i-manager Publications

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

Current Issue Vol. 11 Issue 2

Most Cited

Volume 2 Issue 1 January - March 2014

Research Paper

Evaluation method for measuring the statistical parameters of existing heartbeat classification method of ECG signal

Mansi Varshney* , Chinmay Chandrakar, Monisha Sharma*

* Assistant Professor, Department of Electronics, & Telecommunication, SSCET, Bhilai, India.

Senior Associate Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India.

* Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India.

Varshney,M., Chandrakar,C., and Sharma,M. (2014). Evaluation Method for Measuring Statistical Parameters of Existing Heartbeat Classification Method of ECG Signal. i-manager's Journal on Digital Signal Processing, 2(1),1-6. https://doi.org/10.26634/jdp.2.1.2718

Abstract

Diagnosis of diseases using ECG signal required, classification of beat, which is the main criteria for arrhythmia detection. But accuracy of the detected beat plays an important role in the diagnosis of the disease using ECG signal. This paper describes an evaluation method which is used to analyze the ECG signal and help in diagnosis of cardiac arrhythmia diseases. The evaluation method is used in classification of the beats of ECG signal. Generally there are two types of beats present in human heartbeat – normal beats (NORM) and abnormal beats (LBBB, RBBB, APC, and VPC). For diagnosis, these parameters are first determined. These values can be classified with the help of any existing methodology. This paper aims to check the accuracy of the beat (normal and abnormal beat). Statistical parameters are used for calculating accuracy. Statistical parameters are nothing but the parameter which is used to check the accuracy of the detected beat. The proposed method in this paper evaluates statistical parameters namely Sensitivity (Se), Specificity (Sp), Positive Predictive Values (PPV,) and Negative Predictive Values (NPV) and compares it with the ideal values. The proposed method can be used to detect the accuracy of any existing heartbeat classification methods.

References

[1]. R Ghongade, A.A. Ghatol Vishwakarma (2007). "A brief performance evaluation of ECG feature extraction techniques for artificial neural network based classification". Institute Of Information Technology, Pune 12/2007; OI:10.1109/TENCON. 429096, IEEE Region 10 Conference Source: IEEE Xplore.

[2]. "Detection of Arrhythmia from ECG Signals by a Robust Approach to Outliers", Umut ORHAN Cukurova University.

[3]. Philip de Chazal,, Maria O'Dwyer, and Richard B. Reilly, (2004). "Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features" Transactions on Biomedical Engineering, Vol. 51, No. 7.

[4]. N. Belgacem, M.A Chikh, F. Bereksi Reguig, (1997). "Supervised Classification of Ecg Using Neural Networks", Biomedical Engineering Laboratory, Department of Electronics, Science Engineering Faculty, ITransactions on Biomedical Engineering, Vol. 44, No. 9.

[5]. Yu Hen Hu, urekha Palreddy, and Willis J. Tompkins, "A Patient-Adaptable ECG Beat Classifier Using a Mixture of Experts Approach", IEEE.

[6]. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P.C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, (2000). "Components of a new research resource for complex physiologic signals". PhysioBank, PhysioToolkit, and PhysioNet, Circulation, 101 (23) e215–e220.

[7]. Qibin Zhao, and Liqing ZhanI (2005). "ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines," International Conference on Neural Networks and Brain, CNN&B '05, Vol. 2, pp. 1089- 1092.

[8]. S. Z. Mahmoodabadi, A. Ahmadian, and M. D. Abolhasani, (2005). "ECG Feature Extraction using Daubechies Wavelets," Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, pp. 343-348.

[9]. P. Tadejko, and W. Rakowski, (2007). "Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification," 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM '07, pp. 322-327.

[10]. Xiaomin Xu, and Ying Liu, (2004). "ECG QRS Complex Detection Using Slope Vector Waveform (SVW) Algorithm," Proceedings of the 26th Annual International Conference of the IEEE EMBS, pp. 3597-3600.

[11]. M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, (2005). "Support vector machine based arrhythmia classification using reduced features," Int. J Control, Autom., Syst., Vol. 3, No. 4, pp. 571–579.

[12]. I. Christov, G. G´omez-Herrero, V. Krasteva, I. Jekova, A. Gotchev, and K. Egiazarian, (2006). "Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification," Med. Eng. Phys., Vol. 28, No. 9, pp. 876–887.

[13]. H. H. Haseena, A.T. Mathew, and J. K. Paul, (2009). "Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia.

Full Article (HTML)

Pdf

Research Paper

Comparative Study of Lung ROI Segmentation with Different Morphological Structuring Element Sizes in Noise Environment

V. Kalpana* , S. Varadarajan**

* Assistant Professor, Department of Electronics and Instrumentation Engineering, SVEC, Tirupathi, India.

** Professor, Department of Electronics and Communication Engineering, SVU, Tirupathi, India.

Kalpana.V., and Varadarajan.S. (2014), Comparative Study of Lung ROI Segmentation With Different Morphological Structuring Element Sizes in Noise Environment. i-manager’s Journal on Digital Signal Processing, 2(1), 7-13. https://doi.org/10.26634/jdp.2.1.2719

Abstract

Lung cancer is the most challenging problem chasing the scientific and medical fields in several countries around the world. Several medical modalities are specialised to diagnosis the extinct of disease via PET, MRI, CT, US and DICOM. Automated Computer Aided Diagnosing (CAD) system has shown its improvements in the detection of lung cancer using advanced radiology. CAD system merges geometric and intensity models to improve particular area of anatomical structure. Nodules are comparatively smaller than 3-4 cm in diameter and are common abnormalities that are adjacent to vessels or chest wall. Detection of non-spherical shaped nodules is a primary difficulty. Sixty percent of all nodules are not harmful but malignant nodules may be lung cancer tumours. To identify these nodules the methods like intensity thresholding or model based might fail. In this paper the nodules are extracted from the DICOM lung image in the noisy environment such as Gaussian, salt and pepper, Poisson and speckle using morphology and watershed algorithm. The nodules are extracted from the lung portion using different edge detection operators such as Gaussian, Laplacian, prewitt, LOG, Unsharp, average and Sobel in presence of noise and various sizes of the structuring element. These results support to examine and calculate the influence of noises on the DICOM images in extracting the nodules.

References

[1]. Zhao Yu qian, Gui Wei-hua, (2005). ”Medical Images edge detection Based on Mathematical Morphology” Proceedings of 2005 IEEE Engineering in Medicine and th Biology 27 Annual Conference.

[2]. H. Chidiac, D. Ziou, (1999). “Classification of image Edges”, Vision interface'99, Troise-Rivieres, Canada, pp. 17-24.

[3]. M. Heath, S. Sarker, T. Sanocki and K. Bowyer, (1996). “Comparison of Edge Detectors: A Methodology and Initial Study”, Proceedings of CVPR'96 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 143-148.

[4]. Speeuwers, L. j. Heijden, F. Vander, (1992). “Evaluation of Edge Detectors using Average Risk”, Robust Computer Vision (Eds: Forstner/Ruwiedel), Wichmann, Karlsruhe:.

[5]. Tuba Sirin, Mehmet Izzet Saglam et al. “A Comparative Evaluation of Competitive Learning Algorithms for Edge detection Enhancement”,

[6]. Shin. M. C, Goldgof. D. B, Bowyer.K.W, Nikiforou.S, (2001). ”Comparision of edge detection algorithms using structure from motion task” Systems, Man and Cybernetics, Part B, IEEE Transactions, Vol 31,Issue 4, pp.589-601.

[7]. E. Clavier, S. Clavier, J. Labiche, (1999). “Image Sorting-Image Classification: A Global Approach”, icdar '99, Proceedings of the Fifth International IEEE Conference on Document Analysis and Recognition, pp. 123-126.

[8]. RamanMainiand J. S. Sohal, (2006). ”Performance Evaluation of Prewitt Edge Detector for Noisy Images”, GVIP Journal, Vol-6, Issue-3.

[9]. Mohsen Sharifi, Mahmoud Fathy, (2002). “A Classified and Comparitive Study of Edge detection Algorithms”, Proceedings of International Conference on Information Technology: Coding and Computing (ITCC'02) IEEE.

[10]. Mohamed Roushdy, (2006). ”Comparitive Study of Edge detection Algorithms Applying on the Gray Sacle Noisy Image using Morphological Filter”, GVIP Journal, Vol- 6, Issue-4.

[11]. R. M. Haralick, L. g. Slhapiro, (1992). “Computer and Robot Vision”, Vision 1, Addition- Wesley Publishing Company Inc.

[12]. Albovik, (2000). ”Handbook of Image and video Processing”, Academic Press.

[13]. P. Garcia, F. Pla, I. Garcia, (1999). “Detecting Edges in colour Images using dichromatic Differences”, Seventh International IEEE Conference on Image processing and ITS Applications, Vol. 1, pp.363-367.

[14]. K. Timmermann and R. Novak, (1999). ”Multiscale modelling and estimation of Poisson processes with applications to photon-limited imaging”, IEEE, Trans Information Theory., Vol.45, No.3, pp.846-852.

[15]. S. G. Chang, Y. Binand, M.Vetterli, (2006). ”Adaptive wavelet thresholding for image denoising and compression”, IEEE Trans. On Image Processing, Vol.9, No.9, pp. 1532-1546.

[16]. L. Gagnon and F. D. Smaili, (1996). ”Speckle Noise Reduction of Airborne SAR Images with Symmetric Daubechies Wavelets”, SPIE Proc. #2759, pp. 1424.

[17]. Cronin, P. et al., (2008). Solitary Pulmonary Nodules: Meta-analytic Comparison of Cross-sectional Imaging Modalities for Diagnosis of Malignancy. Radiology. 246:772-782.

[18]. H. E Durdick, (1996). Digital imaging: theory and applications, McGraw-hill.

[19]. Z. Wang and D. Hang, (1999). ” Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images”, IEEE Trans. on Circuits and Systems –II: Analog and Digital Signal Processing, Vol. 46. No. 1, pp.78- 80.

Full Article (HTML)

Pdf

Research Paper

Power Efficient Implementation of Least Mean Square Algorithm Based FIR Filter Design Using FPGA

M. Devipriya* , V.Saravanan, N. Santhiyakumari*

* M.E Student, Knowledge Institute of Technology, Anna University, Salem, Tamilnadu, India.

PhD Schlor, Knowledge Institute of Technology, Anna University, Salem, Tamilnadu, India.

* Professor & Head, Department of ECE, Knowledge Institute of Technology, Anna University, Salem, Tamilnadu, India.

Devipriya.M.,Saravanan.V., and Santhiyakumari.N. (2014). Power Efficient Implementation Of Least Mean Square Algorithm Based FIR Filter Design Using FPGA. i-manager’s Journal on Digital Signal Processing, 2(1), 14-21. https://doi.org/10.26634/jdp.2.1.2720

Abstract

This paper describes a high-speed and low-complexity implementation of FIR filter using least mean square technique. The proposed structure of multiplexed based zero-adaptation-delay structure and two adaptation delay structure for a direct LMS adaptive FIR filter. This paper describes the proposed adder technique provides much faster convergence and lower complexity for obtaining lower area, power dissipation, high speed and lower propagation delay. The multiplexer circuits were schematized using the DSCH2 schematic design tool, and their layouts were generated with the Micro wind 2 VLSI layout CAD tool. The parameter analyses were performed with a BSIM4 analyzer. The proposed multiplex -based filters are use carry save adder as well as other existing adder circuit in terms of power dissipation, propagation delay, latency, and throughput. Our proposed structure involves the minimum power. Finally the simulations are done using Xilinx ISE design suite to get power and implemented on Spartan 3E FPGA kit.

References

[1]. S. Y. Park and P. K. Meher (2013) “Low-power, highthroughput, and low-area adaptive FIR filter based on distributed arithmetic,” IEEE Trans. Circuits Syst. II, Exp. Briefs, Vol. 60, No. 6, pp. 346–350.

[2]. Pramod Kumar Meher and Sang Yoon Park (2013) “Critical-Path Analysis and Low-Complexity implementation of the LMS Adaptive Algorithm” in Proc. IEEE transactions on circuits and systems.

[3]. P.K.Meher and S.Y.Park (2011), “Low adaptation-delay LMS adaptive filter Part-II: An optimized architecture,” in Proc. IEEE Int. Midwest Symp. Circuits Syst.

[4]. P. K. Meher and M. Maheshwari (2011), “A high-speed FIR adaptive filter architecture using a modified delayed LMS algorithm,” in Proc. IEEE Int. Symp. Circuits Syst., pp. 121–124.

[5]. Xilinx (2009), ”Spartan-3E FPGA Family Complete Data Sheet”, Xilinx, Inc.

[6]. E. Mahfuz, C. Wang, and M. O. Ahmad (2005), “A highthroughput DLMS adaptive algorithm,” in Proc. IEEE Int. Symp. Circuits Syst., pp. 3753–3756.

[7]. Y.Yi,R.Woods, L.K.Ting, and C.F.N.Cowan (2005), “High speed FPGA-based implementations of delayed-LMS filters,” Trans.Very Large Scale Integr. (VLSI) Signal Process., Vol. 39, No. 1–2, pp. 113–131.

[8]. L.-K.Ting, R.Woods, and C.F.N.Cowan (2005), “Virtex FPGA implementation of a pipelined adaptive LMS predictor for electronic support mea-sures receivers” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., Vol. 13, No. 1, pp. 86–99.

[9]. S. Haykin and B. Widrow (2003), “Least-Mean-Square Adaptive Filters”, Hoboken, NJ, USA: Wiley-Inter science.

[10]. L.D.Van and W. S. Feng (2001), “An efficient systolic architecture for the DLMS adaptive filter and its applications” IEEE Trans. Circuits Syst.II, Analog Digit. Signal Process., Vol. 48, No. 4, pp. 359–366.

[11]. M. D. Meyer and D. P. Agrawal (1990), “A modular pipelined implementation of a delayed LMS transversal adaptive filter,” in Proc. IEEE Int. Symp. Circuits Syst., pp. 1943–1946.

[12]. G.Long, F.Ling, and J.G.Proakis (1989), “The LMS algorithm with delayed coefficient adaptation” IEEE Trans. Acoust., Speech, Signal Process., Vol. 37, No. 9, pp. 1397–1405.

[13]. B. Widrow and S. D. Stearns (1985), Adaptive Signal Processing. Engle-wood Cliffs, NJ,USA: Prentice-Hall.

Full Article (HTML)

Pdf

Research Paper

Digitally Controlled Delay lines Using Strobe Controlled Logic

N. Manimekalai* , A. Lelina Devi**

* II-ME VLSI Design, Department of ECE, Knowledge Institute of Technology, Salem, India.

** Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, India.

Manimekalai.N., and Devi,L.A. (2014). Digitally Controlled Delay Lines Using Strobe Controlled Logic. i-manager’s Journal on Digital Signal Processing, 2(1), 22-25. https://doi.org/10.26634/jdp.2.1.2721

Abstract

In recent years, Digitally Controlled Delay-Lines (DCDL) is a key block in number of applications and play the role of DAC in traditional circuits. This paper presents a totally glitch free DCDL which overcame the limitation of a NAND based DCDL using strobe control method. Using this logic a clock is presented, that reduces the output jitter when compared to the existing method. The existing method uses a delay control code and reduces the delay of about 40%, but it consumes more power and less area efficient. By using strobe controlled logic, the peak to peak absolute output jitter of 70-80% were reduced. As an example application, All-digital spread-spectrum clock generator (SSCG), All-digital phase-locked loops (ADPLL), Phase-locked loop (PLL) were used.

References

[1]. C. C. Chung and C. Y. Lee, (2003). "An all-digital phaselocked loop for high speed clock generation," IEEE J. Solid- State Circuits, Vol. 38, No. 2, pp. 347–351.

[2]. B. M. Moon, Y. J. Park, and D. K. Jeong, (2003). "Monotonic wide-range digitally controlled oscillator compensated for supply voltage variation," IEEE Trans. Circuits Syst. II, Exp. Briefs, Vol. 55, No. 10, pp. 1036–1040.

[3]. T. M. Matano, Y. Takai, T. Takahashi, Y. Sakito, I. Fujii, Y. Takaishi, H. Fujisawa, S. Kubouchi, S. Narui , K.Arai,M.Morino, M.Nakamura, S. Miyatake, T. Sekiguchi, and K. Koyama, (2003). "A 1-Gb/s/pin 512-Mb DDRII SDRAM using a digital DLL and a slew-rate-controlled output buffer," IEEE J. Solid-State Circuits, Vol. 38, No. 5, pp. 762–768.

[4]. R. J. Yang and S. I. Liu, (2007). "A 40–550 MHz harmonic-free all digital delay locked loop using a variable SAR algorithm," IEEE J. Solid-State Circuits, Vol. 42, No. 2, pp. 361–373.

[5]. L.Wang, L. Liu, and H. Chen, (2010). "An implementation of fast-locking and wide-range 11-bit reversible SAR DLL," IEEE Trans. Circuits Syst. II, Exp. Briefs, Vol. 57, No. 6, pp. 421–425.

[6]. J. S. Wang, C. Y. Cheng, J. C. Liu, Y. C. Liu, and Y. M. Wang, (2010). "A duty cycle distortion tolerant half delay line low-power fast lock in all digital delay locked loop," IEEE J. Solid-State Circuits, Vol. 45, No. 5, pp. 1036–1047.

[7]. S. Damphousse, K. Ouici, A. Rizki, and M. Mallinson, (2007). "All digital spread spectrum clock generator for EMI reduction," IEEE J. Solid- State Circuits, Vol. 42, No. 1, pp. 145–150.

[8]. Rashed Zafar Bhatti. Data Strobe Timing of DDR2 using a Statistical Random Sampling Technique "EE- Systems Department, University of Southern California, Marina del Rey".

Full Article (HTML)

Pdf

Research Paper

Two bit Soften Hard Decision Fusion for Cooperative Spectrum Sensing inCognitive Radio Networks

J.Abinaya* , K.Yalini, S.Shanmugavel*

* -*** Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, India.

Abinaya,J.,Yalini.K.,, and Shanmugavel.S. (2014). Two Bit Soften Hard Decision Fusion for Cooperative Spectrum Sensing In Cognitive Radio Networks. i-manager’s Journal on Digital Signal Processing, 2(1), 26-32. https://doi.org/10.26634/jdp.2.1.2722

Abstract

This paper presents a comprehensive analysis of spectrum sensing (SS) in cognitive radio (CR) network. The main function of cognitive radio is detecting unused spectrum and sharing it without harmful interference to the licensed user. Cooperative spectrum sensing refers to incorporation of multiple secondary users (SU) to analyze the primary user (PU) signal in order to achieve more accurate detection performance. In order to solve the sensing failure problem in conventional two threshold energy detection method soften hard decision fusion mechanism is used exploiting all the sensing results. Consideration of the confused region which is the intersection of the energy distribution of PU signal and noise. SUs could not distinguish between the absence or presence of the primary user in the confused region. So the fusion center makes a soft combination of the observations. The objective of this paper is to produce an efficient spectrum sensing algorithms using two thresholds with soften hard decision fusion mechanism.

References

[1]. Danijela Cabric, Shridhar Mubaraq Mishra, Robert W. Brodersen, (2004). "Implementation Issues in Spectrum Sensing for Cognitive Radios", IEEE Proc. of Asilomar Conf. on Signals, Systems, and Computers, pp. 772–776

[2]. Simon Haykin, David J. Thomson and Jeffrey H. Reed, (2009). "Spectrum Sensing for Cognitive Radio", IEEE.

[3]. Sina Maleki and Geert Leus, (2013). "Censored Truncated Sequential Spectrum Sensing for Cognitive Radio Networks", Journal on selected areas in communications, IEEE, Vol.31 pp. 364-377

[4]. Jun Ma and Ye (Geoffrey) Li, (2007). "Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks", IEEE GLOBECOM, pp. 3139-3143.

[5]. Ashish Bagwari & Geetam Singh Tomar, (2013). "Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks", International Journal of Electronics Letters, Vol. 1, pp. 24-32

[6]. Yan Xin and Honghai Zhang, (2009). "A Simple Sequential Spectrum Sensing Scheme for Cognitive Radio", IEEE Transactions on Signal Processing.

[7]. Rahul Tandra and Anant Sahai, (2008). "SNR Walls for Signal Detection", IEEE Journal of Selected Topics in Signal Processing, Vol. 2 pp.4-17.

[8]. I.F Akyildiz, W.Y. Lee, K.R. Chowdhury, (2009). "CRAHNs: cognitive radio ad hoc networks," Ad Hoc Networks 7 (5), pp. 810-836.

[9]. Yulong Zou and Yu-Dong Yao Baoyu Zheng, (2010). "Spectrum Sensing and Data Transmission Tradeoff in Cognitive Radio Networks", IEEE.

[10]. Tevfik Yucek and Huseyin Arslan, (2009). "A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications", IEEE Communications Surveys & Tutorials, Vol. 2, pp. 116-130

[11]. Urkowitz, H, (1967). " Energy detection of unknown deterministic", Signals, Proceedings of IEEE, pp.523-531.

[12]. Shishir Kumar Srivastava, Adrish Banerjee, (2009). "Inratio" Logic Based Cooperative Spectrum Sensing using Double threshold Energy Detection", Proceedings of 4th International Conference on Crowncom.

Full Article (HTML)

Pdf

Research Paper

FPGA Based Convolution Encoder Time Domain and State machine design Using VHDL

S P Kurlekar*

Assistant Professor, Department of Electronics &Tele Communications Engineering, (SITCOE, Yadrav) Maharashtra, India.

Kurlekar.S.P. (2014), FPGA Based Convolution Encoder Time Domain And State Machine Design Using VHDL. i-manager’s Journal on Digital Signal Processing, 2(1), 33-36. https://doi.org/10.26634/jdp.2.1.2723

Abstract

When the errors introduced by the information channel are unacceptable then the channel coding is needed. The use of channel coders with source coders provides the efficient and reliable transmission in the presence of noise. The use of channel coders with source coders provides the efficient and reliable transmission in the presence of noise. Coding permits an increased rate of information transfer at a fixed error rate, or a reduced error rate for a fixed transfer rate. A convolution coder accepts a fixed number of message symbols and produces a fixed number of code symbols, but its computations depends not only on the current set of input symbols but also on some of previous input symbols.[1] Through this paper we have illustrated design of convolution encoder using time domain approach with VHDL platform. The target technology is used as Sparten FPGA device.

References

[1]. Viterbi.A.J, (1971). "Convolution codes and their performance in communication systems," IEEE Transaction on Communications, Vol.com-19, pp. 751 to 771.

[2] Wong, Y., Jian, W., HuiChong, O., Kyun, C., Noordi, N. (2009). “Implementation of Convolutional Encoder and Viterbi Decoder using VHDL”, Proceedings of IEEE International conference on Research and Development Malaysia.

[3] Serkan Sozen “A Viterbi Decoder Using System C for Area Efficient VLSI Implementation”

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

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 2 Issue 1 January - March 2014

Evaluation method for measuring the statistical parameters of existing heartbeat classification method of ECG signal

Mansi Varshney* , Chinmay Chandrakar**, Monisha Sharma***

* Assistant Professor, Department of Electronics, & Telecommunication, SSCET, Bhilai, India. ** Senior Associate Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India. *** Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India.

Varshney,M., Chandrakar,C., and Sharma,M. (2014). Evaluation Method for Measuring Statistical Parameters of Existing Heartbeat Classification Method of ECG Signal. i-manager's Journal on Digital Signal Processing, 2(1),1-6. https://doi.org/10.26634/jdp.2.1.2718

Abstract

References

Full Article (HTML)

Pdf

Comparative Study of Lung ROI Segmentation with Different Morphological Structuring Element Sizes in Noise Environment

V. Kalpana* , S. Varadarajan**

* Assistant Professor, Department of Electronics and Instrumentation Engineering, SVEC, Tirupathi, India. ** Professor, Department of Electronics and Communication Engineering, SVU, Tirupathi, India.

Kalpana.V., and Varadarajan.S. (2014), Comparative Study of Lung ROI Segmentation With Different Morphological Structuring Element Sizes in Noise Environment. i-manager’s Journal on Digital Signal Processing, 2(1), 7-13. https://doi.org/10.26634/jdp.2.1.2719

Abstract

References

Full Article (HTML)

Pdf

Power Efficient Implementation of Least Mean Square Algorithm Based FIR Filter Design Using FPGA

M. Devipriya* , V.Saravanan**, N. Santhiyakumari***

Devipriya.M.,Saravanan.V., and Santhiyakumari.N. (2014). Power Efficient Implementation Of Least Mean Square Algorithm Based FIR Filter Design Using FPGA. i-manager’s Journal on Digital Signal Processing, 2(1), 14-21. https://doi.org/10.26634/jdp.2.1.2720

Abstract

References

Full Article (HTML)

Pdf

Digitally Controlled Delay lines Using Strobe Controlled Logic

N. Manimekalai* , A. Lelina Devi**

* II-ME VLSI Design, Department of ECE, Knowledge Institute of Technology, Salem, India. ** Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, India.

Manimekalai.N., and Devi,L.A. (2014). Digitally Controlled Delay Lines Using Strobe Controlled Logic. i-manager’s Journal on Digital Signal Processing, 2(1), 22-25. https://doi.org/10.26634/jdp.2.1.2721

Abstract

References

Full Article (HTML)

Pdf

Two bit Soften Hard Decision Fusion for Cooperative Spectrum Sensing inCognitive Radio Networks

J.Abinaya* , K.Yalini**, S.Shanmugavel***

* -*** Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, India.

Abinaya,J.,Yalini.K.,, and Shanmugavel.S. (2014). Two Bit Soften Hard Decision Fusion for Cooperative Spectrum Sensing In Cognitive Radio Networks. i-manager’s Journal on Digital Signal Processing, 2(1), 26-32. https://doi.org/10.26634/jdp.2.1.2722

Abstract

References

Full Article (HTML)

Pdf

FPGA Based Convolution Encoder Time Domain and State machine design Using VHDL

S P Kurlekar*

Assistant Professor, Department of Electronics &Tele Communications Engineering, (SITCOE, Yadrav) Maharashtra, India.

Kurlekar.S.P. (2014), FPGA Based Convolution Encoder Time Domain And State Machine Design Using VHDL. i-manager’s Journal on Digital Signal Processing, 2(1), 33-36. https://doi.org/10.26634/jdp.2.1.2723

Abstract

References

Full Article (HTML)

Pdf

Mansi Varshney* , Chinmay Chandrakar, Monisha Sharma*

* Assistant Professor, Department of Electronics, & Telecommunication, SSCET, Bhilai, India.

Senior Associate Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India.

* Professor, Department Of Electronics, & Telecommunication, SSCET, Bhilai, India.

* Assistant Professor, Department of Electronics and Instrumentation Engineering, SVEC, Tirupathi, India.

** Professor, Department of Electronics and Communication Engineering, SVU, Tirupathi, India.

M. Devipriya* , V.Saravanan, N. Santhiyakumari*

* II-ME VLSI Design, Department of ECE, Knowledge Institute of Technology, Salem, India.

** Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, India.

J.Abinaya* , K.Yalini, S.Shanmugavel*