Refine your search
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Chandrakar, Chinmay
- Driver’s Drowsiness Detection (DDD) System
Abstract Views :149 |
PDF Views:2
Authors
Affiliations
1 Department of VLSI Design, Shri Shankaracharya College of Engineering and Technology, Bhilai, Chhattisgarh, IN
2 Department of Electronic and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai, Chhattisgarh, IN
1 Department of VLSI Design, Shri Shankaracharya College of Engineering and Technology, Bhilai, Chhattisgarh, IN
2 Department of Electronic and Telecommunication, Shri Shankaracharya College of Engineering and Technology, Bhilai, Chhattisgarh, IN
Source
Digital Image Processing, Vol 3, No 18 (2011), Pagination: 1169-1172Abstract
This paper describes a real-time online prototype driver-fatigue monitor. It uses remotely located charge-cou-pled-device cameras equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization. It is a difficult problem to make drivers drowsiness detection meet the needs of real time in embedded system; meanwhile, there are still some unsolved problems like drivers’ head tilted and size of eye image not large enough. This paper proposes an efficient method to solve these problems for eye state identification of drivers’ drowsiness detection in embedded system which based on image processing techniques. This method break traditional way of drowsiness detection to make it real time, it utilizes face detection and eye detection to initialize the location of driver’s eyes; after that an object tracking method is used to keep track of the eyes; finally, we can identify drowsiness state of driver with PERCLOS by identified eye state. Experiment results show that it makes good agreement with analysis.Keywords
Driver Vigilance, Eyelid Movement, Face Position, Percent Eye Closure (PERCLOS), Visual Fatigue Behaviors.
- An Overview of Image Enhancement Techniques
Abstract Views :213 |
PDF Views:0
Authors
Affiliations
1 Shree Shankaracharya College of Engineering and Technology, Bhilai (CG), IN
1 Shree Shankaracharya College of Engineering and Technology, Bhilai (CG), IN
Source
International Journal of Technology, Vol 1, No 1 (2011), Pagination: 15-19Abstract
Retinex theory addresses the problem of separating the illumination from the reflectance in a given image and thereby compensating for non-uniform lighting. This is in general an ill-posed problem. In this paper we propose a variational model for the Retinex problem that unifies previous methods. Similar to previous algorithms, it assumes spatial smoothness of the illumination field. In addition, knowledge of the limited dynamic range of the reflectance is used as a constraint in the recovery process. A penalty term is also included, exploiting apriori knowledge of the nature of the reflectance image. The proposed formulation adopts a Bayesian view point of the estimation problem, which leads to an alge.braic regularization term that contributes to better conditioning of the reconstruction problem.Keywords
Image Enhancement, Reflectance, Illumination, Visual System, Constancy, SSR, MSR, MSR-CR.- System Design Approach for Heartbeat Detection and Classification of Individuals Irrespective of their Physical Condition
Abstract Views :263 |
PDF Views:95
Authors
Affiliations
1 Department of Electronics and Telecommunication, Shri Shankaracharaya College of Engineering and Technology, Chhattisgarh Swami Vivekananda Technical University, Bhilai 490 020, IN
1 Department of Electronics and Telecommunication, Shri Shankaracharaya College of Engineering and Technology, Chhattisgarh Swami Vivekananda Technical University, Bhilai 490 020, IN
Source
Current Science, Vol 112, No 09 (2017), Pagination: 1915-1920Abstract
In an electrocardiogram (ECG), the heartbeat feature QRS is an important parameter for analysis in any heartbeat classification automated diagnosis system. In this communication the method which we have proposed is by using the counter which is used in parallel. The first level is detection of heartbeats, which uses hashing of ECG features. In the second level, the profiler profiles a person's regular and irregular ECG characteristic behaviour. The proposed method works on data related with ECG, instead of particular features of ECG. Because of parallel processing, data storage unit requirements and the processing time are less. The dependent values in the proposed method vary according to the changes in the ECG waveform. Such type of analysis is suitable for detection of heart disease. The most significant application of such characteristic plotting is to generate an alert signal for irregular ECG behaviour in a person. Such automated system will be useful in remote areas where a cardiologist may not be easily available.Keywords
Data Storage Units, Electrocardiogram Signal, Parallel Processing, QRS Detection.Full Text
References
- Lilly, L. S., Pathophysiology of Heart Disease, Lippincott Williams & Wilkins, Philadelphia, USA, 2003, 3rd edn, pp. 57–90.
- SNORT Network Intrusion Detection System; www.snort.org
- MIT-BIH; http://www.physionet.org
- Senhadji, L. et al., Comparing wavelet transforms for recognizing cardiac patterns. IEEE Eng. Med. Biol., 1995, 14(2), 167–173.
- Sahambi, J. S., Tandon, S. M. and Bhatt, R. K. P., Using wavelet transforms for ECG characterization: an on-line digital signal processing system. IEEE Eng. Med. Biol., 1997, 16(1), 77–83.
- Hamilton, P. S. and Tompkins, W. J., Quantitative investigation of QRS detection rules using the MIT–BIH arrhythmia database. IEEE Trans. Biomed. Eng., 1986, 33(12), 1157–1165.
- Thakor, N. V., Webster, J. G. and Tompkins, W. J., Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans. Biomed. Eng., 1986, 31(11), 702–706.
- Li, C., Zheng, C. and Tai, C., Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng., 1995, 42(1), 21–28.
- Castro, K. D. and Geva, A. B., ECG feature extraction using optimal mother wavelet. In IEEE EMBE International Conference, 2000, pp. 346–350.
- Romero, I., Serrano, L. and Ayesta, ECG frequency domain features extraction: a new characteristic for arrhythmias classification. Conf. IEEE Eng. Med. Boil. Soc., 2001, p. 2.
- Kadambe, S., Murray, R. and Boudreaux-Bartels, G. F., Wavelet transform based QRS complex detector. IEEE Trans. Biomed. Eng., 1999, 46(7), 838–848.
- Romero, L., Addison, P. S. and Grubb, N., R-wave detection using continuous wavelet modulus maxima. IEEE Proc. Comp. Cardiol., 2003, 30, 565–568.
- Tkacz, E. J. and Komorowski, D., An improved statistical approach to the QRS detection problem using matched filter facilities. Biomed. Technik/Biomed. Eng., 1992, 37(1), 99–109.
- Pan, J. and Tompkins, W. J., A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 1885, 32(3), 230–236.
- Dotsinsky, I. A. and Stoyanov, T. V., Ventricular beat detection in single channel electrocardiograms. BioMed. Eng. Online, 2004, 3, 3.
- Zhang, F., Tan, J. and Lian, Y., An effective QRS detection algorithm for wearable ECG in body area network. In Proceedings of IEEE Biomedical Circuits System Conference, 2007, pp. 195– 199.
- Iliev, I., Krasteva, V. and Tabakov, S., Real-time detection of pathological cardiac events in the electrocardiogram. Physiol. Meas., 2007, 28(3), 259–276.
- Zhou, H.-Y. and Hou, K.-M., Embedded real-time QRS detection algorithm for pervasive cardiac care system. In Proceedings of 9th IEEE International Conference on Signal Process, 2008, pp. 2150–2153.
- Cvikl, M. and Zemva, A., FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm. Digital Signal Process, 2010, 20(1), 238–248.
- Ning, X. and Selesnick, I. W., ECG enhancement and QRS detection based on sparse derivatives. Biomed. Signal Process. Control., 2013, 8, 713–723.
- Jain, S., Kumar, P. and Subashini, M. M., LABVIEW based expert system for detection of heart abnormalities. In International Conference on Advances in Electrical Engineering, 2014, pp. 1–5.
- Yazdani, S. and Vesin, J.-M., Adaptive mathematical morphology for QRS fiducial points detection in the ECG. Comput. Cardiol., 2014, 38, 725–728.
- Nallathambi, G. and Principe, J. C., Integrate and fire pulse train automation for QRS detection. IEEE Trans. Biomed. Eng., 2014; 61(2), 317–326.
- Faezipour, M., Nourani, M. and Panigrahy, R., A real-time worm outbreak detection system using shared counters. In Proceedings of the 15th Annual IEEE Symposium High Performing Interconnects, Dallas, 2007, pp. 65–72.
- Song, M. H., Lee, J., Cho, S. P., Lee, K. J. and Yoo, S. K., Support vector machine based arrhythmia classification using reduced features. Int. J. Control, Autom. Syst., 2005, 3(4), 571–579.
- Besrour, R., Lachiri, Z. and Ellouze, N., ECG beat classifier using support vector machine. In Proceedings of the Third IEEE International Conference on Information and Communication Technology: From Theory Application, 2008, pp. 1–5.
- Hu, Y. H., Palreddy, S. and Tompkins, W. J., A patient adaptive ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng., 1997, 44(9), 891–900.
- de Chazal, P., O’Dwyer, M. and Reilly, R. B., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng., 2004, 51(7), 1196–1206.
- Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A. and Egiazarian, K., Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys., 2006, 28(9), 876–887.
- Haseena, H. H., Mathew, A. T. and Paul, J. K., Fuzzy clustered probabilistic and multi layered feed forward neural networks for electrocardiogram arrhythmia classification. J. Med. Syst., 2009, 29(4), 1–10.
- Chandrakar, C. and Sharma, M., A real time approach for ECG signal denoising and smoothing using adaptive window technique. In IEEE 9th International Conference on Industrial and Information Systems, Indian Institute of Information Technology, Gawlior, 2014, pp. 1–6.