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i-manager's Journal on Embedded Systems (JES)

Current Issue Vol. 12 Issue 2

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Volume 8 Issue 2 January - June 2020

Research Paper

Development of a Raspberry Pi Secured Management System for Home Automation

Keshinro kazeem kolawole* , Iromini Nurudeen Ajibola, Alimi Teslim Adekunle*

* Department of Computer Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria.

Department of Electrical/Electronic Engineering, Federal Polytechnic Offa, Kwara, Nigeria.

* Department of Electrical Engineering, Federal Polytechnic Bida, Niger, Nigeria.

Kolawole, K. K., Ajibola, I. N., and Adekunle, A. T. (2020). Development of a Raspberry Pi Secured Management System for Home Automation. i-manager's Journal on Embedded Systems, 8(2), 1-7. https://doi.org/10.26634/jes.8.2.17182

Abstract

This paper deals with the design and implementation of Raspberry Pi Secure Home Automation for mobile applications using mobile technology, which ensure that we are safe in our home, and related monitoring activities. Our new integration of the camera and motion detector is the key to our proposed Home Security solution. Raspberry Pi operates and controls remote sensing, live video sharing and recording, and handles tasks for potential replication on home hardware, such as turning on/off a TV or a microwave. Initially, when the activity is detected and Raspberry Pi alerts householders for possible interference, the cameras will automatically start recording. Raspberry Pi has two primary interaction parts: web applications running on the portable device browser, and server scripts running on the Raspberry Pi hardware element in the cloud. Due to its friendliness and ease of use, we have decided to include domestic security and home safety in a single program. For example, when an attacker enters a room, the PRI sensors detect the activity and trigger the monitor, and the camera captures photos of an individual interacting with a live video feed. Additional backup and email services are also provided for the customer, which provides better recovery facility for the user.

References

[1]. De Luca, G., Lillo, P., Mainetti, L., Mighali, V., Patrono, L., & Sergi, I. (2013, September). The use of NFC and Android technologies to enable a KNX-based smart home. In 21st International Conference on Software, Telecommunications and Computer Networks-(SoftCOM 2013). https://doi.org/10.1109/SoftCOM.2013. 6671904

[2]. Gu, Y., Kim, M., Cui, Y., Lee, H., Choi, O., Pyeon, M., & Kim, J. (2013, June). Design and implementation of UPnPbased surveillance camera system for home security. In International Conference on Information Science and Applications (ICISA) (pp. 1-4). IEEE. https://doi.org/10. 1109/ICISA.2013.6579349

[3]. Makwana, R., Baviskar, J., Panchal, N., &Karia, D. (2013, April). Wireless based load control and power monitoring system. In International Conference on Energy Efficient Technologies for Sustainability (pp. 1207-1211). IEEE. https://doi.org/10.1109/ICEETS.2013.6533559

[4]. Ryu, Y., Kim, Y., & Yoo, J. (2017). Mobile photovoltaic systems cloud services. In Proceedings of Cloud Computing Technology and Science (CloudCom), 4th International Conference. IEEE.

[5]. Thampi, S. M., Marques, O., Krishnan, S., Li, K. C., Ciuonzo, D., & Kolekar, M. H. (Eds.). (2019). Advances in signal processing and intelligent recognition systems. 4th International Symposium SIRS 2018, Bangalore, India.

[6]. Trung, B. V. T., & Cuong, N. V. (2019). GPRS monitoring and control of systems on the FPGA platform. Advanced Communications Technologies (ATC), International Conference. IEEE. https://doi.org/10.1109/ATC.2013.669 8209

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Research Paper

Real-Time Fatigue Detection using a Low Cost Wireless EEG Device

Shashidhar R.* , Shruthi B. R., Hariprasad S. A.*, Kavita R. Singh****

* Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysore, India.

Department of Medical Electronics, MVJ College of Engineering, Bangalore, Karnataka, India.

* Department of Electronics and Communication Engineering, Jain University, Bengaluru, Karnataka, India.

**** Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.

Shashidhar, R., Shruthi, B. R., Hariprasad, S. A., and Singh, K. R. (2020). Real-Time Fatigue Detection using a Low Cost Wireless EEG Device. i-manager's Journal on Embedded Systems, 8(2), 8-13. https://doi.org/10.26634/jes.8.2.17197

Abstract

The evolution of methods for the observation of psychological tiredness has wide range of implementation in regions where continuous observation is of prominence like safety and transportation. This work is to improve a new driving fatigue recognition method in real time using brainsense band. One of the most significant features of road accidents is the driver's fatigue and this can be fatal, resulting in serious injuries and death. Therefore, a reliable real-time driver fatigue detection system that can alert the driver before an accident occurs is required. The various techniques available using EEG signals to detect the driver's drowsiness are found to be more effective. Here we used a single channel wireless EEG device which can obtain driver's attention and blink levels. The values are analyzed using MATLAB as a platform to detect driver's fatigue level. A threshold is set for the driver's attention and blink values and the system alerts the driver with an alarm if the extracted values fall below the threshold. A test is conducted on 15 subjects using the Brainsense headband, which provides an effective resolution for analyzing and avoiding driver fatigue in real-time situations.

References

[1]. Acharya, S., Rai, D., Rao, K. P. S., Srijith, V. J., & Nithin, K. (2013). Fatigue detection alarm system. In International Conference on Multimedia Processing, Communication and Information Technology-MPCIT.

[2]. Amin, H. U., Malik, A. S., Ahmad, R. F., Badruddin, N., Kamel, N., Hussain, M., & Chooi, W. T. (2015). Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australasian Physical & Engineering Sciences in Medicine, 38(1), 139- 149. https://doi.org/10.1007/s13246-015-0333-x

[3]. Bonjyotsna, A., & Roy, S. (2014). Correlation of drowsiness with electrocardiogram: A review. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(5), 9538- 9544.

[4]. Deepa, T. P., & Reddy, V. (2013). EEG based drowsiness detection using mobile device for intelligent vehicular system. International Journal of Engineering Trends and Technology (IJETT), 6(1), 21-24.

[5]. Hal, B. V. (2013). Real-time stage 1 sleep detection and warning system using a low-cost EEG headset. Master Thesis, Grand Valley State University, USA (pp. 1-60).

[6]. Kaplan, S., Guvensan, M. A., Yavuz, A. G., & Karalurt, Y. (2015). Driver behavior analysis for safe driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3017-3032. https://doi.org/10.1109/TITS.2015.2 462 084

[7]. Ko, L. W., Lai, W. K., Liang, W. G., Chuang, C. H., Lu, S. W., Lu, Y. C., Hsiung, T. Y., Wu, H. H., & Lin, C. T. (2015, July). Single channel wireless EEG device for real-time fatigue level detection. In 2015, International Joint Conference on Neural Networks (IJCNN) (pp. 1-5). IEEE. https://doi.org/ 10.1109/IJCNN.2015.7280817

[8]. Ma, Y., Chen, B., Li, R., Wang, C., Wang, J., She, Q., Luo, Z., & Zhang, Y. (2019). Driving fatigue detection from EEG using a modified PCANet method. Computational Intelligence and Neuroscience, 1-9. https://doi.org/10.11 55/2019/4721863

[9]. Min, J., Wang, P., & Hu, J. (2017). Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system. PLoS One, 12(12). https://doi.org/10. 1371/journal.pone.0188756

[10]. Rawat, S., & Verma, V. K. (2015). A review on sleep detection using EEG signal. International Journal of Engineering Trends and Technology (IJETT), 23(6). https://doi.org/10.14445/22315381/IJETT-V23P255

[11]. Rohit, F., Kulathumani, V., Kavi, R., Elwarfalli, I., Kecojevic, V., & Nimbarte, A. (2017). Real-time drowsiness detection using wearable, lightweight brain sensing headbands. IET Intelligent Transport Systems, 11(5), 255- 263. https://doi.org/10.1049/iet-its.2016.0183

[12]. Tang, X., Zhou, P., & Wang, P. (2016, July). Real-time image-based driver fatigue detection and monitoring th system for monitoring driver vigilance. In 2016, 35 Chinese Control Conference (CCC) (pp. 4188-4193). IEEE. https://doi.org/10.1109/ChiCC.2016.75540 07

[13]. Wang, H., Dragomir, A., Abbasi, N. I., Li, J., Thakor, N. V., & Bezerianos, A. (2018). A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive Neurodynamics, 12(4), 365-376. https://doi. org/10.100 7/s11571-018-9481-5

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Research Paper

Design of Blink to Speak System for Paralysed People

Shalvi Tyagi* , Anshul Gupta, Anamika Maurya*, Arshita Garg**, Kaustubh Vats*, Amit Kumar****

*-****** Department of Electronics and Communication Engineering, Meerut Institute of Engineering and Technology, Aktu, Uttar Pradesh, India.

Tyagi, S., Gupta, A., Maurya, A., Garg, A., Vats, K., and Kumar, A. (2020). Design of Blink to Speak System for Paralysed People. i-manager's Journal on Embedded Systems, 8(2), 14-19. https://doi.org/10.26634/jes.8.2.17389

Abstract

According to the research, there are approximately 5.4 million people who are suffering from paralysis. The prototype system focuses on an unique system to help people with LIS paralysis, so they can communicate with a person in an easy manner. The system is done by making use of eye-blinks or eye movement as they cannot move their body parts. A camera module had been used to capture the image and convert the text to voice signal, which is displayed on LCD. The proposed system is coordinated by the help of Raspberry pi.

References

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[3]. Li, Y., Pan, J., Long, J., Yu, T., Wang, F., Yu, Z., & Wu, W. (2015). Multimodal BCIs : Target detection, multidimensional control, and awareness evaluation in patients with disorder of consciousness. Proceedings of the IEEE, 104(2), 332-352. https://doi.org/10.1109/JPROC. 2015.2469106

[4]. Martinez, C. M., Heucke, M., Wang, F. Y., Gao, B., & Cao, D. (2017). Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey. IEEE Transactions on Intelligent Transportation Systems, 19(3), 666-676. https://doi.org/10.1109/TITS.2017.2706978

[5]. Meng, C., & Zhao, X. (2017). Webcam-based eye movement analysis using CNN. IEEE Access, 5, 19581- 19587. https://doi.org/10.1109/ACCESS.2017.2754299

[6]. Mukherjee, K., & Chatterjee, D. (2015, January). Augmentative and alternative communication device based on eye-blink detection and conversion to morsecode to aid paralyzed individuals. In 2015, International Conference on Communication, Information & Computing Technology (ICCICT) (pp. 1-5). IEEE. https://doi. org/10.1109/ICCICT.2015.7045754

[7]. Singh, M., Jain, P., & Chopra, S. (2015). Eye movement detection for wheelchair control application. In International Conference on Electrical, Electronics, Signals, Communication and Optimization, EESCO 2015 (pp. 4-8).

[8]. Zhang, R., He, S., Yang, X., Wang, X., Li, K., Huang, Q., Yu., Z., Zhang., X., Tang., D., & Li, Y. (2018). An EOG-based human–machine interface to control a smart home environment for patients with severe spinal cord injuries. IEEE Transactions on Biomedical Engineering, 66(1), 89-100. https://doi.org/10.1109/TBME.2018.2834555

[9]. Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., & Gu, Z. (2015). Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(1), 128-139.

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Research Paper

A Study of Automatic Bottle Filling, Capping, Labelling Machine using Different Controllers

Aditi Devidas Ghuge*

Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India.

Ghuge, A. D. (2020). A Study of Automatic Bottle Filling, Capping, Labelling Machine using Different Controllers. i-manager's Journal on Embedded Systems, 8(2), 20-25. https://doi.org/10.26634/jes.8.2.17145

Abstract

This paper presents a small plant of the water bottle filling, capping and labelling machine, and states the comparison between same projects using different controllers such as PLC, ARM7 controller, 8051 controller. A large number of bottles can be filled simultaneously using PLC (Programmable Logic Control). At the same time, a project using 8051 or 89C51 controller takes more time to perform the same task and it only fills one bottle at a time. The aim of the project is to develop an automatic machine for bottle filling, capping, and labelling to reduce labor. It is performed with superior performance using ARM7 controller. The project stated in this paper uses a conveyor belt, which is controlled by an ARM7 processor, which manually places the bottle on the conveyor to move forward to fill with water. The graphical display indicates the quantity of water filled in bottles and the number of bottles filled. In capping section, a robotic arm is used to fix the cap on bottle using DC motor. In next step, the bottle gets labelled on running conveyor belt. The software used is the embedded C programming, which guides the ARM7 processor to perform the respective tasks. The conveyor belt is controlled with the ARM7 processor using IC LPC2148.

References

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Review Paper

A Review on Performance Evaluation of Multiprocessor based System using Scheduling Algorithms and Simulation Tools

M. Sreenath* , P. A. Vijaya**

*-** Department of Electronics and Communication Engineering, BNMIT, Bangalore, Karnataka, India.

Sreenath, M., and Vijaya, P. A. (2020). A Review on Performance Evaluation of Multiprocessor based System using Scheduling Algorithms and Simulation Toolsi-manager's Journal on Embedded Systems, 8(2), 26-33. https://doi.org/10.26634/jes.8.2.17120

Abstract

The scheduling algorithm determines the process of execution by allocating the tasks to the available processors in a system. In an embedded system, real time control is essential for different applications like aircraft, manufacturing, information processing systems, etc. Multiprocessor based systems play a vital role to satisfy the real time performance constraints. In this aspect, different issues came to light when using scheduling algorithms for proper resource allocation and utilization. Many of the research scholars face difficulty in choosing the suitable platform for developing the new scheduling algorithms. For this concern, we acknowledge the performance parameters reviewed in the evaluation of multiprocessor based system using scheduling algorithms and simulation tools. Traditional real time scheduling algorithms are implemented using standard structure on suitable simulation tools for improved performance in terms of makespan, latency, flow time, utilization, success ratio, throughput and energy consumption. The main objective of this review is to find the optimum solution for multi-processor based system using scheduling algorithms in supported simulation tools.

References

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i-manager's Journal on Embedded Systems (JES)

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Volume 8 Issue 2 January - June 2020

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* Department of Computer Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria. ** Department of Electrical/Electronic Engineering, Federal Polytechnic Offa, Kwara, Nigeria. *** Department of Electrical Engineering, Federal Polytechnic Bida, Niger, Nigeria.

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Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India.

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A Review on Performance Evaluation of Multiprocessor based System using Scheduling Algorithms and Simulation Tools

M. Sreenath* , P. A. Vijaya**

*-** Department of Electronics and Communication Engineering, BNMIT, Bangalore, Karnataka, India.

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Keshinro kazeem kolawole* , Iromini Nurudeen Ajibola, Alimi Teslim Adekunle*

* Department of Computer Engineering, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria.

Department of Electrical/Electronic Engineering, Federal Polytechnic Offa, Kwara, Nigeria.

* Department of Electrical Engineering, Federal Polytechnic Bida, Niger, Nigeria.

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