iosr-JECE   Volume-17 ~ Issue-2 ~ Mar. - Apr. 2022

Abstract: Lidar is a laser-based remote measurement sensor that finds its best-known application in the study of the atmosphere. Our study on remote sensing by lidar system focuses on a few basic and fundamental principles on this one. Thus, included in this principle, it is necessary to know the type of detection that we should use for a defined study using lidars. The aim of this work is to start a study on the performance of lidar in direct detection and lidar in heterodyne detection.

Keywords: Lidar, direct detection, heterodyne detection, CNR

[1]. Céline Klein, « Design and prototyping of a lidar for measuring water content liquid in the fog », 2013
[2]. Ulla Wandinger, « Introduction to Lidar », 2000
[3]. David Daon, « Characterization of aerosols by inversion of the combined data of solar photometers and ground lidars », 2012
[4]. Claudine Besson and Dolfi-Bouteyre, «Recent development in fiber lidar », 2013
[5]. JL Meyzonnette, « Laser radars », 1992

Abstract: In this work, real time experiments evaluation of the 60 GHz 802.11ad live streaming was conducted. An error free 4k live transmission over 802.11ad up to distance of 22 m which is more than double the 802.11ad standard specification (10 m) was reported. Investigating in real time the effect of polarization on multi- channel transmission over this band and standards, using three different settings to provide 18 configurations shows that 60 GHz bands has the potential of supporting applications that requires ultra-high speed wireless links.......

Keywords: Millimetre wave, polarization. 4k UHD, Gigabytes, compute unified device architecture (CUDA), Shutter time, Frame.

[1]. Adewale Abe and Stuart D. Walker "Multi-hop 802.11ad Wireless H.264 Video Streaming" IEEE 2016 94-99
[2]. S. Venuti, \Introducing hdmi 1.4 specification features," Press Release, 2009.
[3]. H. Singh, J. Oh, C. Kweon, X. Qin, H.-R. Shao, and C. Ngo, \A 60 ghz wireless network for enabling uncompressed video communication," IEEE Communications Magazine, (46) 12, 2008 71-78.
[4]. J. M. Gilbert, C. H. Doan, S. Emami, and C. B. Shung, \A 4-gbps uncompressed wireless hd a/v transceiver chipset,"IEEEmicro,(28)2,2008,56-64.
[5]. S. Colonnese, G. Panci, and G. Scarano, \Error resilient video coding for wireless channels," in Personal, Indoor and Mobile Radio Communications, 2004. PIMRC 2004. 15th IEEE InternationalSymposiumon,vol.4,2004,3040-30442004.

Abstract: The problems being experienced by video conferencing are voice and video packet end-to-end delay, video packets delay, and throughput. Worldwide Interoperability for Microwave Access (WIMAX) comes into use as subscribers demand for better quality of service in the area of voice and video multimedia applications with high-speed data connection, excellent security and mobility. There is a great need to show improvements in modulation scheme with respect to packet end-to-end delay, throughput and packet delay variation performance metrics. Hence, a quantitative approach was developed using the Optimized Network Engineering Tool (OPNET) Modeler to show improvements in modulation scheme........

Keywords: Modulation scheme, OPNET, Video Conference, WIMAX.

[1] Abdulrahaman, M. D., Faruk, N., Oloyede, A. A., Surajudeen-Bakinde, N. T., Olawoyin, L. A., Mejabi, O. V., Imam-Fulani, Y. O., Fahm, A. O. & Azeez, A. L. (2020). Multimedia Tools in the Teaching and Learning Processes: A Systematic Review. Heliyon Cell Press Journal.
[2] Abdulrazzaq, A. A., Abid, A. J. & Ali, A. H. (2018). QoS Performances Evaluation for Mobile WIMAX Networks based on OPNET. International Journal of Applied Engineering Research, 13(9), 6545-6550.
[3] Aliu, D., Usman, A. D. & Sani, S. M. (2015). A Review on Scheduling Algorithms for Multicast Services over WiMAX Networks. International Journal of Scientific and Engineering Research, 6(6). 1584-1590.
[4] Bulira, D. & Walkowiak, K. (2012). Voice and video Streaming in wireless computer Networks – Evaluation of Network Delays. 2012 2nd Baltic Congress on Future Internet Communications.
[5] Garhwal, A. & Bhattacharya, P. P. (2012). A Review on WiMAX Technology. International Journal of Advances in Computing and Information Technology, 167-173.

Abstract: Background: The most aggressive and dangerous disease of the Central nervous system is Brain tumor, if detected in highest grade it's like a life-threatening situation. Hence, early detection of Brain Tumor is very crucial part to avoid all the risky factors and to enhance the quality of life. In general, many computerized based techniques are used which starts with Magnetic resonance Imaging (MRI), Ultrasounds, CT scan etc. All this involves MRI images to detect the Brain tumors and classification was done as Tumorous or non-tumorous by inspecting those images. However, with enormous amount of data it becomes difficult as well as time consuming to detect and classify the Tumor along with consideration of Human errors. So, to minimize all these consequences, in this paper Brain Tumor detection is proposed with the help of convolutional Neural network (CNN) technique. CNN is a Deep Learning technique which does not require an expert from that particular field to work on it.

Keywords: Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN), Brain Tumor

[1]. Aurav Gupta and Vinay Singh, "Brain Tumor Segmentation and Classification using FCM and Support Vector Machine", International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.
[2]. S.U Aswathy, G. Glan Devadhas and S.S. Kumar, "MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier", Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.
[3]. T. Saba, A. S. Mohamed, M. El-Affendi, J. Amin, and M. Sharif, "Brain tumor detection using fusion of hand crafted and deep learning features," Cognitive Systems Research, vol. 59, pp. 221–230, 2020.
[4]. N. Vani, A. Sowmya and N. Jayamma, "Brain Tumor Classification using Support Vector Machine", International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.
[5]. Narmada M. Balasooriya and Ruwan D. Nawarathna, "A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification", Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-5, 2017.

Abstract:This study presents the design and analysis of adaptive modulation and coding scheme intended for a codec used in short-range wireless communication systems; the study aims to investigate and analyze the existing channel prediction parameters for adapting the variation of wireless communication channels. Three modulation and coding schemes are used to develop the codec, namely 8QAM, 16QAM with a 1/3 BCH encoder, and 4QAM without error correction. MIMO antenna is used to increase the spectral efficiency of the codec within the fading channel. The presented scheme operates at.......

Key Word: Adaptive Modulation and coding scheme; LSTM-NN; Predictor; Codec adaptive

[1]. L. Zhang and Z. Wu, "Machine Learning–Based Adaptive Modulation and Coding Design," Mach. Learn. Futur. Wirel. Commun., pp. 157–180, 2020, doi: 10.1002/9781119562306.ch9.
[2]. K. M. S. Soyjaudah and B. Rajkumarsingh, "Adaptive coding and modulation using Reed Solomon codes for Rayleigh fading channels," EUROCON 2001 - Int. Conf. Trends Commun. Proc., pp. 50–53, 2001, doi: 10.1109/EURCON.2001.937761.
[3]. F. Peng, J. Zhang, and W. E. Ryan, "Adaptive modulation and coding for IEEE 802.11n," IEEE Wirel. Commun. Netw. Conf. WCNC, no. 1, pp. 657–662, 2007, doi: 10.1109/WCNC.2007.126.
[4]. D. L. Goeckel, "Adaptive coded modulation for transmission over fading channels," Adapt. Cross Layer Des. Wirel. Networks, vol. 2, no. 5, pp. 71–94, 2018, doi: 10.1201/9781420046021.ch3.
[5]. P. Patil, M. Patil, S. Itraj, and U. Bombale, "IEEE 802.11n: Joint modulation-coding and guard interval adaptation scheme for throughput enhancement," Int. J. Commun. Syst., vol. 33, no. 8, pp. 3–5, 2020, doi: 10.1002/dac.4347..

Abstract: In this paper, a comparison of Internet of Things protocols used for data transfer in Internet of Things constrained networks is presented. Setting up such a network with a large number of physical interconnected IoT devices can be a challenge. In the IoT world, one of the key challenges is to efficiently support M2M communication in constrained networks. This can be achieved using CoAP (Constrained Application Protocol) protocols and RPL (Routing Protocol for Low-Power and Lossy Networks). Choosing the appropriate protocol can be difficult while developing IoT application. There are several conditions that need to be considered while determining which protocol should be used. In this paper, we will evaluate performance and compare these protocols through different scenarios.

Key Word: IoT, CoAP, RPL

[1]. Yuang Chen, Thomas Kunz, "Performance Evaluation of IOT Protocols under a Constrained Wireless Access Network," 2016 International Conference on selected topics in Mobiles and Wireless Networking (MoWNeT)
[2]. Monishanker Halder, Mohammad Nowsin Amin Sheikh, Md. Saidur Rahman, Md. Amanur Rahman, "Performance Analysis of CoAP, 6LowPAN and RPL Routing Protocols of IOT using COOJA Simulator," International Journal of Scientific & Engineering Research, Volume 9, Issue 6, June-2018 ISSN 2229-5518
[3]. G. Vennila, Dr. D. Arivazhagan, Dr. R.Jayavadivel, "Experimental Analysis Of RPL Routing Protocol In IOT," INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 10, OCTOBER 2019 ISSN 2277-8616"
[4]. Dr.S.Umamaheswari, Dr.Atul Negi, "INTERNET OF THINGS AND RPL ROUTING PROTOCOL: A STUDY AND EVALUATION," 2017 International Conference on Computer Communication and Informatics (ICCCI -2017), Jan. 05 – 07, 2017, Coimbatore, INDIA
[5]. Belghachi Mohamed, Feham Mohamed, "Experimental Evaluation of RPL Protocol," The 11th International Conference for Internet Technology and Secured Transactions (ICITST-2016).

Abstract: Cereal grains vitally important in meeting the nutrient needs of the human population. Cereals are an upscale source of vitamins, minerals, carbohydrates, fats, oils, and protein. Legumes are an important source of protein, dietary fiber, carbohydrates and dietary minerals. Oilseeds are wont to make vegetable oils and biodiesel. Grain quality can have different aims to different people depending upon the sort of grain or seed and its intended use. Our objective is to develop a system to analyze the cereals, oilseeds and pulses. Hence, we develop a technique which is used to find the quality of these cereals, oil seeds and the pulses using the deep learning technique which is a CNN based transfer learning method called Dense Net.

Key Word: Computer vision, Image processing, Kernel, Grains, Contour, CNN

[1]. T.Gayathri Devi, Dr. P. Neelamegam, S. Sudha, Machine vision based quality analysis of Rice grains, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering(ICPCSI-2017).
[2]. Engr. Zahida Parveen, Dr. Muhammad Anzar Alam, Engr. Hina Shakir, Assessment of Quality of Rice Grain using Optical and Image Processing Technique, 2017 International Conference on Communication, Computing and DigitalSystems
[3]. Engr. Zahida Parveen, Dr. Muhammad Anzar Alam, Engr. Hina Shakir, Assessment of Quality of Rice Grain using Optical and Image Processing Technique, 2017 International Conference on Communication, Computing and DigitalSystem.
[4]. Transportation Systems. 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE) [5]. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Third Edition, Prentice Hall Inc., 2008.
[5]. Raja Haroon Ajaz ,Lal Hussain(Pakistan), Seed classification using machine learning techniques, Journal of Multidisciplinary Engineering Science and Technology (JMEST) ISSN: 3159-0040 Vol. 2 Issue 5, May –2015.

Abstract: This project aims to assist the driver in avoiding potholes on the roads, by giving the buzzer alert and also levels holes. This robot automatically detects potholes based on distance using an Ultrasonic sensor while traveling. Leveling the detected hole is to be done using Servo motors. The robot is controlled using android based smartphones through Bluetooth. Based on the commands given by the user the robot moves accordingly. At the receiver end, there are two motors interfaced with the microcontroller for robot movement. This system uses an ultrasonic sensor to sense the potholes and humps and which quantity the height and deepness of the potholes based on the acknowledged signals. Hence, this work can make accidents or mishaps reduction and aid in traffic control.

Key Word: Raspberry pi, ultrasonic sensor, cloud, Global Positioning System, Pothole.

[1]. Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection IEEE Sensors Journal (Volume: 15, Issue: 2, Feb. 2015)
[2]. Pothole Detection and Warning System: System Design.2009 International Conference on Electronic Computer Technology, 27 February 2009
[3]. Real-Time Pothole Detection using Android Smartphones with Accelerometers.2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS) August 2011.
[4]. Automatic Detection of Potholes and Humps on Roads to Aid Driver. IEEE Sensors Journal, 30 March 2015.
[5]. Speed Control of Vehicle by Detection of Potholes and Humps. International Journal of Advanced Res Electronics and Instrumentation Engineering, March 2016.