iosr-JVSP   Volume-7 ~ Issue-2 ~ March-April 2017

ABSTRACT: In this paper we proposed a technique to remove eye blink artifact from electroencephalogram (EEG) using lifting wavelet transform (LWT). The LWT has been successfully used in eye blink artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The LWT is applied to decompose EEG signal into a finite set of subbands. The energy based subband filtering is implemented to separate the lower frequency noise components to clean the EEG signal. The energies of individual subbands respectively for EEG and fGn that of contaminated EEG are compared to derive the energy based threshold for the suppression............

Keywords: Electroencephalography, artifact reduction, stationary subspace analysis, lifting wavelet transform

[1] S. V. Ramanan, N. V. Kalpakam, J. S. Sahambi, "A novel wavelet based technique for detection and de-noising of ocular artifact in
normal and epileptic electroen-cephalogram", ICCCAS, pp. 180–183,27-29 June 2004.
[2] C. Joyce, I. Gorodnitsky, M. Kutas, "Automatic removal of eye movement and blink artifacts from EEG data using blind
component separation", Psychophysi-ology 41, 313–325, 2004.
[3] V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, K. Ramadoss, "Auto-matic identification and removal of ocular
artifacts from EEG using wavelet transform", Meas. Sci. Rev. 6 (4) ,45–57, 2006.
[4] Z. Wang, P. Xu, T. Liu, Y. Tian, X. Lei, D. Yao, "Robust removal of ocular arti-facts by combining independent component
analysis and system identification", Biomed. Signal Process. Control 10, 250–259, March, 2014.
[5] M. Garcia, M. Thomlinson, J. Lopez, B. Jervis, C. Mair, "Residual ocular arte-fact subsequent to ocular artefact removal from the
electroencephalogram", IEEProc. – Sci. Meas. Technol. 146, 293–298, November, 1999.

ABSTRACT: In complementary metal oxide semiconductor (CMOS) the power dissipation predominantly comprises of dynamic as well as static power. Prior to introduction of "Deep submicron technologies" it is observed that in case of technology process with feature size larger than 1micro meter, the consumption of dynamic power out of the overall power consumption of any circuit is more than 90%,while that of static power is negligible. But in the present deep submicron technologies in order to, reduce the dynamic power consumption in VLSI circuits, the power supply is being scaled down, keeping in view the principle that the dynamic power dissipated is directly proportional to the square of the supply voltage (Vdd).The threshold voltage also needs to be reduced since the supply voltage is scaled down...........

Keywords: Deep submicron, Low power, Sub-threshold leakage current, Power Gating, Threshold voltage , Transistor stacking.

[1]. H. J. M Veendrick, "Short circuit dissipation of static CMOS circuitry and its impact on the design of buffer circuits ," IEEE J. Solid-State Circuits, vol. SC-19, pp. 468–473, Aug. 1984.
[2]. R. X. Gu and M. I. Elmasry, "Power dissipation analysis and op-timization for deep submicronCMOSdigital circuits," IEEE J. Solid-State Circuits, vol. 31, pp. 707–713, May 1999.
[3]. Jun Cheol Park and Vincent J. Mooney III, Senior Member, IEEE Sleepy Stack Leakage Reduction," IEEE Transactions On Very Large Scale Integration (VLSI) Systems, vol. 14, no. 11, november 2006.
[4]. N. Hanchate and N.Ranganathan, "LECTOR: A Technique for Leakage Reduction in CMOS Circuits", IEEE Transactions on VLSI Systems, vol. 12, pp. 196-205, Feb., 2004
[5]. M. C. Johnson, D. Somasekhar, L. Y. Chiou, and K. Roy, "Leakage control with efficient use of transistor stacks in single threshold CMOS," IEEE Trans. VLSI Syst., vol. 10, pp. 1-5,Feb. 2002..

ABSTRACT: In this paper real time system identification is implemented on TMS320C6713 for speech signal using standard IEEE sentence (SP23) of NOIZEUS database with different types of real world noises at different level SNR taken from AURORA data base. The performance is measured in terms of signal to noise ratio (SNR) improvement. The implementation is done with "C' program for least mean square (LMS) and recursive least square (RLS) algorithms and processed using DSP processor with Code composer studio

Keywords: System Identification, LMS, RLS, SNR, TMS3206713

[1]. Simon Haykin " Adaptive Filter Theory" Fourth edition pearson education
[2]. V.Udayashankara " Modern digital signal processing" second edition PHI
[3]. Gaurav Saxena, SubramaniamGanesan, and Manohar Das "Real time implementation of adaptive noise cancellation" 978-1-4244-2030-8/08,2008 IEEE. PP431-436
[4]. Texas Instruments Tutorial, TMS320C6713 Hardware Designers Resource Guide",(July 2004), SPRAA33.
[5]. D. Reay, "Digital Signal Processing and Applications with the TMS320C6713 and TMS320C6416 DSK," John Wiley and Sons, Inc,Edition- 2nd 2008..

ABSTRACT: Activation functions are used to transform the mixed inputs into their corresponding output counterparts. Commonly, activation functions are used as transfer functions in engineering and research. Artificial neural networks (ANN) are the preferred choice for most studies and comparisons of activation functions. The Sigmoid Activation Function is the most common and its popularity arise from the fact that it is easy to derive, its boundedness within the unit interval, and it has mathematical properties that work well with the approximation theory. On the other hand, not so common is the Fibonacci Activation Function with similar and perhaps better features than.............

Keywords: Fibonacci Activation Function, Sigmoid Activation Function, Independent Component Analysis, Natural Gradient Algorithm.

[1]. B. Karlik and V. A. Olgac, "Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks," International Journal of Artificial Intelligence And Expert Systems (IJAE), vol. 1, no. 4, pp. 111-122, 2011.
[2]. B. DasGupta and G. Schnitger, "The Power of Approximating: A Comparison of Activation Functions," Advances in Neural Information Processing Systems, vol. 5, pp. 615-622, 1993.
[3]. N. Suttisinthong, B. Seewirote and A. Ngaopitakkul, "Selection of Proper Activation Functions in Back-propagation Neural Network algorithm for Single-Circuit Transmission Line," in Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS , Hong Kong, 2014.
[4]. L. Lei, W. Yu and W. Xing-Hui, "Natural gradient algorithm based on a class of activation functions and its applications in BSS," in Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 2006.
[5]. J. P. Chibole, "Blind separation of two human speech signals using natural gradient algorithm by employing the assumptions of independent component analysis," in Proceedings of the 2014 International Annual Conference on Sustainable Research and Innovation, Nairobi, 2014.

ABSTRACT: This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant...........

Keywords: Equalization, intersymbol interference, multilayer perceptron, back propagation, variable learning rate, bit error rate (BER)

[1]. Proakis J. G. Digital communications, 3rd Edition, McGraw-Hill, Inc. 1995
[2]. Paul J. R, Vladimirova T. "Blind equalization with recurrent neural networks using natural gradient", ISCCSP symposium, March 2008, pp 178-183
[3]. Haykin S., Adaptive Filter Theory, Prentice Hall, 4th Edition, 2002
[4]. Bic J. C., Duponteil D. and Imbeaux, J. C. Principles of communication systems, John Wiley & Sons, 1986
[5]. Pichevar R., Vakili V. T., Channel equalization using neural networks, IEEE, 1999

ABSTRACT: This paper is aimed to reduce background noise introduced in speech signal during capture, storage, transmission and processing using Spectral Subtraction algorithm. To consider the fact that colored noise corrupts the speech signal non-uniformly over different frequency bands, Multi-Band Spectral Subtraction (MBSS) approach is exploited wherein amount of noise subtracted from noisy speech signal is decided by a weighting factor. Choice of optimal values of weights decides the performance of the speech enhancement system. In this paper weights are decided based on SFM...........

Keywords: Multi-Band Spectral Subtraction, Spectral Flatness Measure, Speech enhancement, SFM, MBSS.

[1] M. Berouti, R. Schwartz, J. Makhoul, "Enhancement of speech corrupted by acoustic noise,"Proc. IEEE Int. Conf. Acoust., Speech, Signal Process.,pp. 208–211, April 1979.
[2] C.-T. Lin, "Single-channel speech enhancement in variable noise-level environment," IEEE Trans. Syst. Man Cybernet. A 33 (1) (2003) 137–143.
[3] Radu Mihnea Udrea, Nicolae D. Vizireanu, Silviu Ciochina, "An improved spectral subtraction method for speech enhancement using a perceptual weighting filter," Elsevier Digital Signal Processing 18, pp. 581-587, Aug 2007.
[4] S. Kamath, and P. C. Loizou, "A multi-band spectral subtraction method for enhancing speech corrupted by colored noise," in Proceedings of Int. Conf. on Acoustics, Speech, and Signal Processing, Orlando, USA, May 2002, vol. 4, pp. 4160 4164.
[5] S.F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech

ABSTRACT: Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed...........

Keywords: Macular Edema (ME), Optical Coherence Tomography (OCT), Thickness, Area, Textural Features, Discrete Wavelet Transform (DWT), Support Vector Machine (SVM), Confusion Matrix

[1]. James G. Fujimoto, Costas Pitris, Stephen A. Boppart, Mark E. Brezinski. "Optical Coherence Tomography: An Emerging Technology for Biomedical Imaging and Optical Biopsy". Neoplasia vol. 2, pp. 9-25, January-April 2000.
[2]. Joel S. Schuman. "Introduction to Optical Coherence Tomography", 5 October, 2012.
[3]. Eric H. Broecker, Mark T. Dunbar. "Optical Coherence Tomography: its clinical use for the diagnosis, pathogenesis, and management of macular conditions". Optometry, Elsevier. vol.76, no.2, pp. 79-101. February, 2005.
[4]. Gary R. Wilkins, Odette M. Houghton, Amy L. Oldenburg. "Automated Segmentation of Intraretinal Cystoid Fluid in Optical Coherence Tomography". IEEE Trans Biomed Eng. vol. 59(4), pp. 1109-1114, April 2012.
[5]. Appaji M. Abhishek, Tos T.J.M. Berendschot, Shyam Vasudeva Rao, Supriya Dabir. "Segmentation and nalysis of Retinal Layers (ILM & RPE in Optical Coherence Tomography Images with Edema". 2014 IEEE Conference on Biomedical Engineering and Sciences. pp. 204-209, 8 – 10 December, 2014

ABSTRACT: As there is a demand for portable electronic systems or devices, there is an incremental growth in the technology in the past few decades and also technology is cumulative at a random rate, devices are consuming large amount of power due to this the life of the battery is draining fast. so there must be a alternative devices or circuits which can reduce the power by efficiently maintaining the area and performance, therefore life of battery can be increased. As SRAM is the heart of block in all the electronic design, where the power consumption is maximum there by analyzing, estimating & modifying or changing the logic, will be able to reduce the power and performance can be greatly achieved..........

Keywords: SNM, RNM, Subthreshold operation, ultra-low power applications.

[1] SRAM cell with improved stability and reduced leakage current for subthreshold region of operation" by P. Sreelakshmi, K. S. Pande and N. S. Murty In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, 2015, pp. 1-5.doi: 10.1109/ICCIC.2015.7435750.
[2] David J. Comer, Senior Member, IEEE, and Donald T. Comer"Operation of Analog MOS Circuits in the Weak or Moderate Inversion Region" IEEE TRANSACTIONS ON EDUCATION, VOL. 47, NO. 4, NOVEMBER 2004.
[3]. "Low-power 9T subthreshold SRAM cell with single-ended write scheme, By A. Sinha, V. Kumar and A. Islam, " 2015 AnnualIEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.doi: 10.1109/INDICON.2015.7443137.[10]. "Analysis of SRAM cell designs for low power applications, By C. K. Sharma and R. Chandel, " Convergence of Technology (I2CT), 2014 International Conference for, Pune, 2014, pp. 1-4.doi: 10.1109/I2CT.2014.7092157
[3] Alice Wang Benton H. Calhoun Anantha P. Chandrakasan "Sub-threshold Design for Ultra Low-Power Systems"SERIES ON INTEGRATED CIRCUITS AND SYSTEMS
[4] Chang, L. Montoye, R.K. Nakamura, Y.Batson, K.A.Eickemeyer, R.J.Dennard, R.H. Haensch,W.Jamsek, D, "An 8T-SRAM for variability tolerance and low-voltage operation in highperformancecaches", Solid-State Circuits, IEEE Journal vol. 43, April 2008, Issue 4, pp-956-963.

ABSTRACT: There is need to develop various new design techniques in order to fulfil the demand of increased speed, reduced area for compactness and reduced power consumption. It is considered that improved other performance specifications such as less delay, high noise immunity and suitable ambient temperature conditions are the prime factors. In this paper two different techniques are used for designing a 4-bit Magnitude Comparator(MC) and then a comparison is made about area and average delay. First one is Transmission Gate (TG) technique and second one is GDI Technique. This paper describes the design of an Integrated Circuit (IC) layout for a 4-bit MC. The layout was designed by use of an open source software namely Electric VLSI Design System which is Electronic Design Automation (EDA) tool. LTspiceXVII is used as simulator to carry out the simulation work.

Keywords: TG, GDI, Comparator, VLSI, CMOS, DRC, LVS, ERC, MC.

[1]. C. H. Chang, J. Gu and M. Zhang, "A review of 0.18um full adder performance for tree structured arithmetic circuits", IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 13, No. 6, pp.686-695, June 2005.
[2]. Soh Hong Teen and Li Li Lim," IC Layout Design of Decoder Using Electric VLSI Design System", International Journal of Electronics and Electrical Engineering Vol. 3, No. 1, February, 2015, pp:54-60
[3]. A. P. Douglas and E. Kamran, Basic VLSI Design, 3rd ed., Prentice Hall, 1994, pp. 72-76.
[4]. About Electric. (May 2013). [Online]. Available: www.staticfreesoft.com/electric.html

ABSTRACT: Brain-computer interface (BCI) is a communication pathway between brain and an external device. It translates human thought into commands to control the external devices.Electroencephalography (EEG) is cost effective and easier way to implement the BCI. This paper presents a novel method for classifying EEG during motor imagery by the combination of common spatial pattern (CSP) and linear discriminant analysis (LDA). In the proposed method, the EEG signal is bandpass-filtered into multiple frequency bands. The CSP features are then extracted from each of these bands............

Keywords: Brain computer interface, electroencephalography, sub-band common spatial pattern.

[1]. B. Rebsamen, E. Burdet, C. Guan, H. Zhang, C. L. Teo, Q.Zeng, C.Laugier, and M. H. Ang Jr., "Controlling a Wheelchair Indoors UsingThought," IEEE IntelligentSystems, vol. 22, no. 2, pp. 18-24, 2007.
[2]. N. Birbaumer, "Brain-computer-interface research: Coming of age," Clin. Neurophysiol., vol. 117, no. 3, pp. 479-483, 2006.
[3]. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller and T. M. Vaughan, "Brain-computer interfaces for communication andcontrol,"Clin. Neurophys., vol. 113, pp. 767-791, 2002.
[4]. H. Ramoser, J. M¨uller-Gerking, and G. Pfurtscheller, "Optimal spatial filtering of single trial EEG during imagined hand movement," IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441–447, 2000.
[5]. G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.- R. M¨uller, "Optimizing spatio-temporal filters for improving Brain-Computer Interfacing," in Advances in Neural Inf. Proc. Systems (NIPS05), J. Platt, Ed., vol. 18, Vancouver, Canada, December 2005.

ABSTRACT: This paper represents the design and implementation of Low Noise Amplifier for Ultra wideband application using 0.18μm CMOS Technology. The proposed two stage LNA is for a 3-5 GHz. At supply voltage of 1.8V, for the exceed limit of 50μm of width of each transistor, the power consumption is 7.22mW. Noise figure is 4.33dB, Maximum power gain i.e. S21 is 20.4dB, S12 < -20dB, S11 < -8dB, S22 < -10dB. For the required bandwidth range, LNA is unconditionally stable and have good linearity.

Keywords: LNA, Biasing circuit, Input and Output matching, Power Consumption, Noise Figure, Gain, Stability.

[1]. R.M.Patrikar, V.P.Bhale, U.D.Dalal, "A high Stability and excellent gain flatness 3-5GHz 0.18μm CMOS Low Noise Amplifier for Ultra-Wide-Band Applications". 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS).
[2]. Behzad Razavi , "Design of Analog CMOS Integrated Circuits". McGraw Hill Education (India) Edition 2002.
[3]. J.Y.Hasani , "Low Noise Amplfier Design and Optimization". , 2008.
[4]. "JFET Biasing Techniques". Siliconix 10-march-97.
[5]. Syed Ibadur Rahman, Shaik Abdul Kareem, Shaik Habeeb "Design of a Low Noise Amplifier using 0.18μm CMOS technology"(The International Journal Of Engineering And Science (IJES); Volume 4, Issue 6, Pages PP.11-16, June – 2015, ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805).