Archive

Multi-Context Cluster Based Trust Aware Routing ForInternet of Things

Authors:

Sowmya Gali, Venkatram N

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00029

Abstract:

Due to openness of the deployed environment and transmission medium (Internet), Internet of Things (IoT) suffers from various types of security attacks including Denial of service, Sinkhole, Tampering etc. Securing IoT is achieved a greater research interest and this paper proposes a new secure routing strategy for IoT based on trust model. In this model, initially the nodes of the network are formulated as clusters and the IoT nodes which are more prominent in trustworthiness and energy are only chosen as Cluster Heads. Further a trust evaluation mechanism was accomplished for every Cluster Node at Cluster Head to build a secure route for data transmission from source node to destination node. The trust evaluation is a composition of the communication trust, nobility trust and data trust. Simulation experiments are conducted over the proposed approach and the performance is analyzed through the performance metrics such as Packet Delivery Rate, Network Lifetime, and Malicious Detection Rate. The obtained performance metrics shows the outstanding performance of proposed method even in the increased malicious behavior of network.

Keywords:

Internet of Things,Trust Management,Clustering,Communication Trust,Malicious Detection Rate,Network Lifetime,

Refference:

I. Atzori L, Iera A and Morabito G. The Internet ofThings: a survey, ComputNetw
2010; 54(15): 2787–2805
II. Atzori, L., Iera, A., Morabito, G., Nitti, M. The Social Internet of Things (SIoT) –
when social networks meet the Internet of Things: concept, architecture and
networkcharacterization. Comput. Netw2012; 56(16):3594–3608,.
III. Bernabe, J.B., Ramos, J.L. H., Gomez, A.F.S.TAC-IoT: multidimensional
trustawareaccess control system for the Internet of Things. Soft
Comput.2016;20(5):1–17.
IV. D. Chen, G. Chang, D. Sun, J. Li, J. Jia, and X. Wang. TRM-IoT: A trust
management model based on fuzzy reputation for internet of things”, Computer
Science and Information Systems. 2011; 8(4):1207-1228.
V. Dong J, Qi M.A new clustering algorithm based on PSO with the jumping
mechanism of SA. In Proceedings of the 3rd International Conference on
Intelligent Information Technology Application, NJ, USA, 21–22, 61–64.
VI. FangyuGai, Jiexin Zhang, Peidong Zhu, and Xinwen Jiang. Multi-dimensional
Trust-Based AnomalyDetection System in Internet of Things.Springer
International Publishing2017; pp. 302–313.
VII. F. Bao and I. R. Chen. Dynamic trust management for internet of things
applications. In: Proc. of international workshop on Self-aware internet of
things2012; California, USA, pp.1-6.
VIII. F. Hao, G. Min, M. Lin, C. Luo, and L. Yang. Mobi-FuzzyTrust: An efficient
fuzzy trust inference mechanism in mobile social network. IEEETrans. Parallel
Distrib. Syst., 2014; 25(11): 2944-2955.
IX. F. Ishmanov, A.S. Malik, S.W. Kim, B. Begalov. Trust management systemin
wireless sensor networks: design considerations and research challenges. Trans.
Emerg. Telecommun. Technol 2015; 26:107–130.
X. Hasnat MA, Akbar M, Iqbal Z, Khan ZA, QasimU, JavaidN.Bio inspired
distributed energy efficient clustering for Wireless SensorNetworks, Information
Technology: Towards New Smart World (NSITNSW). 5th National Symposium
on, Riyadh;2015: pp. 1-7.
XI. Jabeur N, Yasar AUH, Shakshuki E, Haddad H. Towards bio-inspired adaptive
spatial clustering approach for IoT applications. Future Generation Computer
Systems: May 2017.
XII. Jacobsen R. H, Zhang Q, Toftegaard T. S.Bio-inspired Principles for Large-Scale
Networked Sensor Systems:An Overview. Sensors: 2011; 11(4): 4137–4151.
XIII. Jin Wang, Yiquan Cao, Bin Li.Particle swarm optimization based clustering
algorithm with mobile sink for WSNs.Journal of Future Generation Computer
Systems. 2017; 76©: 452-457.
XIV. Karaboga D, Okdem S, OzturkC.Cluster based wireless sensor network routing
using artificial bee colony algorithm. International journal of Wireless
Networks:2012;7(18): 847-860.
XV. Kokoris Kogias E, Voutyras O, Varvarigou T.TRM-SIoT:A scalable hybrid trust
& reputation model for the socialinternet of things. In: Proc., of IEEE 21st
international conference on emerging technologies and factory automation
(ETFA); 2016:1–9.

XVI. KrishnaveniV, Arumugam G.A novel enhanced bio-inspired harmony search
algorithm for clustering.International Conference onRecent Advances in
Computing and Software Systems (RACSS).2012;7-12.
XVII. Liang Y, Cai Z, Yu J, Han Q, Li Y. Deep learning based inferenceof private
information using embedded sensors insmart devices. IEEE Netw Mag 2018; 32:
8–14.
XVIII. Liu X, Li K, Guo S and Liu A. Top-k queries for categorizedRFID systems. IEEE
ACM T Network 2017; 25(5):2587–2600.
XIX. López T. S., Brintrup A., Isenberg, M A. and Mansfeld J. Resource Management
in the Internet of Things: Clustering, Synchronization and Software Agents.In:
Harrison, Mark, Uckelman, D and Michahelles, F, (eds.) Architecting the
Internet of Things. Springer-Verlag.2011; ISBN978-3-642-19156-5.
XX. Mohammad DahmanAlshehri, FarookhKhadeerHussain, Omar KhadeerHussain.
Clustering Driven Intelligent Trust Management Methodologyfor the Internet of
Things (CITM-IoT).Mobile Networks and Applications. 2018; 23(3):419-431.
XXI. Nitti, M., Girau, R., Atzori, L.Trustworthiness management in the Social
Internetof Things.IEEE Trans. Knowl. Data Eng.2014;26(5): 1253–1266.
XXII. P. K. Reddy, R.S. Babu. An Evolutionary Secure Energy Efficient Routing
Protocol in Internet of Things. International Journal of Intelligent Engineering
and Systems. 2017;10(3): 337-346.
XXIII. Qiu T, Liu X, Li K and Hu Q.Community-aware data propagationwith small
world feature for internet of vehicles.IEEE Commun Mag 2018;56(1):86-91.
XXIV. Raja SP, Rajkumar TD, and Raj VP. Internet of Things: challenges, issues and
applications. J Circuit Syst Comp 2018;27(12).
XXV. Rajagopal, A.Soft computing based cluster head selection in wireless sensor
network using bacterial foraging algorithm. Int. J. Electron. Commun. Eng2015;
9(3): 379-384.
XXVI. Reena Varghese, Dr. T. Chithralekha, CarynthiaKharkongor. Self-organized
Cluster Based Energy efficient MetaTrust model for Internet of Things. 2nd IEEE
International Conference on Engineering and Technology (ICETECH),
Coimbatore, 2016.
XXVII. Sandeep K.E, Kusuma S.M., Kumar V.B.P. Fire-LEACH: A Novel Clustering
Protocol for Wireless Sensor Networks Based on FireflyAlgorithm.International
Journal of ComputerScience Theory and Application. 2014: 1(1): 12-17.
XXVIII. Sarma N.V.S.N, and Gopi M. Implementation of Energy Efficient Clustering
Using Firefly Algorithm in Wireless Sensor Networks. 1st International Congress
on Computer, Electronics, Electrical, and Communication Engineering
(ICCEECE2014),IACSIT Press, Singapore, 2014: 59.
XXIX. Sarobin V.R, GanesanR. Bio-Inspired Cluster-Based Deterministic Node
Deployment in Wireless Sensor Networks. International Journal of Technology.
2016;4: 673-682.
XXX. Sarobin V.R, Ganesan R. Bio-Inspired, Cluster-Based Deterministic Node
Deployment in Wireless Sensor Networks. International Journal of
Technology.2016; 673-682.
XXXI. Senthilnath J, Omkar S.N, Mani V.Clustering using firefly algorithm:
performance study. Swarm and Evolutionary Computation. 2011: 1(3): 164 –
171.

XXXII. SowmyaGali, andVenkatramNidumolu. Multi-Context Trust Aware Routing For
Internet of Things.International Journal of Intelligent Engineering and
System.2019:12(1): 189-200.
XXXIII. Z. Yan, P. Zhang, and A.V. Vasilakos,.A survey on trust management for
Internet ofThings. J. Netw. Comput. Appl 2014; 42:120–134.
XXXIV. Zhang Q, Jacobsen RH, Toftegaard T. S.Bio-inspired low-complexity clustering
in large-scale dense wireless sensor networks.Global Communications
Conference (GLOBECOM), Anaheim, CA. 2012;658-663.
XXXV. Zhihua Zhang, Hongliang Zhu, ShoushanLuo, Yang Xin, and Xiaoming Liu.
Intrusion Detection Based on State Context andHierarchical Trust in Wireless
Sensor Network.IEEE Access, 2017; 5: 12088-12102.

View Download

Adaptive threshold back propagation neural network for rice grain classification using variance and co-variance colour features

Authors:

Ksh. Robert Singh, Saurabh Chaudhury

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00030

Abstract:

This paper presents a simple and fast feature extraction technique for classification of four varieties of rice grain. Three colour models (RGB, HSV and HSI) are obtained from the input colour images. Variance and Covariance features are then extracted from each of the three colour models. The classification of rice grains are then carried out using a Back Propagation Neural Network with adaptive thresholding. The computational time for feature extraction and their classification accuracies are also compared with other feature extraction techniques. It is found that the time taken using variance and covariance features extraction technique is relatively less compared to other feature extraction techniques. It is also seen that the proposed feature extraction technique is able to achieve better classification accuracy as compared to other feature extraction techniques discussed in this paper. Results suggest that the proposed technique is able to yield higher classification accuracy than that of other statistical classifiers like K- Nearest Neighbour (K-NN), Naïve Bayes and Support Vector Machine (SVM). The performances of all four classifiers were also tested against standard data sets.

Keywords:

Image,Colour,Features,Variance,Co-variance,Neural Network,

Refference:

I. A. Douik, and M. Abdellaoui, “Cereal grain classification by optimum
features and intelligent classifiers,” Int. J. of computer, communications and
control, Vol.: 5, pp. 506-516, 2010 6 1
II. Agung Wibowo, Yuri Rahayu, Andi Riyanto and Taufik Hidayatulloh,
“Classification algorithm for edible mushroom identification,” International
conference on Information and communications Technology (ICOIACT),
Indonesia, pp. 250-253, 2018 38 2
III. Alireza Pazokia, and Zohreh Pazokia, “Classification system for rain fed
wheat grain cultivars using artificial neural network,” African J.
Biotechnology, Vol.: 10, Issue: 41, pp. 8031-8038, 2011 16 3
IV. Alireza Pourreza, Hamidreza Pourreza, and M.H. Hbbaspour-Fard,
“Identification of nine Iranian wheat seed varieties by textural analysis with
image processing,” Computers and Electronics in Agriculture. Vol.: 83, pp.
102-108, 2012 19 4
V. Alireza Sanaeifar, Adel Bakhshipour, and Miguel Dela Guardia, “Prediction
of banana quality indices from colour features using support vector
regression,” Talanta. Vol.: 148, pp. 54-61, 2016 29 5
VI. A.R. Pazoki, F. Farokhi, and Z. Pazoki, “Classification of rice grain varieties
using two artificial neural networks (MLP and Neuro-Fuzzy),” The Journal of
Animal & Plant Sciences. Vol.: 24, Issue: 1, pp. 336-343, 2014 15 6
VII. Aydin Gullu, Ozan AKI, and Erdem Ucar, “Classification of rice grain using
image processing and machine learning techniques,” International Scientific
Conference, pp. 352-354, 2015 22 7
VIII. B.S. Anami, D.G. Savakar, and Aziz Makandar, “A neural network model for
classification of Bulk grain samples based on colour and texture,” Proceeding
of International conference on cognition and recognition, pp. 359-368, 2005
17 8
IX. D.K. Srivastava, and Lekha Bhambhu, “Data Classification Using Support
Vector Machine,” Journal of Theoretical and Applied Information
Technology, Vol.: 12, Issue: 1, pp. 1-7, 2010 23 9
X. F. Guevara-Hernandez, and J. Gonez-Gil, “A machine vision system for
classification of wheat and barley grain kernels,” Spanish Journal of
Agricultural Research. Vol.: 9, Issue: 3, pp. 672-680, 2011 24 10

XI. Federico Marini, Remo Bucci, and Antonio L. Magri, “Classification of 6
durum wheat cultivars from Sicily (Italy) using artificial neural network,”
Chemometrics and intelligent laboratory systems, Vol.: 90, pp.1-7, 2007 7 11
XII. Harpret Kaur, and Baljit Singh, “Classification and grading of rice using
multi-class SVM,” International Journal of scientific and research
publication, Vol.: 3, Issue: 4, pp. 1-5, 2013 25 12
XIII. H.K. Mebatsion, J.Paliwal, and D.S. Jayas, “Automatic classification of nontouching
cereal grain in digital image using limited morphological and colour
features,” Computers and electronics in Agriculture. Vol.: 90, pp. 99-105,
2013 3 13
XIV. Ian C. Navotas, Charisse Nadine V. Santos, Earl John M. Balderrama,
Francia Emmanuelle B. Candido, Aloysius John E. Villacanas, and Jessica S.
Velasco, “Fish identification and freshness classification through image
processing using artificial neural network,” ARPN Journal of Engineering
and Applied Sciences, Vol.:13, Issue: 18,pp. 4912-4922, 2018 46 14
XV. Iman Golpour, Jafar Amir Parian, and Reza Amir Chayjan, “Identification
and classification of bulk paddy,brown,and white rice cultivars with colour
features extraction using image analysis and neural network. Czech J. Food
Sci. Vol.: 32, Issue: 3, pp. 280-287, 2014 26 15
XVI. Irena Orina, Marena Manley, and Paul J williams, “Non-destructive
technique for detection of fungal infection in cereal grain”, Food Research
International. Vol.: 100, pp. 74-86, 2017 32 16
XVII. Irmgard Hein, Aifonso Rojas-Dominguez, Manuel Ornelas, Giulia D’Ercole,
and Lisa Peloschek, “Automatic classification of archaeological ceramic
materials by means of texture measures,” Journal of Archaeological Science
Report, Vol.: 21, pp. 921-928, 2018 44 17
XVIII. Ji Sang Bae, Sang-Ho Lee, Kang Sun Choi, and Jonk ok kim, “Robust skin
roughness estimation based on co-occurrence matrix,” J. Vis. Commun.
Image R., Vol.: 46, pp. 13-22, 2017 33 18
XIX. J. Paliwal, N.S. Visen, and D.S. Jayas, “Evaluation of neural network
architecture for cereal grain classification using morphological features.” J.
argic. Engg Res., Vol.: 79, Issue: 4, pp. 361-370, 2001 4 19
XX. J. Paliwal, N.S. Visen, and D.S. Jayas, “Cereal grain and dockage
identification using machine Vision,” Bio-system Engineering. Vol.: 85,
Issue: 1, pp. 51-57, 2003 14 20
XXI. Kamil Dimililer and Ehsan Kiani, “Application of Back Propagation Neural
Networks on Maize plant detection”. Procedia Computer Science, 9th
International Conference on theory and applications of soft computing,
computing with words and perceptron, ICSCCW, Hungary, pp. 376-381,
2017 34 21
XXII. Kivanc Kilic, Ismail Hakki Boyaci, and Hamit KoKsel, “A classification
system for beans using computer vision system and artificial neural
networks,” Journal of Food Engineering, Vol.: 78, pp. 897-904, 2007 8 22

XXIII. K. Neelamma Patil, S. Virendra, and Malemath, “Colour and texture based
identification and classification of food grains using different colour models
and Haralick features,” International journal of Computer Science and
Engineering. Vol.: 3, pp. 3669-3679, 2011 21 23
XXIV. Kusworo Adi, Catur Edi Widodo, Aris Puji Widodo, Rahmat Gernowo, Adi
Pamungkas, and Rizky Ayomi Syifa, “Detection lungs cancer using Gray
level co-occurrence matrix (GLCM) and Back propagation neural network
classification,” Journal of Engineering Science and Technology Review,
Vol.:11, Issue: 2, pp. 8-12, 2018 45 24
XXV. Lin Mar Oo and Nay Zar Aung, “A simple and efficient method for automatic
strawberry shape and size estimation and classification,” Biosystem
Engineering, Vol.: 170, pp. 96-107, 2018 39 25
XXVI. LIU Zhao-yan, CHENG Fang, and YING Yi-bin, “Identification of rice seed
varieties using neural network,” Journal of Zhejiang University SCIENCE.
Vol.: 6B, Issue: 11, pp.1095-1100, 2005 9 26
XXVII. Malay Kishore Dutta, Ashish Issac, Navroj Minhas, and Biplab Sarker,
“Image processing based method to assess fish quality and freshness,”
Journal of Food Engineering. Vol.: 177, pp. 50-58, 2016 30 27
XXVIII. Malgorzata Charytanowicz, PiotrKulezycki and piotr A. Kowalski, “An
evaluation of utilized geometric features for wheat grain classification using
X-ray image,” Computers and Electronics in agriculture. Vol.: 144, pp. 260-
268, 2018 40 28
XXIX. Muhammad Tahir, “Pattern analysis of protein image from fluorescence
microscopy using GLCM,” Journal of King Saud University Science, Vol.:
30, pp. 29-40, 2018 41 29
XXX. N.S. Visen, J. Paliwal, D.S. Jayas, “Image analysis of bulk grain samples
using neural network,” Canadian Biosystem Engineering. Vol.: 46, pp. 7.11-
7.15, 2004 18 30
XXXI. P. Vithu, and J.A. Moses, “Machine vision system for food grain quality
evaluation: A review,” Trends in food Science and Technology. Vol.: 56, pp.
13-20, 2016 31 31
XXXII. Rafael C Gonzalez and Richard E Woods, “Digital Image Processing,” New
Delhi, Pearson Prentice Hall (2009). 2 32
XXXIII. R. Choudhary, J. Paliwal, and D.S. Jayas, “Classification of cereal grain
using wavelet, morphological, colour and texture features of non-touching
kernel,” Biosystem Engineering, Vol.: 99, pp. 330-337, 2008 5 33
XXXIV. Sabiq Adzhani Hammam, Tito Waluyo Purboyo, and Randy Erfa Saputra,
“Cotton texture segmentation based on image texture analysis using gray
level run length and Ecludian distance,” Journal of theoretical and applied
information technology. Vol.: 95, Issue: 24, pp. 6915-6923, 2017 35 34
XXXV. Saurabh Agrawal, N.K. Verma, & Prateek Tamrakar, “Content based colour
image classification using SVM,” Eight International conferences on
information technology: New generation (2011), pp. 1090-1094, 2011 27 35
XXXVI. Silvia Grassi, Ernestina Casiraghi, and Cristina Alamprese, “Fish fillet
authentication by image analysis,” Journal of food Engineering, Vol.: 234,
pp. 16-23, 2018 43 36

XXXVII. Sitt Wetenriajeng, Ansar Suyuti, Intan Sari arena and Ingrid Nurtanio,
“Classification of Passion fruit’s ripeness using K-mean clustering and
Artificial neural network,” International conference on Information and
communications Technology (ICOIACT), Indonesia, pp. 304-309, 2018 42
37
XXXVIII. S. Jayaraman, S. Esakkirajan, and T. Veerakumar, “Digital Image
Processing,” New Delhi, Tata McGraw Hill Education (2009). 1 38
XXXIX. S. Majundar, and D.S. Jayas, “Classification of bulk samples of cereal grain
using machine vision,” J. Agric. Engng Res. Vol.: 73, pp. 35-47, 1999 20 39
XL. S. Majundar, and D.S. Jayas, “Classification of cereal grain using machine
vision. I. Morphology model,” Transaction of the ASAE, Vol.: 43, Issue: 6,
pp.1669-1675, 2000 10 40
XLI. S. Majundar, and D.S Jayas, “Classification of cereal grain using machine
vision. II. Colour model,” Transaction of the ASAE. Vol.: 43, Issue: 6,
pp.1677-1680, 2000 11 41
XLII. S. Majundar, and D.S. Jayas, “Classification of cereal grain using machine
vision.III. Texture Model,” Transaction of the ASAE. Vol.: 43, Issue: 6, pp.
1681-1687, 2000 12 42
XLIII. S. Majundar, and D.S. Jayas, “Classification of cereal grain using machine
vision. IV. Combined morphology, colour and texture model,” Transaction of
the ASAE. Vol.: 43, Issue: 6, pp. 1689-1694, 2000 13 43
XLIV. Suharjito, Bahtiar Imran and Abba Suganda Girsang, “Family relationship
identification by using Extract Features of Gray Level Co-occurrence Matrix
(GLCM) Based on Parents and Children Fingerprint,” International Journal of
Electrical and Computer Engineering, Vol.: 7, Issue: 5, pp. 2738-2745, 2017
36 44
XLV. Wan Nur Hafsha Wan Kairuddin and Wan Mahani Hafizah Wan
Mahmud,“Texture feature analysis for different resolution level of kidney
ultrasound images,” International Research and Innovation Submit
(IRIS2017). IOP Conf. Series: Material Science and Engineering 226, pp. 1-
9, 2017 37 45
XLVI. Yudong Zhang, Shuihua Wang, and Genlin Ji, “Fruit classification using
computer vision and feed forward neural network,” Journal of Food
Engineering. Vol.: 143, pp. 167-177, 2014 28 46

View Download

Assessment of Data Sophistication in HR functions by Applying Ridit Analysis

Authors:

Sripathi Kalvakolanu, Chendragiri Madhavaiah

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00031

Abstract:

The wealth of organizations is being determined by the amount of quality data they possess. Organizations across the globe have recognised this phenomenon. With abundance of data along with advanced analytic tools and technologies, many organizations have embraced business analytics into their essential strategic and operational decision-making tools. Heart of business analytics is the data. The quality and value of decision-making outcomes lie with the data inputs supplied. Big data and social media analytics have given impetus to the expansion of business analytics into all critical functional areas of the organization. Though a little late, the domain of HR has also caught up the trend of applying analytics. This new area is termed as people analytics or HR analytics. In this paper, an attempt is made to understand the extent of data availability and usage in analytics, termed as data sophistication in HR analytics in the organizations. As there are no definite ways to determine the data sophistication levels, a response sheet with a set of 20 items is developed based on previous literature. Data is collected from HR professionals. This data is subjected to exploratory factor analysis to capture the important dimensions from the items. Using structural equation modelling, confirmatory factor analysis was carried out to assess the model fit. Based on the resultant model, data is subjected to ridit analysis to interpret the treatment effect intuitively. The findings of the study add to the field of study in the area of data analytics, HR analytics, and decision-making domains. New approaches and study opportunities in related areas can be explored in this area due to its fast-emerging nature.

Keywords:

Data analytics,Data sophistication,HR analytics,ridit analysis,

Refference:

I. Agresti A. (1984). Analysis of Ordinal Categorical Data, N.Y.:John Wiley
andSons.
II. Akerkar, R. (2014). Analytics on Big Aviation Data: Turning Data into
Insights. IJCSA, 11(3), 116-127.
III. Barton, D., & Court, D. (2012). Making advanced analytics work for
you. Harvard business review, 90(10), 78-83.
IV. Bross I. (1958).How to Use RIDIT Analysis, Biometrics, 14(1), 18-38.
V. Banerjee, A., Bandyopadhyay, T., &Acharya, P. (2013). Data analytics:
Hyped up aspirations or true potential?. Vikalpa, 38(4), 1-12.

VI. Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent
analytics. Harvard business review, 88(10), 52-58.
VII. DeRomree, H., Fecheyr-Lippens, B., &Schaninger, B. (2016). People
analytics reveals three things HR may be getting wrong. McKinsey Quarterly.
VIII. Earley, S. (2015). Analytics, machine learning, and the internet of things. IT
Professional, 17(1), 10-13.
IX. Fink, A. A., &Sturman, M. C. (2017). HR Metrics and Talent Analytics. The
Oxford Handbook of Talent Management, 375-390.
X. Hair, J. F., Black, W.C., Babin, B.J. and Anderson, R.E. (2013), Multivariate
Data Analysis: A Global Perspective, 7/e., Pearson Education: New Delhi.
XI. Kohavi, R., Rothleder, N. J., &Simoudis, E. (2002). Emerging trends in
business analytics. Communications of the ACM, 45(8), 45-48.
XII. Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., &Haydock, M. (2012).
Analytics: The widening divide. MIT Sloan Management Review, 53(2), 1.
XIII. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big
data: Issues and challenges moving forward. In 2013 46th Hawaii
International Conference on System Sciences (pp. 995-1004). IEEE.
XIV. Lahrmann, G., Marx, F., Winter, R., &Wortmann, F. (2010, October).
Business intelligence maturity models: an overview. In VII conference of the
Italian chapter of AIS (itAIS 2010). Italian chapter of AIS, Naples.
XV. Levenson, A. (2011): Using targeted analytics to improve talent decisions.
People & Strategy, Vol. 34, pp. 34–43.
XVI. Malladi, S. (2013). Adoption of business intelligence & analytics in
organizations–an empirical study of antecedents.
XVII. Miller, L., Schiller, D., & Rhone, M. (2011). Data warehouse maturity
assessment service. TERADATA. In.
XVIII. Molefe, M. (2014). From data to insights: HR analytics in organisations
(Doctoral dissertation, University of Pretoria).
XIX. Poreca, S. (2018). The 4 Sophistication Levels of Data Analysis – Level Blog.
Retrieved from
https://www.northeastern.edu/levelblog/2018/03/28/sophistication-levelsdata-
analysis/
XX. Phillips-Wren, G. E., Iyer, L. S., Kulkarni, U. R., &Ariyachandra, T. (2015).
Business Analytics in the Context of Big Data: A Roadmap for
Research. CAIS, 37, 23.
XXI. Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Beyond the hype: the
hard work behind analytics success. MIT Sloan Management Review, 57(3).
XXII. Rex B. Kline. (2005). Principles and Practice of Structural Equation
Modeling, The Guilford Press

XXIII. Sahay, B. S., &Ranjan, J. (2008). Real time business intelligence in supply
chain analytics. Information Management & Computer Security, 16(1), 28-
48.
XXIV. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth
quarter, 19(4), 1-34.
XXV. Shanks, G., Bekmamedov, N., & Sharma, R. (2011). Creating value from
business analytics systems: a process-oriented theoretical framework and case
study.
XXVI. Tabachnick, B.G. &Fidell, L.S. (2013). Using multivariate statistics (6thedn).
Boston: Pearson Education.
XXVII. Wu, C. H. (2007). An empirical study on the transformation of Likert-scale
data to numerical scores. Applied Mathematical Sciences, 1(58), 2851-2862.
XXVIII. Watkins, M.W. (2000). Monte Carlo PCA for parallel analysis [computer
software]. State College, PA: Ed & Psych Associates.
XXIX. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding
its capabilities and potential benefits for healthcare
organizations. Technological Forecasting and Social Change, 126, 3-13.

View Download

A distinct approach to diagnose dengue fever with the help of soft set theory

Authors:

Fariha Iftikhar, Faiza Ghulam Nabi

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00032

Abstract:

Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper we used an expert system based on “soft set theory” and “fuzzy set theory” named as soft expert system to diagnose tropical disease dengue. This study discuss the role of “Soft set theory” as system which worked on the basis of knowledge in medical field. Study used “soft expert system” to predict the risk level or chances of a patient causing dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.

Keywords:

dengue fever,soft set theory,fuzzy set theory,intelligent systems,

Refference:

I. D. A. Molodtsov, soft set theory- first results, Comput. Math. Appl. 37
(1999) 19-31.
II. D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang, the parameterization
reduction of soft sets and its applications, Comput. Math. Appl. 49
(2005) 757-763.
III. F. M. E. Uzoka and K. Barker, Expert system and uncertainty in medical
diagnosis: A proposal for fuzzy-AHP hybridization, an international
journal of Medical Engg. And informatics, 2 (2010) 329-342.
IV. H. T. Nguyen and E. A. Walker, A first course in fuzzy logic,
Application in intelligent systems Boston; Kluwer Academic.
V. J. Durkin, Expert System Design and Development, New Jersy: Prentice
Hall (1994).
VI. L. A. Zadeh, Fuzzy sets, Inform. and Control 8 (1965) 338-353.
VII. L. Boullart, A. Krijgsman, R. A. Vingerhoeds, editors Applications of
Artificial Intelligence in process control program press, (1992).
VIII. M. I. Ali, F. Feng, X. Liu, W. K. Min, M. Shabbir, On new operation in
soft set theory. Comput. Math. Appl. 57 (2009) 1547-1553.

IX. P. K. Maji, R. Biswas, A. R. Roy, Soft set theory, Comput. Math. Appl.
45 (2003) 555-562.
X. P. Sharam, DBV. Singh, M. K. Bandil, and N. Mishra, Decision Support
System for Malaria and Dengue Diagnosis, International Journal of
information and Computational Technology, 3 (2013) 633-640.
XI. P. Szolovits, R. S. Patil and W. B. Schwartz, Artificial intelligence in
medical diagnosis, Journal of international medicine, 108 (1988) 80-87.
XII. S. B. Halsted, V. A. Suaya, D. S. Shepard, The burden of dengue
infection Lancet, 369 (2007) 1410-1411.
XIII. V. Pabbi: Fuzzy Expert System for Medical Diagnosis. IJSRP, 5 (2015).
XIV. X. Ma, N. Sulaiman, H. Qin, T. Herawan, V. M Zain, A new efficient
normal parameter reduction algorithm of soft sets, Comput. Math. Appl.
62 (2011) 588-598.
XV. Y. Zou, Z. Xiao, Data analysis approaches of soft sets under incomplete
information, known. Based Syst. 59 (2008)2128-2137.

View Download

A New Video Watermarking Using Redundant Discrete Wavelet in Singular Value Decomposition Domain

Authors:

Kalyanapu Srinivas, Pala Mahesh Kumar, Annam Jagadeeswara Rao

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00033

Abstract:

Digital watermarking is an innovation for the hiding a secret information into an object. It can be utilized ascopyright protection and secure concern for multimedia and digital information. This article presents a new video watermarking using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain. Further, it is also computed several image quality metrics lie peak signal-tonoise ratio (PSNR), structural similarity (SSIM) index and root mean square error (RMSE) to disclose the imperceptibility and robustness of proposed watermarking approach compared to conventional approaches. Extensive simulation results show that the proposed algorithm have performed superior to the conventional water marking algorithms.

Keywords:

Digital Watermarking,discrete wavelet transform,singular value decomposition,redundant discrete wavelet and image quality metrics,

Refference:

I. A. Siddiqui and A. Kaur, “A secure and robust image
watermarking system using wavelet domain”, Int. Conf. on Cloud
Comp., Data Sci. and Eng., Noida, India, Jun. 2017.
II. D. Frederic, et al. “Robust 3D DFT video watermarking”, in Proc.
of Security and Watermarking of Multimedia Contents, Electronic
Imaging, San Jos, CA, USA, vol. 3657, Apr. 1999.
III. F. Y. Shih, “Digital Watermarking and Steganography:
Fundamentals and Techniques”, December 17, 2007.
IV. http://www.alpvision.com/watermarking.html
V. https://en.wikipedia.org/wiki/Digital_watermarking
VI. J. R. Hernandez, M. Amado and F. Perez-Gonzalez, “DCT-domain
watermarking techniques for still images: detector performance
analysis and a new structure”, IEEE Trans. On Imag. Proces., vol.
9, no. 1, pp. 55-68, 2000.
VII. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VIII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square
Patch Antenna using Truncated Technique for WLAN
Applications”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
IX. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure
design for low profile steerable antenna applications” Journal of
advanced research in dynamical and control systems, Vol-10,
Special issue-03, 2018.
X. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG
structure for mutual coupling reduction in antenna arrays; a
comparitive study” International Journal of engineering and
technology, Vol-7, No-3.6, Special issue-06, 2018
XI. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression
band, In phase reflection band and High Impedance region of
3DEBG Characterization” International journal of applied
engineering research (IJAER), Vol 10, No 11, 2015.
XII. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna;
Design and Analysis”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-
8 June, 2019.
XIII. K. Sudhir and M. S. Gopal, “Dual watermarking based on Multiple
parameter fractional Fourier transform and LSB technique”, Int.
Conf. on Image Info. Proces., Shimla, India, 2011, pp. 1-5.

XIV. L. Kedmenec, A. Poljicak and L. Mandic, “Copyright protection of
images on a social network”, in Proc. ELMAR, Zadar, Croatia,
2014, pp. 1 – 4.
XV. M. Abdullatif, et al. “Properties of digital image
watermarking”, 9th Int. Collo. on Sig. Proces. and Appl., Kuala
Lumpur, Malaysia, Jun. 2013, pp. 235–240.
XVI. P. V. Nagarjuna, L. Bhaskar and B. R. Reddy, “Non-Decimated
Wavelet Domain based Robust Blind Digital Image Watermarking
Scheme Using Singular Value Decomposition”, 3rd Int. Conf. on
Sig. Proces. and Integ. Net., Noida, India, 2016, pp. 389-393.
XVII. R. K. Arya, S. Singh and R. Saharan, “A secure non-blind block
based digital image watermarking technique using DWT and
DCT”, Int. Conf. on Adv. in Comput., Comm. and Infor., Kochi,
India, IEEE, Aug. 2015, pp. 2041-2048.
XVIII. S. Chandran and K. Bhattacharya, “Performance analysis of LSB,
DCT and DWT for digital image watermarking application using
steganography”, Int. Conf. on Elect., Electr., Sig., Comm. and
Opt., Visakhapatnam, Andhra Pradesh, vol. 1, 2015, pp. 1-5.
XIX. S. Kadu, et al. “Discrete Wavelet Transform based Video
Watermarking technique”, Int. Conf. on Micro., Comp. and
Comm., Durgapur, India, 2016, pp. 1-6.
XX. S. Kumar, et al. “RGB image steganography on multiple frame
video using LSB technique”, Int. Conf. on Comp. and Comput.
Sci., Noida, India, Dec. 2015, pp. 226–231.
XXI. S. Saxena, P. Soni and M. Gujar, “A Secure and Robust DWT
based Digital Image Watermarking Technique”, International
Journal of Applications, vol. 168, no. 11, pp. 21-24, 2017.
XXII. Z. Jiang-bin, et al. “Color image watermarking based on DCTdomains
of color channels”, 10th Conf. on Comp., Comm., Cont.
and Power Eng., Beijing, China, vol. 1, no. 1, 2002, pp. 281-284.

View Download

Futuristic Machine Learning Techniques for Diabetes Detection

Authors:

Pavan kumar Panakanti, Sammulal Porika, SK Yadav

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00034

Abstract:

Diabetes detection has become an important task for medical practitioners in India and abroad. Researchers and scientists have been working on this problem actively. Machine learning has been contributing majorly to systems, techniques and solutions for diabetes detection problem. Yet there are challenges which remain to be addressed. Recently convolution based machine learning techniques have evolved to give efficient results in various domains. They have shown applicability over range of problems. So here recent architectures of Convolution based machine learning models like Convolutional Neural Networks (CNN) and Capsule Networks (CapsNet) are discussed. Also, application of these recent models is presented here. Additionally, challenges faced by current Diabetes detection systems are discussed. Along with these challenges CapsNet architecture for text analytics is presented. This CapsNet architecture is closest to Diabetes detection problem in terms of structure and arrangement of data to be handled. Thus in future this architecture and its variants can be applied for Diabetes detection.

Keywords:

Diabetes detection,Convolutional Neural Networks,CNN,Capsule Networks,CapsNet,

Refference:

I. A. Mackiewicz and W. Ratajczak, “Principal components analysis
(pca),” Computers and Geosciences, vol. 19, pp. 303-342, 1993.
II. A. Mobiny and H. Van Nguyen, “Fast capsnet for lung cancer
screening,” arXiv preprint arXiv:1806.07416, 2018.
III. A. Shahroudnejad, A. Mohammadi, and K. N. Plataniotis,
“Improved explainability of capsule networks: Relevance path by
agreement,” arXiv preprint arXiv:1802.10204, 2018.
IV. B. D. Kanchan and M. M. Kishor, “Study of machine learning
algorithms for special disease prediction using principal of
component analysis,” in Global Trends in Signal Processing,
Information Computing and Communication (ICGTSPICC), 2016
International Conference on, pp. 5-10, IEEE, 2016.
IV. B. Sierra and P. Larranaga, “Predicting survival in malignant skin
melanoma using bayesian networks automatically induced by
genetic algorithms. an empirical comparison between different
approaches,” Artificial Intelligence in Medicine, vol. 14, no. 1-2,
pp. 215-230, 1998.
V. C. Bennett, M. Guo, and S. Dharmage, “Hba1c as a screening tool
for detection of type 2 diabetes: a systematic review,” Diabetic
medicine, vol. 24, no. 4, pp. 333-343, 2007.
VI. C. Willi, P. Bodenmann, W. A. Ghali, P. D. Faris, and J. Cornuz,
“Active smoking and the risk of type 2 diabetes: a systematic review
and meta-analysis,” Jama, vol. 298, no. 22, pp. 2654 -2664, 2007.
VII. D. B. Carr and S. Gabbe, “Gestational diabetes: detection,
management, and implications,” Clinical Diabetes, vol. 16, no. 1,
pp. 4-12, 1998.
IX. Deliege, A. Cioppa, and M. Van Droogenbroeck, “Hitnet: a neural
network with capsules embedded in a hit-or-miss layer, extended
with.
X. D. K. Choubey, S. Paul, S. Kumar, and S. Kumar, “Classification of
pima indian diabetes dataset using naive bayes with genetic
algorithm as an attribute selection,” in Communication and
Computing Systems: Proceedings of the International Conference
on Communication and Computing System (ICCCS 2016), pp. 451-
455, 2017.

XI. Dolz, X. Xu, J. Rony, J. Yuan, Y. Liu, E. Granger, C. Desrosiers, X.
Zhang, I. B. Ayed, and H. Lu, “Multi-region segmentation of
bladder cancer structures in mri with progressive dilated
convolutional networks,” arXiv preprint arXiv:1805.10720, 2018.
XII. dos Santos and M. Gatti, “Deep convolutional neural networks for
sentiment analysis of short texts,” in Proceedings of COLING 2014,
the 25th International Conference on Computational Linguistics:
Technical Papers, pp. 69-78, 2014.
XIII. D. Rawlinson, A. Ahmed, and G. Kowadlo, “Sparse unsupervised
capsules generalize better,” arXiv preprint arXiv:1804.06094, 2018.
XIV. E. Alpaydin, “Introduction to machine learning”. MIT press, 2014.
XV. E. Xi, S. Bing, and Y. Jin, “Capsule network performance on
complex data,” arXiv preprint arXiv:1712.03480, 2017.
XVI. F. Liang, H. Liu, X.Wang, and Y. Liu, “Hyperspectral image
recognition based on artificial neural network,” NeuroQuantology,
vol. 16, no. 5, 2018.
XVII. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally,
and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x
fewer parameters and< 0.5 mb model size,” arXiv preprint
arXiv:1602.07360, 2016.
XVIII. H. Uemura, A. A. Ghaibeh, S. Katsuura-Kamano, M. Yamaguchi,
T. Bahari, M. Ishizu, H. Moriguchi, and K. Arisawa, “Systemic in
ammation and family history in relation to the prevalence of type 2
diabetes based on an alternating decision tree,” Scientific reports,
vol. 7, p. 45502, 2017.
XIX. H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes
mellitus prediction model based on data mining,” Informatics in
Medicine Unlocked, vol. 10, pp. 100-107, 2018.
XX. H. Ze, A. Senior, and M. Schuster, “Statistical parametric speech
synthesis using deep neural networks,” in Acoustics, Speech and
Signal Processing (ICASSP), 2013 IEEE International Conference
on, pp. 7962{7966, IEEE, 2013.
XXI. Jaiswal, W. AbdAlmageed, and P. Natarajan, “Capsulegan:
Generative adversarial capsule network,” arXiv preprint
arXiv:1802.06167, 2018.
XXII J. Li, G. Li, and H. Fan, “Image dehazing using residual-based deep
cnn,” IEEE Access, 2018.
XXIII J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,”
IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3,
pp. 665-685, 1993.

XXIV Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas,
and I. Chouvarda, “Machine learning and data mining methods in
diabetes research,” Computational and structural biotechnology
journal, vol. 15, pp. 104-116, 2017.
XXV Khan, I. Yaqoob, I. A. T. Hashem, Z. Inayat, M. Ali, W.
Kamaleldin, M. Alam, M. Shiraz, and A. Gani, “Big data: survey,
technologies, opportunities, and challenges,” The Scientific World
Journal, vol. 2014, 2014.
XXVI K. Kayaer and T. Yldrm, “Medical diagnosis on pima indian
diabetes using general regression neural networks,” in Proceedings
of the international conference on artificial neural networks and
neural information processing (ICANN/ICONIP), pp. 181-184,
2003.
XXVII K. Polat and S. Gunes, “An expert system approach based on
principal component analysis and adaptive neuro-fuzzy inference
system to diagnosis of diabetes disease,” Digital Signal Processing,
vol. 17, no. 4, pp. 702-710, 2007.
XXVIII K. Polat, S. Gunes, and A. Arslan, “A cascade learning system for
classification of diabetes disease: Generalized discriminant analysis
and least square support vector machine,” Expert systems with
applications, vol. 34, no. 1, pp. 482-487, 2008.
XXIX L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement
learning: A survey,” Journal of artificial intelligence research, vol.
4, pp. 237-285, 1996.
XXX M. Engelin, “Capsnet comprehension of objects in different
rotational views,” divaportal.org, 2018.
XXXI M. Fatima and M. Pasha, “Survey of machine learning algorithms
for disease diagnostic,” Journal of Intelligent Learning Systems and
Applications, vol. 9, no. 01, p. 1, 2017.
XXXII M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman,
“Reading text in the wild with convolutional neural networks,”
International Journal of Computer Vision, vol. 116, no. 1, pp. 1-20,
2016.
XXXIII M. Singh, “Classification of diabetic retinopathy stages using deep
learning,” Scientific reports, 2018.
XXXIV P. Afshar, A. Mohammadi, and K. N. Plataniotis, “Brain tumor type
classification via capsule networks,” arXiv preprint
arXiv:1802.10200, 2018.

XXXV Q.Wang, T. Ruan, Y. Zhou, D. Gao, and P. He, “An attention-based
bi-gru-capsnet model for hypernymy detection between compound
entities,” arXiv preprint arXiv:1805.04827, 2018.
XXXVI Q. Xuan, H. Xiao, C. Fu, and Y. Liu, “Evolving convolutional
neural network and its application in fine-grained visual
categorization,” IEEE Access, 2018.
XXXVII R. Johnson and T. Zhang, “Effective use of word order for text
categorization with convolutional neural networks,” arXiv preprint
arXiv:1412.1058, 2014.
XXXVIII R. J. Williams and D. Zipser, “A learning algorithm for continually
running fully recurrent neural networks,” Neural computation, vol.
1, no. 2, pp. 270-280, 1989.
XXXIX R. LaLonde and U. Bagci, “Capsules for object segmentation,”
arXiv preprint arXiv:1804.04241, 2018.
XL. Safran, M. Bloomrosen, W. E. Hammond, S. Labkoff, S. Markel-
Fox, P. C. Tang, and D. E. Detmer, “Toward a national framework
for the secondary use of health data: an american medical
informatics association white paper,” Journal of the American
Medical Informatics Association, vol. 14, no. 1, pp. 1-9, 2007.
XLI. S. Aich and I. Stavness, “Object counting with small datasets of
large images,” arXiv preprint arXiv:1805.11123, 2018.
XLII. Schmidhuber, “Deep learning in neural networks: An overview,”
Neural networks, vol. 61, pp. 85-117, 2015.
XLIII. S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
XLIV. S. Ibrahim, P. Chowriappa, S. Dua, U. R. Acharya, K. Noronha, S.
Bhandary, and H. Mugasa, “Classification of diabetes maculopathy
images using data-adaptive neuro-fuzzy inference classifier,”
Medical & biological engineering & computing, vol. 53, no. 12, pp.
1345-1360, 2015.
XLV. S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between
capsules,” in Advances in Neural Information Processing Systems,
pp. 3856-3866, 2017.
XLVI. S. Valverde, M. Salem, M. Cabezas, D. Pareto, J. C. Vilanova, L.
Ramio-Torrenta, A. Rovira, J. Salvi, A. Oliver, and X. Llado, “Oneshot
domain adaptation in multiple sclerosis lesion segmentation
using convolutional neural networks,” arXiv preprint
arXiv:1805.12415, 2018.
XLVII. T. Deshmukh and H. Fadewar, “Fuzzy deep learning for diabetes
detection,” in Computing, Communication and Signal Processing,
pp. 875-882, Springer, 2019.

XLVIII. W. Liu, E. Barsoum, and J. D. Owens, “Object localization and
motion transfer learning with capsules,” arXiv preprint
arXiv:1805.07706, 2018.
XLIX. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural
networks,” in Proceedings of the fourteenth international conference
on artificial intelligence and statistics, pp. 315-323, 2011.
L. X. Li, T. Wu, X. Song, and H. Krim, “Aognets: Deep and-or
grammar networks for visual recognition,” arXiv preprint
arXiv:1711.05847, 2017.
LI. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based
learning applied to document recognition,” Proceedings of the
IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
LII. Y. Sebastian, X. T. Tiong, V. Raman, A. Y. Y. Fong, and P. H. H.
Then, “Advances in diabetes analytics from clinical and machine
learning perspectives,” International Journal of Design, Analysis
and Tools for Integrated Circuits and Systems, vol. 6, no. 1, pp. 32-
37, 2017.
LIII. Y. Wang, A. Sun, J. Han, Y. Liu, and X. Zhu, “Sentiment analysis
by capsules,” in Proceedings of the 2018 World Wide Web
Conference on World Wide Web, pp. 1165-1174, International
World Wide Web Conferences Steering Committee, 2018.
LIV. Y. Wang, W. Ke, and P. Wan, “A method of ultrasonic image
recognition for thyroid papillary carcinoma based on deep
convolution neural network,” NeuroQuantology, vol. 16, no. 5,
2018.
LV. Zahangir Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K.
Asari, “Recurrent residual convolutional neural network based on unet
(r2u-net) for medical image segmentation,” arXiv preprint
arXiv:1802.06955, 2018.
LVI. Z. Chen and D. Crandall, “Generalized capsule networks with
trainable routing procedure,” arXiv preprint arXiv:1808.08692,
2018.
LVII. Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to
diagnose with lstm recurrent neural networks,” arXiv preprint
arXiv:1511.03677, 2015.

View Download

MANET protocol with Ant colony optimization for real time applications

Authors:

Pavan kumar Panakanti

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00035

Abstract:

Mobile ad-hoc network is a most significant job in military applications since it is explicitly planned system for on demand requirement and in circumstances where set up of physical system is beyond the realm of imagination. This special kind of network which takes control in infrastructure less correspondence handles genuine difficulties carefully, for example, exceedingly hearty and dynamic military work stations, devices and littler sub-arranges in the front line. In this manner, there is an intense interest of planning productive directing conventions guaranteeing security and unwavering quality for fruitful transmission of exceedingly touchy and secret military data in guard systems. With this target, a power effective system layer directing convention in the system for military application is structured and mimicked utilizing another cross layer approach of configuration to expand unwavering quality and system lifetime up to a more prominent degree. But here PDO-AODV approach not supports to optimal path selection. So we propose a new ACO-DAEE (Ant colony optimization with delay aware energy efficient) for optimal path selection and mitigating the delay time in network system. The main goal is to maintain the optimal routes in network, during data transmission in an efficient manner. Our simulation results indicate that ACO-ADEE performs extremely well in terms of packet delivery ratio, end to end delay, and throughput. Simulation results through NS2 software to verify the effectiveness of our method.

Keywords:

Mobile ad-hoc network,ACO-DAEE,optimal path selection,NS2 software,

Refference:

I. Agbaria, A.; Gershinsky, G.; Naaman N. & Shagin,K. Extrapolationbased
and QoS-aware real-timecommunication in wireless mobile ad hoc
networks. Inthe 8th IFIP Annual Mediterranean Adhoc
NetworkingWorkshop, Med-Hoc-Net 2009. pp.21-26.doi:
10.1109/MEDHOCNET.2009.5205201
II. Ahmed, M.; Elmoniem, Abd; Ibrahim, Hosny M.;Mohamed, Marghny
H. &Hedar, Abdel Rahman. Antcolony and load balancing optimizations
for AODVrouting protocol. Int. J. Sensor Networks Data
Commun.,2012, 1.doi: doi:10.4303/ijsndc/X110203.
III. Amulya Boyina, K. Praveen Kumar “Active Coplanar Wave guide Fed
Switchable Multimode Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-14,
No.-4, July-August (2019) pp 188-196.
IV. B. Venkateswar Rao, Praveen Kumar Kancherla, Sunita Panda
“Multiband slotted Elliptical printed Antenna Design and Analysis”
Journal Of Mechanics Of Continua And Mathematical Sciences
(JMCMS), Vol.-14, No.-4, July-August (2019) pp 378-386.

V. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VI. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VII. K.Praveen Kumar, “Design of 3D EBG for L band Applications” IEEE
International conference on communication technology ICCT-April-
2015. Noor Ul Islam University Tamilnadu.
VIII. K.Praveen Kumar, Dr. Habibulla Khan ” Active progressive stacked
electromagnetic band gap structure (APSEBG) structure design for low
profile steerable antenna applications” International Conference on
Contemporary engineering and technology 2018 (ICCET-2018) March
10 – 11, 2018. Prince Shri Venkateshwara Padmavathy Engineering
College, Chennai.
IX. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced
research in dynamical and control systems, Vol-10, Special issue-03,
2018.
X. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization of
Optimized stacked electromagnetic band gap ground plane for low
profile patch antennas” International journal of pure and applied
mathematics, Vol 118, No. 20, 2018, 4765-4776.
XI. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structures
for Mutual coupling reduction in antenna arrays; A comparative study”
International Conference on Contemporary engineering and technology
2018 (ICCET-2018) March 10 – 11, 2018. Prince Shri Venkateshwara
Padmavathy Engineering College, Chennai
XII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XIII. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XIV. K.Praveen Kumar, Dr Habibulla Khan ” The surface properties of
TMMD-HIS material; a measurement” IEEE International conferance on
electrical, electronics, signals, communication & optimization EESCO –
January 2015

XV. K.Praveen Kumar, “Effect of 2DEBG structure on Monopole Antenna
Radiation and Analysis of It’s characteristics” IEEE International
conference on communication technology ICCT-April-2015. Noor Ul
Islam University Tamilnadu.
XVI. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna Design
for C-Band Applications”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-8
June, 2019.
XVII. K.Praveen Kumar, “Mutual Coupling Reduction between antenna
elements using 3DEBG” IEEE International conference on
communication technology ICCT-April-2015. Noor Ul Islam University
Tamilnadu.
XVIII. K.Praveen Kumar, “The surface properties of TMMD-HIS material; A
measurement” IEEE International conference on communication
technology ICCT-April-2015. Noor Ul Islam University Tamilnadu.
XIX. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XX. K Satish Reddy a, K Praveen Kumar, Habibulla Khan, Harswaroop
Vaish “Measuring the surface properties of a Novel 3-D Artificial
Magnetic Material” 2nd International Conference on Nanomaterials and
Technologies (CNT 2014), Elsevier Procedia material Science.
XXI. Kumaraswami Gajula, Amulya Boyina, K. Praveen Kumar “Active Quad
band Antenna Design for Wireless Medical and Satellite Communication
Applications” Journal Of Mechanics Of Continua And Mathematical
Sciences (JMCMS), Vol.-14, No.-4, July-August (2019) pp 239-252.
XXII. Mamata Rath, Binod Kumar Pattanayak and Bidudhendu Pati, – Energy
efficient MANET Protocol using Cross Layer Design for Military
Applications -, vol.66, no.2, March 2016, pp.146-150.
XXIII. Mamata Rath, Binod Kumar, Pattanayak and Bibudhendu Pati, – Energy
efficient MANET Protocol Using Cross Layer Design for Military
Applications -, Vol.66.2, March 2016, pp.146-150,
DOI:10.14429/dsj.66.9705.
XXIV. Siva, K. & P. Duraiswamy, K. A QoS routing protocol formobile ad hoc
networks based on the load distribution. Inthe IEEE International
Conference on ComputationalIntelligence and Computing Research
(ICCIC), 2010,pp.1-6.doi: 10.1109/ICCIC.2010.5705724.
XXV. Srivastava, S.; Daniel, A.K.; Singh, R. & Saini, J.P.
Energyefficientposition based routing protocol for mobile ad
hocNetworks. In the IEEE International Conference on
RadarCommunication and Computing (ICRCC), 2012, pp.18-23.doi:
10.1109/ICRCC.2012.6450540.

 

View Download

Despeckling SAR Images Thought Nest ESA Tool

Authors:

G. Siva Krishna, Shobini.B, N.Prakash

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00036

Abstract:

The Synthetic Aperture Radar (SAR) usually corrupted by some surplus speckle formed. These speckles having multiplicative noise, which appears likes a grainy pattern in the SAR image. This performs an accurate interpretation of SAR images. The aim of this work was to remove the noise and the accurate classifying the LULC facts with quality evolution with statistical operations. The SAR images to play an import key role on Earth Observation applications using high resolution for allweather conditions and all times. These Radar satellite collecting images have noise. To despeckle the noise, we propose the NEST Tool. Using this tool we (statistical operations) subtract band wise noisily one. The experiment results are better performance from the state of art techniques.

Keywords:

Despeckle learning,SAR,radar,noise,nest tool,

Refference:

I. C.A. Deledalle, L. Denis, and F. Tupin, “How to compare noisy
patches? patch similarity beyond gaussian noise,” International Journal
of Computer Vision, vol. 99, pp. 86–102, 2012.

II. C.A. Deledalle, L. Denis, and F. Tupin, “Iterative weighted maximum
likelihood denoising with probabilistic patch-based weights,” IEEE
Trans. on Image Process., vol. 18, no. 12, pp. 2661–2672, 2009.
III. C.A. Deledalle, L. Denis, M. Jager, A. Reigber, and F. Tupin, ¨ “NLSAR:
A unified nonlocal framework for resolution preserving
(Pol)(In)SAR denoising,” IEEE Trans. Geosci. Remote Sens., vol. 53,
no. 4, pp. 2021–2038, 2015.
IV. C.V. Angelino S. Parrilli, M. Poderico, and L. Verdoliva, “A nonlocal
SAR image denoising algorithm based on LLMMSE wavelet shrinkage,”
IEEE Trans. Geosci. Remote Sens., vol. 50, no. 2, pp. 606–616, Feb.
2012.
V. D. Cozzolino, S. Parrilli, G. Poggi, , G. Scarpa and L. Verdoliva, “Fast
adaptive nonlocal SAR despeckling,” IEEE Geoscience and Remote
Sensing Letters, vol. 11, no. 2, pp. 524– 528, 2014.
VI. D.P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
in International Conf. on Learning Representations (ICLR), 2015.
VII. F. Argenti, L. Alparone, T. Bianchi and A. Lapini, “A tutorial on speckle
reduction in synthetic aperture radar images,” IEEE Geosci. Remote
Sens. Mag., vol. 1, pp. 6–35, 2013.
VIII. G. Chierchia, D. Cozzolino, and G.Poggi, “SAR image despeckling
through convolutional neural networks,” IEEE Geosci. Remote Sens.,
vol. 1, pp.5438–5441, 2017.
IX. H.C. Burger, S. Harmeling, and C.J. Schuler, “Image denoising: Can
plain neural networks compete with BM3D?” in IEEE CVPR, 2012, pp.
2392–2399.
X. https://earth.esa.int/documents/507513/1077939/nest
XI. Jain and S. Seung, “Natural image denoising with convolutional
networks,” in Advances in Neural Information Processing Systems,
2009, pp. 769–776.
XII. K. He, S. Ren X. Zhang, and J. Sun, “Deep Residual Learning for Image
Recognition,” in IEEE CVPR, 2016, pp. 770–778.
XIII. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XIV. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.

XV. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure design
for low profile steerable antenna applications” Journal of advanced
research in dynamical and control systems, Vol-10, Special issue-03,
2018.
XVI. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XVII. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression band,
In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XVIII. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XIX. L. Alparone, F. Argenti, and T. Bianchi, “Segmentation-Based MAP
Despeckling of SAR Images in the Undecimated Wavelet Domain,”
IEEE Trans. Geosci. Remote Sens., vol. 46, no. 9, pp. 2728–2742, 2008.
XX. S. Foucher, “SAR image filtering via learned dictionaries and sparse
representations,” in IEEE IGARSS, 2008, pp. 229–232.
XXI. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep
Network Training by Reducing Internal Covariate Shift,”
arXiv:1502.03167 v3, 2015.
XXII. Y. Chen, D. Meng K. Zhang, L. Zhang, and W. Zuo, “Beyond a
Gaussian Denoiser: Residual Learning of Deep CNN for Image
Denoising,” arXiv:1608.03981v1, 2016.

View Download

Design of 5-Stage Ring Oscillator using Mentor Graphics 130nm Technology

Authors:

Kumaraswamy Gajula

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00037

Abstract:

The design of layout and source VLSI by way of low design rules is a difficult assignment before fabricating required device. A RF integrated circuit contains extensive applications is Ring Oscillator (RO). Current article focus a novel method of design, where a ring oscillator (RO) is simulated with Layout versus Source(LVS) report for physical verification using mentor graphics with Pyxis schematic, ELDOsimulation, EzWaves, Pyxis Layout and Calibre tools. Here RO circuit is designed with inverters of 5 stages operating at 9 GHz with the boundaries obligatory by gdk Generic 13 library. Simulated results, schematic, layout with LVS reports are presented here to verify design of RO with Mentor graphics EDA back end tool in efficient manner compared to Cadence.

Keywords:

Layout Vs Schematic (LVS),Inverter,Oscillator,Mentor Graphics,

Refference:

I. A. HAJIMIRI, S. LIMOTYRAKIS, T. LEE,‟Jitter and Phase Noise in
Ring Oscillators”, IEEE Journal of Solid State Circuits, vol.34, 6,
(1999), 790-804.
II. Asad A. Abidi, “Phase Noise and Jitter in CMOS Ring Oscillators,”
IEEE Journal Of Solid-State Circuits, Vol. 41, No. 8, August 2006.
III. G. JOVANOVI´C , M. STOJˇCEV, “A Method for Improvement
Stability of a CMOS Voltage Controlled Ring Oscillators”, ICEST 2007,
Proceedings of Papers, vol. 2, pp. 715-718, Ohrid, Jun 2007.

IV. MATSUDA, T. et al.‟ A combined test structure with ring oscillator and
inverter chain for evaluating optimum high-speed/low-power operation”.
In proceeding of International Conference on Microelectronic Test
Structures, 2003.
IV. M K Mandal and B C Sarkar,“Ring oscillators: Characteristics and
applications”, Indian journal of pure and applied physics, vol 48, pp 136-
145, 2010.
V. P. M. Farahabadi, H. Miar-Naimi and A. Ebrahimzadeh, “A New
Solution to Analysis of CMOS Ring Oscillators” Iranian Journal of
Electrical & Electronic Engineering, Vol. 5, No. 1, March 2009.
VI. Prakash Kumar Rout, Debiprasad Priyabrata Acharya, “Design of
CMOS Ring Oscillator Using CMODE” National Institute of
Technology, Rourkela, Orissa, India ,pp.1-6,2011.
VII. S. DOCKING, AND M. SACHDEV, ‟An Analytical Equation for the
Oscillation Frequency of High-Frequency Ring Oscillators”, IEEE
Journal of Solid State Circuits, vol.39, 3, (2004), 533-537.
VIII. S. Docking, M. Sachdev, ‟A Method to Derive an Equation for the
Oscillation Frequency of a Ring Oscillator”, IEEE Trans. on Circuits and
Systems – I: Fundamental Theory and Applications, vol. 50, 2,(2003),
259-264.
IX. SEGURA, J., HAWKINS, C.F. ‟CMOS electronics, how it works, how it
fails (ch. 4)”. Book IEEE edition, ISBN 0- 471-47669-2, 2004.
X. Sushil Kumar and Dr. Gurjit Kaur, “Design and performance analysis of
nine stages cmos based ring oscillator” International Journal Of VLSI
Design & Communication Systems (VLSICS),Vol.3, No.3, June 2012.
XII. Vandna Sikarwar,Neha Yadav, Shyam Akashe, ‟Design and analysis of
CMOS ring oscillator using 45nm technology”, 2013 3rd IEEE
International Advance Computing Conference (IACC).

View Download

Digital Beam forming Algorithms for Radar Applications

Authors:

Sri Bindu. Sattu

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00038

Abstract:

Beam forming can be achieved by combining elements in an array in way that certain angles get constructive interference and some destructive interference, which can be utilized for both transmitter and receiver ends so that they can achieve spatial selectivity. Combination of antenna and digital technology as Digital Beam Forming (DBF) which was developed by workers in sonar and Radar systems which was enhanced by development of aperture synthesis methods leading to modern dipolar arrays improvement. Converting RF signals into cos and sin signals representing amplitude and phase values which are combined to get desired output this is done by converting analog signal into digital. Antenna is considered as a device which converts spatio signals into strictly temporal signals which makes it helpful for various signal processing techniques.

Keywords:

Beam Forming (DBF),Radar systems,signal processing techniques,

Refference:

I. Amulya Boyina, K. Praveen Kumar “Active Coplanar Wave guide Fed
Switchable Multimode Antenna Design and Analysis” Journal Of
Mechanics Of Continua And Mathematical Sciences (JMCMS), Vol.-
14, No.-4, July-August (2019) pp 188-196
II. B. Venkateswar Rao, Praveen Kumar Kancherla, Sunita Panda
“Multiband slotted Elliptical printed Antenna Design and Analysis”
Journal Of Mechanics Of Continua And Mathematical Sciences
(JMCMS), Vol.-14, No.-4, July-August (2019) pp 378-386
III. Chou, H.T., Chang, C.H., Chen, Y.T.: Ferrite circulator integrated
phased array antenna module for dual-link beamforming at millimeter
frequencies. IEEE Trans. Antennas Propag. 10, 1–9 (2018)

IV. H. Shokri-Ghadikolaei, F. Boccardi, C. Fischione, G. Fodor, and M.
Zorzi, “Spectrum sharing in mmwave cellular networks via cell
association, coordination, and beamforming,” IEEE Journal on
Selected Areas in Communications, vol. 34, no. 11, pp. 2902–2917,
2016
IV. J. Bechter, K. Eid, F. Roos, and C. Waldschmidt, “Digital
beamforming to mitigate automotive radar interference,” in Proc. IEEE
MTT-S Int. Conf. Microw. Intell. Mobility, May 2016, pp. 1–4.
V. Kim, K.H. Kim, H., Kim, D.Y., Kim, S.K., Chun, S.H., Park, S.J.,
Jang, S.M., Chong, M.K. Jin, H.S.: Development of planar active
phased array antenna for detecting and tracking radar. In: Proceedings
of IEEE Radar Conference (RadarConf’18), Oklahoma City, April 23–
27, pp. 0100–0103, 2018.
VI. K.Praveen Kumar, “Active Switchable Band-Notched UWB Patch
Antenna”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
VII. K.Praveen Kumar, “Circularly Polarization of Edge-Fed Square Patch
Antenna using Truncated Technique for WLAN Applications”,
International Journal of Innovative Technology and Exploring
Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
IX. K.Praveen Kumar, “Design of 3D EBG for L band Applications”
IEEE International conference on communication technology ICCTApril-
2015. Noor Ul Islam University Tamilnadu.
X. K.Praveen Kumar, Dr. Habibulla Khan ” Active progressive stacked
electromagnetic band gap structure (APSEBG) structure design for low
profile steerable antenna applications” International Conference on
Contemporary engineering and technology 2018 (ICCET-2018) March
10 – 11, 2018. Prince Shri Venkateshwara Padmavathy Engineering
College, Chennai.
XI. K.Praveen Kumar, Dr. Habibulla Khan “Active PSEBG structure
design for low profile steerable antenna applications” Journal of
advanced research in dynamical and control systems, Vol-10, Special
issue-03, 2018.
XII. K.Praveen Kumar, Dr. Habibulla Khan, “Design and characterization
of Optimized stacked electromagnetic band gap ground plane for low
profile patch antennas” International journal of pure and applied
mathematics, Vol 118, No. 20, 2018, 4765-4776.
XIII. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG
structures for Mutual coupling reduction in antenna arrays; A
comparative study” International Conference on Contemporary
engineering and technology 2018 (ICCET-2018) March 10 – 11, 2018.
Prince Shri Venkateshwara Padmavathy Engineering College, Chennai.

XIV. K.Praveen Kumar, Dr. Habibulla Khan “Optimization of EBG structure
for mutual coupling reduction in antenna arrays; a comparitive study”
International Journal of engineering and technology, Vol-7, No-3.6,
Special issue-06, 2018.
XV. K.Praveen Kumar, Dr Habibulla Khan ” Surface wave suppression
band, In phase reflection band and High Impedance region of 3DEBG
Characterization” International journal of applied engineering research
(IJAER), Vol 10, No 11, 2015.
XVI. K.Praveen Kumar, Dr Habibulla Khan ” The surface properties of
TMMD-HIS material; a measurement” IEEE International conferance
on electrical, electronics, signals, communication & optimization
EESCO – January 2015.
XVII. K.Praveen Kumar, “Effect of 2DEBG structure on Monopole Antenna
Radiation and Analysis of It’s characteristics” IEEE International
conference on communication technology ICCT-April-2015. Noor Ul
Islam University Tamilnadu.
XVIII. K.Praveen Kumar, Kumaraswamy Gajula “Fractal Array antenna
Design for C-Band Applications”, International Journal of Innovative
Technology and Exploring Engineering (IJITEE), Volume-8 Issue-8
June, 2019.
XIX. K.Praveen Kumar, “Mutual Coupling Reduction between antenna
elements using 3DEBG” IEEE International conference on
communication technology ICCT-April-2015. Noor Ul Islam
University Tamilnadu.
XX. K.Praveen Kumar, “The surface properties of TMMD-HIS material; A
measurement” IEEE International conference on communication
technology ICCT-April-2015. Noor Ul Islam University Tamilnadu.
XXI. K.Praveen Kumar, “Triple Band Edge Feed Patch Antenna; Design and
Analysis”, International Journal of Innovative Technology and
Exploring Engineering (IJITEE), Volume-8 Issue-8 June, 2019.
XXII. K Satish Reddy a, K Praveen Kumar, Habibulla Khan, Harswaroop
Vaish “Measuring the surface properties of a Novel 3-D Artificial
Magnetic Material” 2nd International Conference on Nanomaterials
and Technologies (CNT 2014), Elsevier Procedia material Science.
XXIII. Kumaraswami Gajula, Amulya Boyina, K. Praveen Kumar “Active
Quad band Antenna Design for Wireless Medical and Satellite
Communication Applications” Journal Of Mechanics Of Continua And
Mathematical Sciences (JMCMS), Vol.-14, No.-4, July-August (2019)
pp 239-252.

XXIV. M. A. Vazquez, L. Blanco, and A. I. P ´ erez-Neira, “Hybrid analog–
digital ´ transmit beamforming for spectrum sharing backhaul
networks,” IEEE transactions on signal processing, vol. 66, no. 9, p.
2273, 2018.
XXV. M. Rameez, M. Dahl, and M. I. Pettersson, “Adaptive digital
beamforming for interference suppression in automotive FMCW
radars,” in Proc. IEEE Radar Conf., Apr. 2018, pp. 252–256.
XXVI. P. Kumari, M. E. Eltayeb, R. W. Heath, “Sparsity-aware adaptive
beamforming design for IEEE 802.11ad-based joint communicationradar”,
Accepted to IEEE Radar Conference (RdarConf), pp. 4281-
4285, March 2018.
XXVII. W. Zhang and Z. S. He, “Comments on ‘Waveform optimization for
transmit beamforming with MIMO radar antenna array,”’ IEEE Trans.
Antennas Propag., vol. 66, no. 9, Sep. 2016.

View Download