Volume-7 , Issue-6 , Jun - 2019 Go Back

Information and Commutation Technology (ICT) is the most important strategic issue for any organization and in the age of digital industrialization, every establishment uses the ICT for running their business therefore they are more depended on network. Increasingly uses of ICT posing new security challenges. Network infrastructure is essential to enable the network for communication. All devices in network are equally important to run the network and in failure of any one device may have devastating effect on people, economy, government services and national security. In this hyper-connected world, protecting our network and data from unauthorized access are big challenge. Process of protecting network infrastructures and data from external destructive threats and intrusion is known as network security. Network security for every organization must be consider as essential for functioning of network system and must be dealt with proactive and timely manner. For the above said purposes it is essential to have a dedicated network security system which can perform security function viz. firewall, intrusion detection and prevention, antivirus etc. In this paper we are discussing about the implementation of Unified Threat Management (UTM) Firewall system for network security management and protecting the Network for CWPRS Local Area Network (CLAN). The UTM Firewall has been successfully implemented at CWPRS for indentifying and protecting the Network from internal and external threats.

Network security, Network threats, UTM Firewall, ICT, Internet

[1] Elizabeth D. Zwicky, Simon Cooper & D. Brent Chapman, “Building Internet Firewalls”, Second Edition, June 2000, ISBN: 1-56592-871-7, page no. 1.
[2] Thaier Hayajneh, Bassam J. Mohd , Awni Itradat, and Ahmad Nahar Quttoum “Performance and Information Security Evaluation with Firewalls”, International Journal of Security and Its Applications,Vol.7, No.6 (2013), pp.355-372.
[3] https://blogs.cisco.com/smallbusiness/the-10-most-common-security-threats-explained
[4] http:cwprs.gov.in website
[5] Cert-In, Information Security Policy for protection of critical information and infrastructure CERT-in/NISAP/01, issue 01, May 2006

Pratap Singh Solanki, P. R. Khatarkar, "Network Security Management and Protection using UTM Firewall," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1055-1058, 2019.

Biological creations adapt to environmental changes. Similarly, can autonomous AI system adapt to the environmental changes? During natural disasters such as floods or cyclones, an autonomous robot might unexpectedly face new conditions such as occlusions from dust, and hence may need to adapt itself. Is it possible for a drone flying into a disaster zone to autonomously evolve itself without any human guidance. Many times autonomous AI systems may be exposed to new conditions that it hasn’t yet been trained. How to provision full autonomy to such autonomous AI?. This is the challenge this paper answers. Disruptions in internet connectivity during disasters add an additional dimension to this challenge. How does the AI on drone self-adapt during disasters? Is it possible to employ Neural Architecture Search (NAS) for autonomously evolving the drone’s intelligence to the new environment?. With internet outages during disasters, is it possible to evolve the AI by evolving the model locally on the drone?. In short, this paper explores how to design autonomous drones that can triumph over disasters, by autonomous evolving the drone intelligence to the new environment using NAS.

Artificial Intelligence, Autonomous systems, Neural Architecture Search, AutoML, Transfer Learning

[1] Zoph, Barret, Vasudevan, Vijay, Shlens, Jonathon, and Le, Quoc V.
“Learning transferable architectures for scalable image recognition”. CVPR, 2018.
[2] Pham, Hieu, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean. "Efficient neural architecture search via parameter sharing." arXiv, 1802.03268, 2018.
[3] Stanley,Jeff, Joel, Risto, "Designing neural networks through neuro evolution.", Nature Machine Intelligence, no.1, pp.24-35, 2019
[4] He et al. "Amc: Automl for model compression and acceleration on mobile devices.", ECCV, pp. 784-800. 2018.
[5] Tan, Mingxing, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V. Le. "Mnasnet: Platform-aware neural architecture search for mobile.", arXiv, 1807.11626, 2018.
[6] Liu et al. "Progressive neural architecture search." , ECCV, pp. 19-34. 2018.
[7] Hundt, Andrew, Varun, Hager, "sharpDARTS: Faster and More Accurate Differentiable Architecture Search." arXiv, 1903.09900 , 2019.
[8] Dong, Xuanyi, Yi, "Searching for a robust neural architecture in four gpu hours.", CVPR, vol. 1, 2019.
[9] Miikkulainen et al. "Evolving deep neural networks.", Artificial Intelligence in the Age of Neural Networks and Brain Computing, pp. 293-312, Academic Press, 2019.
[10] Liang, Jason, Elliot, Babak, Dan , Karl , Miikkulainen. "Evolutionary Neural AutoML for Deep Learning.", arXiv, 1902.06827, 2019.
[11] Elsken, Thomas, Metzen, Frank, "Neural Architecture Search: A Survey.", Journal of Machine Learning Research 20, no. 55, pp.1-21, 2019
[12] Parimala, Rajkumar, Ruba, Vijayalakshmi, "Challenges and Opportunities with Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16-20, 2017
[13] S. Verma, S. K. Rathi, V. S. Rathore , "Earth Observation Satellites Series and its Potentialities", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.49-55, 2017
[14] Delmerico et al, "Are we ready for autonomous drone racing? the UZHFPV drone racing dataset.", ICRA, 2019.
[15] Tamaazousti et all. "Learning more universal representations for transfer-learning." IEEE transactions on pattern analysis and machine intelligence, 2019

Rajagopal. A, Nirmala. V, "Autonomous Self-evolution of AI on drones: Transfer Learning of Neural Architecture Search’s brain," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1059-1064, 2019.

Now a day’s capturing a live images with high quality plays an crucial role in all the fields. It is more important as far as security in military, commercial, household fields as well as to monitor the continuous changes in earth surfaces are concern. Most of the time to achieve clear images we have to differentiate between original object and shadow as detecting objects under the influence of shadow is a challenging task. In urban area the shadow produces artificial color features and shape deformation of objects which decays the quality of image. Shadow mainly occurs due to elevate objects and If light source has been blocked by some obstacles. However, a lot of shadowed areas in remote sensing images of urban areas have affected the tasks, such as image classification, object detection and recognition. Tsai presented an efficient algorithm which uses the ratio value of the hue over the intensity to construct the ratio map for detecting shadows of color aerial images. Instead of only using the global thresholding process in Tsai’s algorithm, this paper presents a novel successive thresholding scheme (STS) to detect shadows more accurately. By performing the global thresholding process on the modified ratio map, a coarse-shadow map is constructed to classify the input color aerial image into the shadow pixels and the non-shadow pixels. Instead of only using the global thresholding process in Tsai’s algorithm, this paper presents a novel successive thresholding scheme (STS) to detect shadows more accurately. For the three four testing images, which contain some low brightness objects, our proposed algorithm has better shadow detection accuracy when compared with the previous shadow detection algorithms proposed by Tsai. Thus for the correct image interpretation it is important to detect shadow regions and restore their information. So it is very essential to detect the shadow regions and remove it effectively to get useful information with good quality.

Shadow detection method, Successive Thresholding Algorithm, Shadow removal, Otsu’s method, Image Segmentation, Tsai’s algorithm, Adaptive Histogram Equalization and Image Adjustment

[1] Hongya zhang, Kaimin sun, and Wenzhuo li,” Object-oriented Shadow Detection and Removal from Urban High-resolution Remote Sensing Images”, IEEE transactions on Geoscience and Remote Sensing, vol. 52, no. 11, november 2014.
[2] Kuo-Liang Chung, Yi-Ru Lin, and Yong-Huai Huang,”Efficient Shadow Detection of Color Aerial Images Based on Successive Thresholding Scheme”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 2, February 2009.
[3] Aliaksei Makarau, Rudolf Richter, Rupert Müller and Peter Reinartz,” Adaptive Shadow Detection Using a Blackbody Radiator Model”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, June 2011.
[4] P.S.Ramesh, S. Letitia, “A Novel Approach For Shadows Detection And Shadows Removal From High Resolution Satellite Images,” African Journal of Basic & Applied Sciences 9(4):243-250, 2017.
[5] Dong Cai , Manchun Li , Zhiliang Bao,”Study on Shadow Detection Method on High Resolution Remote Sensing Image Based on HIS Space Transformation and NDVI Index”, 18th International Conference on Geoinformatics, 18-20 June 2010.
[6] P. Sarabandi ,F. Yamazaki , M. Matsuoka,”Shadow Detection and Radiometric Restoration in Satellite High Resolution Images”. IEEE International Geoscience and Remote Sensing Symposium, 20-24 Sept. 2004
[7] Luca Lorenzi, Farid Melgani, and Grégoire Mercier,” A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, september 2012.
[8] Ling Zhang , Qing Zhang , Chunxia Xiao,” Shadow Remover: Image shadow removal based on illumination recovering optimization”. IEEE Transaction on Image Processing. Volume: 24 , Issue: 11 Year : 2015.
[9] Danang Surya Candra, Stuart Phinn, Peter Scarth,”Cloud and Cloud Shadow Removal Of Landsat 8 Images Using Multitemporal Cloud Removal Method”, 6th International Conference on Agro Geoinformatics 7-10 Aug. 2017.
[10] Shuang Luo , Huifang Li , Huanfeng Shen,”Shadow Removal Based on Clustering Correction of Illumination Field for Urban Aerial Remote Sensing Images”, IEEE International Conference on Image Processing (ICIP) Year: 2017.
[11] Vertika Jain , Ajay Khunteta,“Shadow Removal for Umbrageous Information Recovery in Aerial Images”. International Conference on Computer, Communications and Electronics (Comptelix) Year: 2017.
[12] Geethu Vijayan , S. R. Reshma , F. E. Dhanya ; S. Anju , Gayathri R. Nair ; R. P. Aneesh,” A Novel Shadow Removal Algorithm using Niblack Segmetation in Satellite Images,” International Conference on Communication Systems and Networks (ComNet) Year: 2016.
[13] Bin Pan ; Junfeng Wu ; Zhiguo Jiang ; Xiaoyan Luo,” Shadow Detection in remote Sensing Images based on Weighted Edge Gradient Ratio”, IEEE Geoscience and Remote Sensing Symposium Year: 2014.
[14] Nan Su ; Ye Zhang ; Shu Tian ; Yiming Yan ; Xinyuan Miao,” Shadow Detection and Removal for Occluded Object Information Recovery in Urban High-Resolution Panchromatic Satellite Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Year: 2016 , Volume: 9 , Issue: 6.
[15] Hongmei Zhu , Jihao Yin , Ding Yuan , Xiang Liu , Guangyun Zhang,”Dem-based shadow detection and removal forlunar craters”. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2016.
[16] Lei Ma , Bitao Jiang , Xinwei Jiang , Ye Tian,” Shadow removal in remote sensing images using features sample matting”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2015.
[17] W. Zhou, G. Huang, A. Tr oy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. E nv., vol. 113, no. 8, pp. 1769–1777, 2009.
[18] Victor J. D. Tsai,” A Comparative Study on Shadow Compensation of Color Aerial Images in Invariant Color Models”, IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, june 2006.
[19] Rafael C. Gonzalez, Richard E.Woods ,”Digital Image Processing”, Dorling Kindersley publisher, India.

Sadhana R. Sonvane, U.B. Solapurkar, "Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1065-1075, 2019.

This research presents a model for detecting pipeline vandalism in oil and gas sector. Feed-forward deep learning technique was applied. The methodology adopted the Rational Unified Process (RUP), Convolutional neural network and UML tools where applied for the system design. The architectural design consists of three input parameters stored in the hidden neurons, and one output. A back-propagation Convolutional neural network was used to train the parameters. The system was implemented using Hypertext Pre-processor (PHP) programming language. An input interactive interface was generated for predicting parameters threshold values for pipeline intrusion threat ranging from (0-18) pound by square inch(Psi) for threat while (19 and above Psi) for normal. Comparison has been carried out on the outcome between existing system and the proposed system. Results shown in the graph, denoting manual digging, pipeline leakage, walking on pipeline, and pressure. The intrusion point is indicated at line six in the result table where the pressure drops as a result of manual digging. The use of Convolutional neural network in pipeline surveillance system has shown that oil and gas pipeline intrusion can be monitored and controlled.

Vandalism, Prediction, Deep learning, Convolutional Neural Network, Pipeline, Surveillance

[1]. Schmidhuber, J., "Deep learning in neural networks: An overview." Neural networks, Vol.61, Issue.89, pp.85-117, 2015.
[2]. Krzysztof, J. C., “Deep neural networks – A brief historyCao Y, Chen Y and Khosla D. 2014. Spiking deep convolutional neural networks for energy -efficient object recognition”, Intern. Journal of Computer Vision. Vol.21, Issue.51, pp.100-350, 2017.
[3]. C. Adrian, Carlos S., Alejandro R. R., Pascual C., "A review of deep learning methods and applications for unmanned aerial vehicles.", Journal of Sensors, 2017.
[4]. O. G. Chinwe, E. N. Osegi., "An Integrative Systems Model for Oil and Gas Pipeline Data Prediction and Monitoring Using a Machine Intelligence and Sequence Learning Neural Technique.", Vol.6, pp.1-16, 2018.
[5]. Dehghan, Z. (2017).Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition, IEEE Trans. Med. Imag., 3(5),1332-1343.
[6]. Z. Yan, Y. Zhan, Z. Peng, S. Liao, Y. Shinagawa, S. Zhang, D. N. Metaxas, X. S. Zhou, "Multi-instance deep learning: Discover discriminative local anatomies for body part recognition." IEEE transactions on medical imaging, Vol.35, Issue.5, pp.1332-1343, 2016.
[7]. Yakubu AjijiMakeri, J. Technological Innovation in Crime Prevention in Nigeria. International Journal of Scientific Research in computer science and Engineering; sensor Vol.6, issue .6,pp 66-72.2018
[8]. TejashreePhatak, S.D. Sawarkar;Detection of Faulty Sensor Node within Wireless Sensor Network for improving Network Performance; International Journal of Scientific Research in Network Security and Communication; sensors Vol.5,issue.3.2017
[9]. EIA. Nigeria Country Outlook, Washington, EIA Publications. 2015.
[10]. K. Eero, K. Lukka, A. Siitonen., "The constructive approach in management accounting research." Journal of management accounting research Vol.5, Issue.1 pp.243-264, 1993.
[11]. F. Debo, C. Echem, A. Okoli, M. Mondanos, A. Bain, P. Carbonneau, A. Martey., "A Practical Application of Pipeline Surveillance and Intrusion Monitoring System in the Niger Delta: The Umugini Case Study." In SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers, 2017.
[12]. Korndoerfer, T. L., "Sustainable Development: A case study of the natural resource use of Yelwa Village, Nigeria." Vol.39, Issue.45, 2009.

H. Alalibo, N. D. Nwiabu, "Deep Learning Technique for Oil and Gas Pipeline Surveillance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1076-1081, 2019.

According to Moore’s law, the number of transistors on a chip roughly doubles every 2 years. As a result, the current technology accommodates more number of transistors; almost a billion. In System on Chip (SoC), the multiple processor & logic inside the chip are driven at high clock frequency, which in turn dissipates more power, especially at the clock edge. As these trends continue, the power dissipation will become more and more difficult to manage. This increasing power density makes the device more power sensitive in turn creates a huge problem related to heat dissipation. The heat dissipation is not uniform throughout the chip creating hot spots. Hot spots have adverse impact on the performance and reliability of the chip. Recent data shows that more than 50% of all IC failures are related to temperature issue. In this paper two different approaches (ring oscillator & leakage based inverter circuit) were tried out to measure the sensitivity of the dynamic temperature variations. The proposed leakage current based circuit has given dimensionless sensitive value equal to 0.7957 in the scale down technology when compared to ring oscillator dynamic temperature sensor which is 0.076 which is almost 10 times less than of leakage. The other advantage of the leakage current based circuit is it can be built with minimum number of transistors. This circuit can be used in large number to measure the temperature through the chip and integrated to control the area were the heat dissipation is more. The entire set up is simulated and verified in 180ɳm & 45ɳm technological libraries from TSMC to check the scalability

System on Chip (SoC), TSMC, threshold voltage, mobility sub threshold current, drain current and saturation velocity

[1] Yi Ren,Chenxu Wang, Hong Hong “An all CMOS temperature sensor for thermal monitoring of VLSI Circuits”, in the proceedings of the 2009 International Conference on Testing and Diagnosis, Chengdu, China.
[2] Kameswar Rao Vaddina, Liang Guang, Ethiopia Nigussie, Pasi Liljiberg and Juha Plosila “On-line Distributed Thermal Sensing and Monitoring of Multicore Systems”, NORCHIP (2008), pp. 89-93.
[3] Basab Datta, Dhruv Kumar “Analysis of a Ring Oscillator based on Chip Thermal sensor in 65 nm Technology”, University Of Massachusetts-Amherst.
[4] Basab, Datta, Wayne Burleson “Calibration of on-Chip Thermal Sensors Using Monitoring Circuits”, in the proceedings of the 2010 International Symposium on Quality Electronic Design (ISQED).
[5] Ashish Syalt, Victor Lee, Andre Ivanov T, Josep Altet “CMOS Differential and Absolute Thermal Sensors” in the proceedings of 2001 seventh International On-Line Testing workshop, pp: 127 - 132.
[6] Shekhar Borkar “Design challenges of scaled technology” Intel Corporation, 1999, pp 23-29.
[7] Nitin Sachdeva, Neeraj Julka “Effect of Temperature Fluctuations on MOSFET Characteristics” Internationa Journal of Electronics & Communication Technology Vol. 2, Issue 1, pp: 75-78, 2011.
[8] Poki Chen, Member, IEEE, Chun-Chi Chen, Chin-Chung Tsai, and Wen-Fu Lu “A Time-to-Digital-Converter-Based CMOS Smart Temperature Sensor” IEEE Journal Of Solid-State Circuits, Vol. 40, issue 8, pp. 1642 – 1648, 2005.
[9] K.G. Verma Dr. Brajesh Kumar Kaushik Dr. Raghuvir Singh “Analysis of Propagation Delay Deviation under Process Induced Threshold Voltage Variation” International Journal of Computer Applications Vol. 17 issue.5, pp. 20-25, 2011
[10] Dusung Kim and Jiseok Kim “Temperature dependent leakage sensors using specially designed DRAM cells”.
[11] John F. Wakerly, “Digital Design- Principles and Practices”, fourth edition. Pearson,

Yasha Jyothi M Shirur, "Efficient Method to Measure Dynamic Temperature Variations in an Non Uniform Heat Dissipated Integrated Chip," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1082-1087, 2019.

A simplification method for multi-inputs and multi-outputs (MIMO) dynamic system via reducing the order of the original large-scale system is presented in this paper. The common denominator of the original system is reduced by using differentiation method while the numerator coefficients are obtained by applying Cauer second form. The proposed method is computationally simple and capable to retain the properties of the original system. The viability of the proposed method has been checked via one numerical example.

Differentiation, Cauer Second Form, Order Reduction, Simplification, Stability

[1]. Jay Singh, Kalyan Chatterjee, C.B. Vishwakarma, “Reduced order modelling of linear dynamic systems”, ASME-Journals-2015-series: Advances C 70, pp. 71-85, 2015.
[2]. Sharad Kumar Tiwari, Gagandeep Kaur, “Model reduction by new clustering method and frequency response matching”, J Control Autom. Electr. Syst., 28, pp.78-85, 2017.
[3]. Jay Singh, C.B. Vishwakarma, Kalyan Chatterjee, “Biased reduction method by combining improved modified pole clustering and improved Pade approximations”, Applied Mathematical Modelling 40, 2016, pp. 1418-1426, 2015.
[4]. G. Parmar, S. Mukherjee, R. Prasad “System reduction using factor division algorithm and eigen spectrum analysis”, Applied Mathematical Modelling 31, pp. 2542-2552, 2007.
[5]. Shamash Y, “Linear system reduction using Pade approximation to allow retention of dominant modes”, International Journal of Control 21, 2 , pp. 257-272, 1975.
[6]. C.B. Vishwakarma,, R. Prasad “Time domain model order reduction using Hankel matrix approach”, Journal of Franklin Institute 351, pp. 3445-3456, 2014.
[7]. Rudy Eid and Boris Lohmann, “Moment matching model order reduction in time domain using Laguerre series”, Vol. 41, Isuue-2, pp. 3198-3203,2008.
[8]. C.B. Vishwakarma and R. Prasad, “Order reduction using the advantages of differentiation method and factor division”, Indian Journal of Engineering & Materials Sciences, Niscair, New Delhi, Vol. 15, No. 6, pp. 447-451, 2008.
[9]. G. Parmar et. al, “A mixed method for large-scale systems modelling using eigen spectrum analysis and cauer second form”, IETE Journal of Research, Vol. 53, No. 2, pp. 93-102, 2007.
[10]. Shamash Y., “Model reduction using minimal realization algorithm”, Electronics Letters, Vol. 11, No. 16, pp. 385-387, 1975.
[11]. C.B. Vishwakarma, “Model order reduction of linear dynamic systems for control systems design” Indian Institute of Technology Roorkee, Thesis, 2010.

C.B. Vishwakarma , "Simplification of MIMO Dynamic Systems using Differentiation and Cauer Second Form," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1088-1091, 2019.

Buildings are one of the largest energy consumers around the world. Towards reducing the energy wastage in buildings, occupancy based control systems are becoming more wide-spread in commercial office buildings. Accurate identification of occupancy is crucial for such automated energy monitoring systems and for other potential applications such as personal comfort, air quality, and energy auditing. However, efficient and accurate ways to identifying occupancy in large scale buildings is a challenging task. Several approaches have been studied in literature ranging from direct to indirect approaches. In this paper, we present a nonintrusive and indirect occupancy detection approach using environmental sensor data as proxy. Specially, we used temperature and Carbon dioxide sensor dataset available in public domain. We employed the wide used k-means clustered algorithm for identifying occupied and unoccupied state (binary occupancy) of an office room. The proposed approach is validated on a public dataset with one week of environmental sensor data and results are analyzed using confusion matrix. Our experimental results show that the accuracy of detecting binary occupancy is 87.57%. We planned to extend our approach using other environmental sensor data.

Smart buildings, energy management, occupancy detection, data mining, and clustering

[1] IEA, World Energy Outlook, IEA Publications, Paris, 2013, pp. 2013
[2] S. D’Oca, T. Hong, and J. Langevin, "The human dimensions of energy use in buildings: A review" Renewable and Sustainable Energy Reviews, vol. 81, pp. 731–742, 2018.
[3] Rashid, H., Arjunan, P., Singh, P. and Singh, A., 2016, June. Collect, compare, and score: a generic data-driven anomaly detection method for buildings. In Proceedings of the Seventh International Conference on Future Energy Systems Poster Sessions (p. 12). ACM.
[4] V. Garg, N. K., Bansal, Smart occupancy sensors to reduce energy consumption. Energy and Buildings, 2000, 32(1):81-87.
[5] J. Brooks, S. Goyal, R. Subramany, Y. Lin, T. Middelkoop, L. Arpan, L. Carloni, P. Barooah, An experimental investigation of occupancy-based energy-efficient control of commercial building indoor climate, in: Proceeding of the IEEE 53rd Annual Conference on, IEEE, Decision and Control (CDC), Los Angeles, CA, 2014, pp. 5680–5685.
[6] Chen, Zhenghua, Chaoyang Jiang, and Lihua Xie. "Building occupancy estimation and detection: A review." Energy and Buildings (2018).
[7] Jung, Wooyoung, and Farrokh Jazizadeh. "Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions." Applied Energy 239 (2019): 1471-1508.
[8] Duarte C, Van Den Wymelenberg K, Rieger C. Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy and Buildings 2013;67:587–95.
[9] Agarwal Y, Balaji B, Gupta R, Lyles J, Wei M, Weng T. Occupancy-driven energy management for smart building automation. Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building. ACM; 2010.
[10] Kamthe A, Jiang L, Dudys M, Cerpa A. SCOPES: Smart cameras object position estimation system. Proceedings of the 6th European conference on wireless sensor networks. Cork, Ireland: Springer Verlag; 2009. p. 279–95.
[11] Y. Benezeth, H. Laurent, B. Emile, C. Rosenberger, Towards a sensor for detecting human presence and characterizing activity, Energy Build. 43 (2) (2011) 305–314.
[12] M. Jin, N. Bekiaris-Liberis, K. Weekly, C. Spanos, A. Bayen, Sensing by proxy: Occupancy detection based on indoor CO2 concentration, UBICOMM 2015, 2015, 14.
[13] L.M. Candanedo, V. Feldheim, Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models, Energy and Buildings, 2016, 112:28-39.
[14] A. Beltran, V.L. Erickson, A.E. Cerpa, Thermosense: occupancy thermal based sensing for hvac control, in: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, ACM, Rome, Italy, 2013, pp. 11:11–11:18.
[15] N. Li, G. Calis, B. Becerik-Gerber, Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations, Automat. Construct. 24 (2012) 89–99.
[16] E. Hailemariam, R. Goldstein, R. Attar, A. Khan, Real-time occupancy detection using decision trees with multiple sensor types, in: Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design, Society for Computer Simulation International, San Diego, CA, 2011, pp. 141–148.
[17] Z. Yang, N. Li, B. Becerik-Gerber, M. Orosz, A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations, in: Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, Society for Computer Simulation International, San Diego, CA, USA, 2012, pp. 49–56.
[18] Jain, Anil K. "Data clustering: 50 years beyond K-means." Pattern recognition letters 31, no. 8 (2010): 651-666.

C. Durai, "Indirect Occupancy Detection using Environmental SensorData for Smart Office Buildings," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1092-1095, 2019.

The use of bioelectrical impedance analysis (BIA) is common in healthy people as well for patients, but lacks in different consistent mechanism and quality control measures. BIA can be used to measure the fat-free mass (FFM) and total water (TBW) in the matter without considerable fluid and electrolyte abnormalities in the case when appropriate population, age or pathologically specific BIA equations and established procedures are employed. The utilization of basic BIA measurements without relying on the use of regression prediction models and assumptions of the chemical composition of the fat-free body provides a new option for the actual assessment and clinical assessment of nutritional position and prognosis in hospitalized and elderly people. It may help improve patient care and clinical outcomes. Therefore by considering all these points, a survey is provided in this paper. The general description of BIA, types and their application used to determine FFM is demonstrated.

Bioelectrical impedance analysis, Single frequency, multiple frequencies, FFM

[1] U. G. Kyle, I. Bosaeuset al., “Bioelectrical impedance analysis—part II: utilization in clinical practice,” Clinical nutrition, Vol.23, Issue 6, pp.1430-1453, 2004.
[2] I. Janssen, S. B. Heymsfield, R. N. Baumgartner, & R. Ross , “Estimation of skeletal muscle mass by bioelectrical impedance analysis,” Journal of applied physiology, Vol. 89, Issue 2, pp.465-471, 2000.
[3] R. F. Kushner, R. Gudivaka, & D. A. Schoeller, “Clinical characteristics influencing bioelectrical impedance analysis measurements,” The American journal of clinical nutrition, Vol. 64, Issue 3, pp. 423S-427, 1996.
[4] M. C. G. Barbosa-Silva, A. J. Barros, J. Wang, S. B. Heymsfield, “Bioelectrical impedance analysis: population reference values for phase angle by age and sex–.,”The American journal of clinical nutrition, Vol. 82, Issue.1, pp.49-52, 2005
[5] U. G. Kyle, L. Genton, L. Karsegard, D. O. Slosman, & C. Pichard, “Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years,” Nutrition, Vol. 17, Issue 3, pp.248-253, 2001.
[6] U. G. Kyle, I. Bosaeus, A. D. De Lorenzo, P. Deurenberg, M. Elia, “Bioelec,” Clinical nutrition, Vol. 23, Issue 5, pp.1226-1243, 2004.
[7] M. C. G. Barbosa-Silva, & A. J. Barros, “Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations,” Current Opinion in Clinical Nutrition & Metabolic Care, Vol. 8, Issue 3, pp.311-317, 2005.
[8] R. F. Kushner , “Bioelectrical impedance analysis: a review of principles and applications,” J Am Coll Nutr, Vol. 11, Issue 2, pp.199-209, 1992.
[9] B. H. Cornish, B. J. Thomas, & L. C. Ward, “Improved prediction of extracellular and total body water using impedance loci generated by multiple frequency bioelectrical impedance analysis,” Physics in Medicine & Biology, Vol. 38, Issue 3, pp.337, 1993.
[10] K. R. Segal, S. Burastero, A. Chun, P. Coronel, R. N. Pierson Jr, & J. Wang, “Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement,” The American journal of clinical nutrition, Vol. 54, Issue 1, pp.26-29, 1991.
[11] H., Kværner, A. S.Ræder, C. Henriksen, G. Florholmen, H. B. Henriksen, “Validity of bioelectrical impedance analysis in estimation of fat-free mass in colorectal cancer patients,” Clinical Nutrition, Vol.37, No.1, pp.292-300, 2018.
[12] H. Aleman-Mateo, E. Rush, J. Esparza-Romero, E. Ferriolli, M. Ramirez-Zea, A. Bour, “Prediction of fat-free mass by bioelectrical impedance analysis in older adults from developing countries: a cross-validation study using the deuterium dilution method,” The journal of nutrition, health & aging, Vol.14, Issue 6, pp. 418-426, 2010.
[13] C. O`brien, A. J. Young, & M. N. Sawka, “Bioelectrical impedance to estimate changes in hydration status,” International journal of sports medicine, Vol. 23, Issue 5, pp.361-366, 2002.
[14] KJ Ellis, SJ Bell, GM Chertow, et al. ,”Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference,” Nutrition Vol.15, pp.874–80, 1999.
[15] H. C. Lukaski, U. G. Kyle, & J. Kondrup, “Assessment of adult malnutrition and prognosis with bioelectrical impedance analysis: phase angle and impedance ratio,” Current opinion in clinical nutrition and metabolic care, Vol. 20, Issue 5, pp.330-339, 2017.
[16] L. Genton, F. R. Herrmann, A. Spörri, & C. E. Graf , “Association of mortality and phase angle measured by different bioelectrical impedance analysis (BIA) devices,” Clinical nutrition, Vol 37, Issue3, pp.1066-1069, 2018.
[17] M. Sowers, H. Zheng, K. Tomey, C. Karvonen-Gutierrez, M., Li, X. Jannausch, “Changes in body composition in women over six years at midlife: ovarian and chronological aging,” The Journal of Clinical Endocrinology & Metabolism, Vol.92, Issue3, pp.895-901, 2007.
[18] K. J. Shafer, W. A. Siders, L. K. Johnson, & H. C. Lukaski, , “Validity of segmental multiple-frequency bioelectrical impedance analysis to estimate body composition of adults across a range of body mass indexes,” Nutrition, Vol.25, Issue1, pp.25-32, 2009.
[19] M. Hulens, G. Vansant, R. Lysens, A. L. Claessens, & E. Muls, “Exercise capacity in lean versus obese women,” Scandinavian journal of medicine & science in sports, Vol. 11, Issue 5, pp. 305-309, 2001.

Er. Poonam Khalsa, Jayanand Manjhi, "Bioelectrical Impedance Analysis: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1096-1099, 2019.

As the medical body sensor network (BSN) is usually resource limited and vulnerable to environmental effects and malicious attacks, faulty sensor data arise inevitably which may resultin false alarms, faulty medical diagnosis, and even serious misjudgment. Thus, faulty sensor data should be detected and removed as much as possible before being utilized for medical diagnosis making. Most available works directly employed fault detection schemes developed in traditional wireless sensor network (WSN) for body sensor fault detection. However, BSNs adopt a very limited number of sensors for vital information collection, lacking the information redundancy provided by densely deployed sensor nodesin traditional WSNs. In light of this, a Dual sensor network model based sensor fault detection scheme is proposed in this project, which relies on double sensor data for establishing the conditional probability distribution of body sensor readings, rather than the redundant information collected from a large number of sensors. Furthermore, the Dual sensor network-based scheme enables us to minimize the inaccuracy rate by optimally tuning the threshold for fault detection. Extensive online dataset has been adopted to evaluate the performance of our fault detection scheme, which shows that our scheme possesses a good fault detection accuracy and allow false alarm rate.

Arduino UNO, Health Care, Radio Frequency, W.S.N.

[1] O. Salem, A. Guerassimov, A. Mehaoua, A. Marcus, and B. Furht, “Sensor fault and patient anomaly detection and classification in medical wireless sensor networks,” in Proc. IEEE Int. Conf. Commun., 2013, pp. 4373–4378.
[2] O. Salem, Y. Liu, and A. Mehaoua, “Anomaly detection in medical wireless sensor networks,” J. Comput. Sci. Eng., vol. 7, no. 4, pp. 272–284, 2013.
[3] J. Branch, B. Szymanski, C. Giannella, and R. Wolff, “In-network outlier detection in wireless sensor networks,” in Proc. IEEE Int. Conf. Distrib. Comput. Syst., 2006, pp. 51–57.
[4] M. Zhang, S. Shi, H. Gao, and J. Li, “Unsupervised outlier detection in sensor networks using aggregation tree,” in Proc. Adv. Data Mining Appl., 2007, pp. 158–169.
[5] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surveys, vol. 41, no. 3, pp. 1–72, 2009.
[6] J. Liu and N. Kato, “A Markovian analysis for explicit probabilistic stopping based information propagation in post-disaster ad hoc mobile networks,” IEEE Trans. Wireless Commun., vol. 15, no. 1, pp. 81–90, Jan. 2016.
[7] Y. Zhang, N. Meratnia, and P. Havinga, “Outlier detection techniques for wireless sensor networks: A survey,” IEEE Commun. Survey Tuts., vol. 12, no. 2, pp. 159–170, Second Quarter 2010.
[8] W. Wu, X. Cheng, M. Ding, K. Xing, F. Liu, and P. Deng, “Localized outlying and boundary data detection in sensor networks,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 8, pp. 1145–1157, Aug. 2007.
[9] B. Sheng, Q. Li, W. Mao, and W. Jin, “Outlier detection in sensor networks,” in Proc. Mobile Ad Hoc Netw. Comput., 2007, pp. 219–228.
[10] T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, “Distributed deviation detection in sensor networks,” ACM Special Interest Group Manag. Data, vol. 32, no. 4, pp. 77–82, 2003.
[11] S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient algorithms for mining outliers from large data sets,” ACM Special Interest Group Manag. Data, vol. 29, no. 2, pp. 427–438, 2000.
[12] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. IF-13, no. 1, pp. 21–27, Jan. 1967.
[13] S. Rajasegarar, C. Leckie, M. Palaniswami, and J. C. Bezdek, “Distributed anomaly detection in wireless sensor networks,” in Proc. IEEE Int. Conf. Commun. Syst., 2006, pp. 1–5.
[14] B. Krishnamachari and S. Iyengar, “Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks,” IEEE Trans. Comput., vol. 53, no. 3, pp. 241–250, Mar. 2004.
[15] X. Luo, M. Dong, andY. Huang, “On distributed fault-tolerant detection in wireless sensor networks,” IEEE Trans. Comput., vol. 55, no. 1, pp. 58–70, Jan. 2006.
[16] A. Annichini, E. Asarin, and A. Bouajjani, “Symbolic techniques for parametric reasoning about counter and clock systems,” in Proc. Comput. Aided Verification, 2000, pp. 419–434.

D.M. Pavithra, P. Ramchandar Rao, "Dual Sensor based Wearable Sensor Fault Detection for Reliable Medical Monitoring," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1100-1103, 2019.

Automatic Facial Expression Recognition (AFER) systems are gaining importance in various emerging Human Computer Interaction (HCI) applications and affective computing applications. The abstract and robust features to interpret facial expressions and encode them as an emotion, still, remain as a challenge in the field of AFER. The objective of the proposed work is to analyze the performance of still image based AFER system with respect to various feature extraction schemes, and to optimize and thereby improving the recognition accuracy of AFER systems. Features such as; Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and a combination of HOG-LBP are used for the analysis of AFER system performance on feature extraction schemes. Various Parameters corresponding to features of interest are involved during the experimentation to understand the impact of a particular feature parameter on the recognition rate of AFER system. It’s not a simple task to optimize parameters of a feature to achieve better recognition rates. The proposed work is implemented on Extended Cohn-Kanade (CK+) dataset for six expressions. Cell size parameter of the features experimented has shown improvement in performance. Experimental results demonstrate the effectiveness of the proposed work on still image based facial expression recognition by providing significant performance improvement over other methods under comparison.

Facial Expression Recognition, Feature Extraction, Feature combination, Image Classification, Texture Descriptor, Human Computer Interaction Component

[1] P. Ekman and W. Friesen, “Constants across cultures in the face and emotion”, Journal of personality and social psychology, vol.17, no.2, p-124, 1971.
[2] Z.Zeng, M.Pantic, G.I.Roisman and T. S. Huang,”A survey of affect recognition methods: Audio, visual and spontaneous expressions”, IEEE IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31 no. 1, pp. 39-58, 2009.
[3] Xiaoming Zhao and Shiqing Zhang, “A Review on Facial Expression Recognition: Feature Extraction and Classification”, IETE Technical Review, Vol. 33 Issue- 5, 2016.
[4] I.Kotsia and I. Pitas, “Facial expression recognition in image sequences using geometric deformation features and support vector machines”, IEEE Transactions on Image Processing, vol.16, no.1, pp. 172-187, Jan 2007.
[5] P. Ekman and W. Friesen, “The Facial Action Coding System (FACS): A Technique for the Measurement of Facial Movement”, Consulting Psychologists Press, San Francisco, 1978.
[6] Ziheng Wang, Shangfei Wang,Qiang Ji, “Capturing Complex Spatio-Temporal Relations among Facial Muscles for Facial Expression Recognition”, IEEE Conference on Computer Vision and Pattern Recognition.
[7] K. Sikka, T. Wu, J. Susskind, and M. Bartlett, “Exploring bag of words architectures in the facial expression domain”, in Proc. European Conf. Computer Vision Workshops, pp. 250–259, 2012.
[8] S L Happy and Aurobinda Routray, “Automatic facial expression recognition using features of salient facial patches”, IEEE Transactions on Affective Computing.
[9] Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection”, in the Proceedings of Conference on Computer Vision and Pattern Recognition 2005.
[10] Shan, Sh. Gong, P. W. McOwan, “Facial expression recognition based on local binary patterns: a comprehensive study”, Image and Vision Computing, vol. 27, no. 6, pp. 803-816, 2009.
[11] T. Ahonen, A, Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, pp. 2037-2041, 2006.
[12] W. Hsu and C.-J. Lin, "A comparison of methods for multiclass support vector machines", IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
[13] M.Regina, M.S. Josephine, V. Jeyabalraja,”Multi-featured extraction and convolution neural network based SVM for automatic facial emotion recognition”, International Journal of Computer Science and Engineering, Vol.7, Issue 5, 2019.

Naveen Kumar H N, Jagadeesha S, Amith K Jain, "Optimization in Feature Extraction schemes on Static Images to Improve the Performance of Automatic Facial Expression Recognition Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1104-1109, 2019.