendra., and MD. Bakhar. (2016). Smart Antennas for Next Generation Mobile Communication. i-manager’s Journal on Digital Signal Processing, 4(3), 6-11. https://doi.org/10.26634/jdp.4.3.8142

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Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering

Ruchika Sharma* , Pankaj Mohindru**, Pooja Mohindru***

* PG Scholar, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India. **-*** Assistant Professor, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India.

Sharma,R., Mohindru,P., and Pooja. (2016). Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering. i-manager’s Journal on Digital Signal Processing, 4(3), 12-20. https://doi.org/10.26634/jdp.4.3.8143

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Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers

P. Vinay Mallik* , G. Hemachandra**

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India. ** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

Mallik,V.P., and Hemachandra.G. (2016). Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers. i-manager’s Journal on Digital Signal Processing, 4(3), 21-26. https://doi.org/10.26634/jdp.4.3.8144

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A Critical Review of Various Peak Detection Techniques of ECG Signals

Desh Deepak Gautam* , V. K. Giri**

* Research Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India. ** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Gautam, D.D., and Giri, V.K. (2016). A Critical Review of Various Peak Detection Techniques of ECG Signals. i-manager’s Journal on Digital Signal Processing, 4(3), 27-36. https://doi.org/10.26634/jdp.4.3.8145

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* Assistant Professor, Department of Electronics and Communication Engineering, Kumaraguru College of Technology, Coimbatore, India.

** Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India.

Ruchika Sharma* , Pankaj Mohindru, Pooja Mohindru*

* PG Scholar, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India.

-* Assistant Professor, Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India.

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

* Research Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.