S. No

Volume-4 Issue-9, April 2017, ISSN:  2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

G Dinesh, C. K Aravind Raj, S. Muthu Saravanan, M Tamil Thendral

Paper Title:

Cost Effective Resource Scheduling for Infrastructure as a Service (IaaS) in Cloud Computing

Abstract: Resource scheduling assigns the precise and accurate task to CPU, network, and storage. The aim behind this is the extreme usage of resources. However, well organized scheduling is needed for both cloud providers and cloud users. We propose a cost effective Resource scheduling algorithm that minimizes the utilization cost of the resources while meeting the deadline in cloud computing environment. we are calculating processing cost based on MIPS (Machine Instruction Per Second), For Memory (RAM) and Bandwidth based on MBPS(Megabytes Per Second) and For storage is based on MBs consumed to store the data. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay.

Keywords: Resource management, IaaS cloud, Resource scheduling, Resource discovery, Resource allocation.      

References:
1.       Amazon Elastic Compute Cloud (Amazon EC2), http://aws.amazon.com/ec2, 2008.
2.       Amazon Simple Storage Service (Amazon S3), http://aws.amazon.com/s3, 2008.
3.       Chia-Ming Wu∗, Ruay-Shiung Chang, Hsin-Yu Chan,”A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters” in Future Generation
Computer Systems 37 (2014) 141–147
4.       Yu J, Kirley M , Buyya R. Multi-Objective Planning for Workflow Execution on Grids, 8th IEEE/ ACM International Conference on Grid Computing, IEEE Computer Society, USA, 2007, p.10−17.
5.       Chen WN, Zhang J. An Ant Colony Optimization Approach  to a Grid Workflow Scheduling Problem with Various QoS Requirements, IEEE Transaction System Man Cybern. 2009; 39(1):29−43
6.       Zhang F, Cao J, Wu HK. Ordinal Optimized Scheduling of Scientific Workflows in Elastic Compute Clouds, 3rd IEEE International Conference on Cloud Computing Technology and Science, China, IEEE, 2011, p. 1−9.
7.       Maria Alejandra Rodriguez and Rajkumar Buyya, Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds in IEEE Transactions On Cloud computing, vol. 2, no. 2, april-june 2014 
8.       Jasraj Meena, Malay Kumar, And Manu Vardhan, Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint published in ieee access on cloud computing,vol.,4 ,September 28, 2016.
9.       P. Mell, ``The NIST definition of cloud Computing,'' Commun. ACM, vol. 3, no. 6, p. 50, 2010.
10.    R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, ``Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,'' Future Generat. Comput. Syst., vol. 25,no. 6, pp. 599_616, 2009
11.    J. Meena, M. Kumar, and M. Vardhan, ``Efficient utilization of commodity computers in academic institutes: A cloud  Computing approach,'' Int. J. Comput., Elect., Autom., Control Inf. Eng., vol. 9, no. 2, pp. 498_503,2015.
12.    Kashlev, S.Y. Lu, A system architecture for running big data workflows in the cloud, in: 2014 IEEE International Conference on Services Computing, SCC 2014, pp. 51–58.
13.    Amazon EC2 Pricing, accessed on Feb. 19, 2016. [Online]. Available: https://aws.amazon.com/ec2/pricing/
14.    Google Cloud Platform, accessed on Feb. 24, 2016. [Online]. Available: https://cloud.google.com/compute
15.    CloudSigma, accessed on Feb. 25, 2016. [Online]. Available: https://www.cloudsigma.com/pricing
16.    Cloudsim  simulation tool. Available: http://opensourceforu.com/2014/03/cloudsim-framework-modelling-simulating-cloud-environment
17.    Resources chedulingforinfras tructureasa service (IaaS) incloud computing: Challenge sand opportunities on 22 April 2016. Syed Hamid Hussain Madni n, Muhammad Shafie AbdL atiff, Yahaya Coulibaly, Shafi’i Muhammad Abdulhamid.

1-5

2.

Authors:

M. Prabhu, R. Rathish, Amal .P.V, Balakrishnan, Christopher. J, Gokul Nath

Paper Title:

Bicycle Operating on Compressed Air by Inversion of Slider Crank Mechanism

Abstract: This paper describes the working of a cycle which works on pneumatic power. A pneumatic cycle uses compressed air as a source of energy for locomotion. In this system a single acting pneumatic cylinder is operated as a slider crank mechanism which converts the linear reciprocation of the cylinder piston rod into oscillatory motion of the driver crank about the pinion shaft. The Hybrid Bicycle can either be pedaled manually or be run on compressed air. Compressed air is used to turn the real wheel through a slider crank mechanism via a chain and sprocket. This ensures that the pneumatic piston expands for half the revolution and contracts for the other half. A push button valve actuates this mechanism and a flow control valve is used for The actuation of the pneumatic piston used in the slider crank mechanism is controlled by a 5/2 pneumatic directional control valve which in turn is actuated by a push button through cam and follower arrangement connected to the crankshaft. speed control.

Keywords: Pneumatic Cylinder, Sprocket, Solenoid Valve, Pressure Gauge, Slider Crank Mechanism, compressed air

References:
1.    Haisheng Chen et al.  “Air fuelled zero emission road transportation: A comparative  study”, Applied Energy 88 (2011), 24 June 2010, pp: 337–342
2.    Amir Fazeli et al.  “A novel compression strategy for air hybrid engines” Applied Energy 88 (2011) ,8 March 2011,pp:2955–2966
3.    Ulf Bossel “Thermodynamic Analysis of Compressed Air Vehicle Propulsion”   European Fuel Cell Forum,Morgenacherstrasse 2F CH-5452 Oberrohrdorf/Switzerland,  April 2, 2009
4.    J.Gary Wood et al.  “Design of a low pressure air engine for third world use” 17th Annual Intersociety Energy Conversion Los Angeles, California August, 1982.
5.    Balakrishnan, N & Mayilsamy, K 2013, ‘A study of cotton coated with intumescents flame retardant: Kinetics and effect of blends of used vegetable oil methyl ester’,
Journal of Renewable and Sustainable Energy, vol.5, pp. 0531211-0531218. ISSN: 1941-7012.
6.    HE Wei et al.  “Performance study on three-stage power system of compressed air vehicle based on single-screw expander” science china, technological sciences, August 2010, pp: 2299–2303
7.    Thipse S S. Compressed air car. Tech Monitor, 2008, 1(2): 33–37

6-8

3.

Authors:

R. Sateesh, R. Rathish, M. Arunkuma R, P. Balakrishnan, B. Dhilip Kumar, S. Hariharan, M. Asok Raj Kumar

Paper Title:

Model Power Glider for Economic Transportation

Abstract: The venture is outline and manufacture of force lightweight plane for financial transportation. A  lightweight flyer as a lighter than air ship that is bolstered in flight by the dynamic response of the air against its lifting surface ; all lightweight planes adjust to the streamlined rule that make flight conceivable. The lightweight plane is scholarly test joined with the calm and excellence of flying high over the earth are two of the many reasons that individuals both youthful and old get snared on flying lightweight planes .Transportation constitutes nearly 20% of household expenditures, 30% of greenhouse gas (GHG) emissions, and 70% of petroleum utilization. From travel time funds to occupation creation, pay development to property estimation changes, engine vehicle collides with air quality and clamor impacts. The power lightweight flyer use to diminish the time in making a trip and simple to achieve the goal with no deterrent.

Keywords:  Para engine, Para lightweight plane, Altimeter, Two step strategy, vario meter.

References:
1.       Watanabe, T. and Ochi, Y., “Modeling and Simulation of Nonlinear Dynamics of a Powered Para glider,” AIAA ,Guidance, Navigation, and Control Conference and Exhibit, Honolulu, HI, AIAA Paper 2008-7418, Aug. 2008.
2.       J. P. Barnes. Flight without fuel regenerative soaring feasibility study. SAE Technical Paper 2006-01-2422, August 2006.
3.       Carter, David, et al. 'Autonomous Guidance, Navigation, and Control of Large Parafoils', 18th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar.
4.       ‘Guidance, Navigation and Control of a Small-Scale Para motor’  Jack Umenberger Australian Centre for Field Robotics .The University of Sydney, NSW, 2006, Australia.
5.       A Practical Method to Estimate the Aerodynamic Coefficients of a Small-Scale Paramotor ‘ Razvan-Viorel MIHAI*, Radu Calin PAHONIE, Ioana-Raluca EDU *Corresponding author  Millitary Technical Academy, Department of Integrated Systems of Aviation and Mechanics, Bucharest.
6.       ‘’ Evaluating The Geometric Shape Of A Flying Paraglider ’’ Klaus Hanke, Stefan Schenk Surveying and Geoinformation Unit, University of Innsbruck, Australia.
7.       ‘Linear Dynamics and PID Flight Control of a Powered Para glider’
8.       Karthikeyan, R, Solaimuthu, C & Balakrishnan, N 2014,   ‘A study of performance and emissions of diesel engine fuelled with neat diesel and heat hydnocarpus pentandra biodiesel’ IOSR Journal of Mechanical and Civil Engineering, vol. 10, issue.2, pp. 53-57,   E-ISSN: 2278-1684, P-ISSN: 2320-334X.
9.       Balakrishnan, N & Mayilsamy, K 2014, ‘Effect of compression ratio on CI engine performance with biodiesel and producer gas in mixed fuel mode’, Journal of Renewable and Sustainable Energy, vol.6, pp. 0231031-02310313. ISSN: 1941-7012.
10.    Investigation of the glider load spectra  Miroslaw Rodzewicz  Warsaw University of Technology  Nowowiejska 24, 00-665 Warsaw, Poland.

9-12

4.

Authors:

N. Poonguzhali, B. Punitha, J.Ashwini Mega, G. Thamizamudhu

Paper Title:

Convolutional Neural Network and Adaptive Dictionary Learning for Brain Tumour Cell Detection

Abstract: In this paper, we propose an efficient brain tumor detection method and an automatic segmentation method, which can detect tumor and locate it in the brain MRI images. Automatic and reliable segmentation methods are used in order to manage large spatial and structural variability among brain tumors. Also, some pre-processing steps are used for tumor detection purpose. The automatic segmentation method is based on convolution neural networks. We present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The automatic cell detection results are compared with manually annotated ground truth and other state-of-the-art cell detection algorithms.

Keywords: Cell detection, MRI images.

References:
1.        S. Bauer et al., “A survey of mri-based medical image analysis for brain tumor studies,” Physics in medicine and biology, vol. 58, no. 13, pp. 97– 129, 2013.
2.        N. Louis et al., “The 2007 who classification of tumours of the central nervous system,” Acta neuropathologica, vol. 114, no. 2, pp. 97–109, 2007.
3.        G. Van Meir et al., “Exciting new advances in neuro-oncology: The avenue to a cure for malignant glioma,” CA: a cancer journal for clinicians, vol. 60, no. 3, pp. 166–193, 2010.
4.        Tabatabai et al., “Molecular diagnostics of gliomas: the clinical perspective,” Acta neuropathologica, vol. 120, no. 5, pp. 585–592, 2010.
5.        Menze et al., “The multimodal brain tumor image segmentation benchmark (brats),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993–2024, 2015.
6.        N. J. Tustison et al., “N4itk: improved n3 bias correction,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010.
7.        L. G. Nyul, J. K. Udupa, and X. Zhang, “New variants of a method ´ of mri scale standardization,” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 143–150, 2000.
8.        M. Prastawa et al., “A brain tumor segmentation framework based on outlier detection,” Medical image analysis, vol. 8, no. 3, pp. 275–283, 2004.
9.        H. Menze et al., “A generative model for brain tumor segmentation in multi-modal images,” in Medical Image Computing and ComputerAssisted Intervention–MICCAI 2010. Springer, 2010, pp. 151–159.
10.     Gooya et al., “Glistr: glioma image segmentation and registration,” IEEE Transactions on Medical Imaging, vol. 31, no. 10, pp. 1941–1954, 2012.
11.     Kwon et al., “Combining generative models for multifocal glioma segmentation and registration,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer, 2014, pp. 763–770.
12.     S. Bauer, L.-P. Nolte, and M. Reyes, “Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization,” in Medical Image Computing and Computer-Assisted Intervention– MICCAI 2011. Springer, 2011, pp. 354–361.
13.     C.-H. Lee et al., “Segmenting brain tumors using pseudo–conditional random fields,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008. Springer, 2008, pp. 359–366.
14.     R. Meier et al., “A hybrid model for multimodal brain tumor segmentation,” in Proceedings of NCI-MICCAI BRATS, 2013, pp. 31–37.
15.     “Appearance-and context-sensitive features for brain tumor segmentation,” in MICCAI Brain Tumor Segmentation Challenge (BraTS), 2014, pp. 20–26.
16.     Zikic et al., “Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. Springer, 2012, pp. 369–376.
17.     S. Bauer et al., “Segmentation of brain tumor images based on integrated hierarchical classification and regularization,” Proceddings of MICCAIBRATS, pp. 10–13, 2012.
18.     S. Reza and K. Iftekharuddin, “Multi-fractal texture features for brain tumor and edema segmentation,” in SPIE Medical Imaging. International Society for Optics and Photonics, 2014, pp. 903 503–903 503.
19.     N. Tustison et al., “Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with antsr,” Neuroinformatics, vol. 13, no. 2, pp. 209–225, 2015.
20.     Geremia, B. H. Menze, and N. Ayache, “Spatially adaptive random forests,” in 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). IEEE, 2013, pp. 1344–1347.
21.     Pinto et al., “Brain tumour segmentation based on extremely randomized forest with high-level features,” in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 2015, pp. 3037–3040.
22.     Islam, S. Reza, and K. M. Iftekharuddin, “Multifractal texture estimation for detection and segmentation of brain tumors,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204–3215, 2013.
23.     R. Meier et al., “Patient-specific semi-supervised learning for postoperative brain tumor segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer, 2014, pp. 714–721.
24.     Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 8, pp. 1798–1828, 2013.
25.     Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
26.     Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolution neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
27.     S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolution neural networks for galaxy morphology prediction,” Monthly Notices of the Royal Astronomical Society, vol. 450, no. 2, pp. 1441– 1459, 2015.
28.     D. Ciresan et al., “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in neural information processing systems, 2012, pp. 2843–2851.
29.     D. Zikic et al., “Segmentation of brain tumor tissues with convolution neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 36–39, 2014.
30.     Urban et al., “Multi-modal brain tumor segmentation using deep convolution neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 1–5, 2014.
31.     Davy et al., “Brain tumor segmentation with deep neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 31–35, 2014. 32.     M. Havaei et al., “Brain tumor segmentation with deep neural networks,” arXiv:1505.03540v1, 2015. [Online]. Available: http://arxiv.org/abs/1505.03540
33.     M. Lyksborg et al., “An ensemble of 2d convolution neural networks for tumor segmentation,” in Image Analysis. Springer, 2015, pp. 201– 211.
34.     Rao, M. Sharifi, and A. Jaiswal, “Brain tumor segmentation with deep learning,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 56–59, 2015.
35.     P. Dvo˘rak and B. Menze, “Structured prediction with convolution ´ neural networks for multimodal brain tumor segmentation,” MICCAI Multimodal Brain Tumor
Segmentation Challenge (BraTS), pp. 13–24, 2015.
36.     K. Simonyan and A. Zisserman, “Very deep convolution networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556v6, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
37.     M. Shah et al., “Evaluating intensity normalization on mris of human brain with multiple sclerosis,” Medical image analysis, vol. 15, no. 2, pp. 267–282, 2011.
38.     L. Nyul and J. Udupa, “On standardizing the mr image intensity scale,” ´ Magnetic Resonance in Medicine, vol. 42, no. 6, pp. 1072–1081, 1999.
39.     Y. LeCun et al., “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989.
40.     “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
41.     X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in International conference on artificial intelligence and statistics, 2010, pp. 249–256.
42.     K. Jarrett et al., “What is the best multi-stage architecture for object recognition?” in Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009, pp. 2146–2153.
43.     L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. ICML, vol. 30, 2013.
44.     N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
45.     E. Hinton et al., “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580v1, 2012.  [Online]. Available: http://arxiv.org/abs/1207.0580
46.     M. Kistler et al., “The virtual skeleton database: An open access repository for biomedical research and collaboration,” Journal of Medical Internet Research, vol. 15, no. 11, Nov 2013.
47.     VirtualSkeleton, BRATS 2013, 2013. [Online]. Available: https://www.virtualskeleton.ch/BRATS/Start2013 [Accessed: September 30, 2015]
48.     Bastien et al., “Theano: new features and speed improvements,” Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
49.     Bergstra et al., “Theano: a CPU and GPU math expression compiler,” in Proceedings of the Python for Scientific Computing Conference (SciPy), Jun. 2010.

13-18

4.

Authors:

N. Poonguzhali, B. Punitha, J.Ashwini Mega, G. Thamizamudhu

Paper Title:

Convolutional Neural Network and Adaptive Dictionary Learning for Brain Tumour Cell Detection

Abstract: In this paper, we propose an efficient brain tumor detection method and an automatic segmentation method, which can detect tumor and locate it in the brain MRI images. Automatic and reliable segmentation methods are used in order to manage large spatial and structural variability among brain tumors. Also, some pre-processing steps are used for tumor detection purpose. The automatic segmentation method is based on convolution neural networks. We present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The automatic cell detection results are compared with manually annotated ground truth and other state-of-the-art cell detection algorithms.

Keywords: Cell detection, MRI images.

References:
1.        S. Bauer et al., “A survey of mri-based medical image analysis for brain tumor studies,” Physics in medicine and biology, vol. 58, no. 13, pp. 97– 129, 2013.
2.        N. Louis et al., “The 2007 who classification of tumours of the central nervous system,” Acta neuropathologica, vol. 114, no. 2, pp. 97–109, 2007.
3.        G. Van Meir et al., “Exciting new advances in neuro-oncology: The avenue to a cure for malignant glioma,” CA: a cancer journal for clinicians, vol. 60, no. 3, pp. 166–193, 2010.
4.        Tabatabai et al., “Molecular diagnostics of gliomas: the clinical perspective,” Acta neuropathologica, vol. 120, no. 5, pp. 585–592, 2010.
5.        Menze et al., “The multimodal brain tumor image segmentation benchmark (brats),” IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993–2024, 2015.
6.        N. J. Tustison et al., “N4itk: improved n3 bias correction,” IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010.
7.        L. G. Nyul, J. K. Udupa, and X. Zhang, “New variants of a method ´ of mri scale standardization,” IEEE Transactions on Medical Imaging, vol. 19, no. 2, pp. 143–150, 2000.
8.        M. Prastawa et al., “A brain tumor segmentation framework based on outlier detection,” Medical image analysis, vol. 8, no. 3, pp. 275–283, 2004.
9.        H. Menze et al., “A generative model for brain tumor segmentation in multi-modal images,” in Medical Image Computing and ComputerAssisted Intervention–MICCAI 2010. Springer, 2010, pp. 151–159.
10.     Gooya et al., “Glistr: glioma image segmentation and registration,” IEEE Transactions on Medical Imaging, vol. 31, no. 10, pp. 1941–1954, 2012.
11.     Kwon et al., “Combining generative models for multifocal glioma segmentation and registration,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer, 2014, pp. 763–770.
12.     S. Bauer, L.-P. Nolte, and M. Reyes, “Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization,” in Medical Image Computing and Computer-Assisted Intervention– MICCAI 2011. Springer, 2011, pp. 354–361.
13.     C.-H. Lee et al., “Segmenting brain tumors using pseudo–conditional random fields,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008. Springer, 2008, pp. 359–366.
14.     R. Meier et al., “A hybrid model for multimodal brain tumor segmentation,” in Proceedings of NCI-MICCAI BRATS, 2013, pp. 31–37.
15.     “Appearance-and context-sensitive features for brain tumor segmentation,” in MICCAI Brain Tumor Segmentation Challenge (BraTS), 2014, pp. 20–26.
16.     Zikic et al., “Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. Springer, 2012, pp. 369–376.
17.     S. Bauer et al., “Segmentation of brain tumor images based on integrated hierarchical classification and regularization,” Proceddings of MICCAIBRATS, pp. 10–13, 2012.
18.     S. Reza and K. Iftekharuddin, “Multi-fractal texture features for brain tumor and edema segmentation,” in SPIE Medical Imaging. International Society for Optics and Photonics, 2014, pp. 903 503–903 503.
19.     N. Tustison et al., “Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with antsr,” Neuroinformatics, vol. 13, no. 2, pp. 209–225, 2015.
20.     Geremia, B. H. Menze, and N. Ayache, “Spatially adaptive random forests,” in 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). IEEE, 2013, pp. 1344–1347.
21.     Pinto et al., “Brain tumour segmentation based on extremely randomized forest with high-level features,” in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 2015, pp. 3037–3040.
22.     Islam, S. Reza, and K. M. Iftekharuddin, “Multifractal texture estimation for detection and segmentation of brain tumors,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3204–3215, 2013.
23.     R. Meier et al., “Patient-specific semi-supervised learning for postoperative brain tumor segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer, 2014, pp. 714–721.
24.     Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 8, pp. 1798–1828, 2013.
25.     Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
26.     Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolution neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
27.     S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolution neural networks for galaxy morphology prediction,” Monthly Notices of the Royal Astronomical Society, vol. 450, no. 2, pp. 1441– 1459, 2015.
28.     D. Ciresan et al., “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in neural information processing systems, 2012, pp. 2843–2851.
29.     D. Zikic et al., “Segmentation of brain tumor tissues with convolution neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 36–39, 2014.
30.     Urban et al., “Multi-modal brain tumor segmentation using deep convolution neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 1–5, 2014.
31.     Davy et al., “Brain tumor segmentation with deep neural networks,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 31–35, 2014. 32.     M. Havaei et al., “Brain tumor segmentation with deep neural networks,” arXiv:1505.03540v1, 2015. [Online]. Available: http://arxiv.org/abs/1505.03540
33.     M. Lyksborg et al., “An ensemble of 2d convolution neural networks for tumor segmentation,” in Image Analysis. Springer, 2015, pp. 201– 211.
34.     Rao, M. Sharifi, and A. Jaiswal, “Brain tumor segmentation with deep learning,” MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), pp. 56–59, 2015.
35.     P. Dvo˘rak and B. Menze, “Structured prediction with convolution ´ neural networks for multimodal brain tumor segmentation,” MICCAI Multimodal Brain Tumor
Segmentation Challenge (BraTS), pp. 13–24, 2015.
36.     K. Simonyan and A. Zisserman, “Very deep convolution networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556v6, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556
37.     M. Shah et al., “Evaluating intensity normalization on mris of human brain with multiple sclerosis,” Medical image analysis, vol. 15, no. 2, pp. 267–282, 2011.
38.     L. Nyul and J. Udupa, “On standardizing the mr image intensity scale,” ´ Magnetic Resonance in Medicine, vol. 42, no. 6, pp. 1072–1081, 1999.
39.     Y. LeCun et al., “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541–551, 1989.
40.     “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.
41.     X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in International conference on artificial intelligence and statistics, 2010, pp. 249–256.
42.     K. Jarrett et al., “What is the best multi-stage architecture for object recognition?” in Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009, pp. 2146–2153.
43.     L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. ICML, vol. 30, 2013.
44.     N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
45.     E. Hinton et al., “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580v1, 2012.  [Online]. Available: http://arxiv.org/abs/1207.0580
46.     M. Kistler et al., “The virtual skeleton database: An open access repository for biomedical research and collaboration,” Journal of Medical Internet Research, vol. 15, no. 11, Nov 2013.
47.     VirtualSkeleton, BRATS 2013, 2013. [Online]. Available: https://www.virtualskeleton.ch/BRATS/Start2013 [Accessed: September 30, 2015]
48.     Bastien et al., “Theano: new features and speed improvements,” Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.
49.     Bergstra et al., “Theano: a CPU and GPU math expression compiler,” in Proceedings of the Python for Scientific Computing Conference (SciPy), Jun. 2010.

13-18

5.

Authors:

Julius Murumba, Elyjoy Micheni

Paper Title:

Social Media Integration into Problem Based Learning in Universities

Abstract: The current generation of students in universities is made up of digital natives who prefer to use social media to socialize and interact. Many Universities have introduced Problem Based Learning (PBL) and appears to be of growing interest particularly where such learning requires computer supported collaborative working. This article examined the current state of problem based learning in Kenyan universities and investigated the social networking technologies frequently used, factors influencing integration of social networks into PBL and challenges faced. The study is carried out through an examination of scientific research papers in journals and conference proceedings, and from online journals and reports. The paper concludes that integration of social networking technologies into PBL is necessary to enable use of real world problems or situations as a context for learning. The paper recommends that Institutional leaders should recognize the opportunities that social media and the Internet offer to problem based learning and develop supportive policies, and academic members of staff need to provide an educational context that enables students to integrate social media academically, especially in PBL

Keywords:  Social Media, Problem Based Learning, Universities.

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6.

Authors:

Panchal Anklesha, Reddy Neha, Thakare Apurva, V. P. Mulik

Paper Title:

Segway- Transporter

Abstract:  This project describes the design and fabrication of Segway transporter vehicle. The Segway is based on the principle of inverted pendulum that will keep an angle of Zero degrees with vertical at all times. The Segway is an intelligent vehicle which uses accelerometer to detect the motion of wheels, so that rider can accelerate, brake or steer the vehicle. This Segway is eco-friendly industrial transporter which carries load upto100kg including rider and load in trolly attached at front end. This kind of vehicle is interesting since it contains a lot of technology relevant to an environmentally friendly and energy efficient transportation industry. This thesis describes the development of a similar vehicle from scratch, incorporating every phase from literature survey to planning, design, vehicle construction and verification. The main objective of the project is the cost reduction of existing system with added user interface of real time clock, load in the trolley, direction and battery level indicator. The rider also gets warning with alarm on low battery .The rider controls are supposed to be natural movements leaning forward, backward and right-left; tilting of the handlebar and switch buttons also.

Keywords: AVR Atmega 328, Accelerometer, RTC, LCD Display, DC Motor, Motor Driver.

References:
1.    W. Bradley Knox, Juhyun Lee, and Peter Stone;Person Recognition on a Segway Robot: A Video of UT Austin Villa Robocup@Home 2007 Finals Demonstration; 2008 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 2008
2.    Jason Fleischer, BotondSzatm´ary, Donald Hutson, Douglas Moore, James Snook, Gerald M. Edelman, Jeffrey Krichmar; A neurally controlled robot competes and cooperates with humans in Segway; Proceedings of the 2006 IEEE International Conference on Robotics and Automation Orlando, Florida - May 2006
3.    B. Browning, P. Rybski, J. Searock, and M. Veloso, “Development of a soccer-playing dynamically-balancing mobile robot,” ser. Proc of the IEEE Intl Conf on Robotics and Automation, 2004, pp. 1752–1757.
4.    B. Szatm´ary, J. Fleischer, D. Hutson, D. Moore, J. Snook, G. M.Edelman, and J. Krichmar, “A Segway-based human-robot soccer team,”inIEEE International Conference on Robotics and Automation, 2006,this volume, poster submission.
5.    P. J. Besl and N. D. McKay,A Method for Registration of 3-D Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 239-256, 1992.

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