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Journal articles on the topic 'Backaction of the detection system'

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Author: Grafiati

Published: 4 May 2024

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1

Nielsen, William Hvidtfelt Padkær, Yeghishe Tsaturyan, Christoffer Bo Møller, Eugene S. Polzik, and Albert Schliesser. "Multimode optomechanical system in the quantum regime." Proceedings of the National Academy of Sciences 114, no. 1 (December 20, 2016): 62–66. http://dx.doi.org/10.1073/pnas.1608412114.

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We realize a simple and robust optomechanical system with a multitude of long-lived (Q > 107) mechanical modes in a phononic-bandgap shielded membrane resonator. An optical mode of a compact Fabry–Perot resonator detects these modes’ motion with a measurement rate (96 kHz) that exceeds the mechanical decoherence rates already at moderate cryogenic temperatures (10 K). Reaching this quantum regime entails, inter alia, quantum measurement backaction exceeding thermal forces and thus strong optomechanical quantum correlations. In particular, we observe ponderomotive squeezing of the output light mediated by a multitude of mechanical resonator modes, with quantum noise suppression up to −2.4 dB (−3.6 dB if corrected for detection losses) and bandwidths ≲90 kHz. The multimode nature of the membrane and Fabry–Perot resonators will allow multimode entanglement involving electromagnetic, mechanical, and spin degrees of freedom.

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2

Pereira, S. F., Z. Y. Ou, and H. J. Kimble. "Backaction evading measurements for quantum nondemolition detection and quantum optical tapping." Physical Review Letters 72, no. 2 (January 10, 1994): 214–17. http://dx.doi.org/10.1103/physrevlett.72.214.

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3

Yan, Jia‐shun, and Jun Jing. "Backaction‐Noise Suppression and System Stabilization in Double‐Mode Optomechanical Systems." Annalen der Physik 533, no. 7 (May 27, 2021): 2100119. http://dx.doi.org/10.1002/andp.202100119.

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4

Chao, Shi‐Lei, Da‐Wei Wang, Zhen Yang, Cheng‐Song Zhao, Rui Peng, and Ling Zhou. "Backaction Evading and Amplification of Weak Force Signal in an Optomechanical System." Annalen der Physik 534, no. 4 (January 11, 2022): 2100421. http://dx.doi.org/10.1002/andp.202100421.

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5

Grangier, Philippe, Jean-François Roch, and Gérard Roger. "Observation of backaction-evading measurement of an optical intensity in a three-level atomic nonlinear system." Physical Review Letters 66, no. 11 (March 18, 1991): 1418–21. http://dx.doi.org/10.1103/physrevlett.66.1418.

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6

Xia, Ji, Fuyin Wang, Chunyan Cao, Zhengliang Hu, Heng Yang, and Shuidong Xiong. "A Nanoscale Photonic Crystal Cavity Optomechanical System for Ultrasensitive Motion Sensing." Crystals 11, no. 5 (April 21, 2021): 462. http://dx.doi.org/10.3390/cryst11050462.

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Optomechanical nanocavities open a new hybrid platform such that the interaction between an optical cavity and mechanical oscillator can be achieved on a nanophotonic scale. Owing to attractive advantages such as ultrasmall mass, high optical quality, small mode volume and flexible mechanics, a pair of coupled photonic crystal nanobeam (PCN) cavities are utilized in this paper to establish an optomechanical nanosystem, thus enabling strong optomechanical coupling effects. In coupled PCN cavities, one nanobeam with a mass meff~3 pg works as an in-plane movable mechanical oscillator at a fundamental frequency of πΩm/2π=4.148 MHz. The other nanobeam couples light to excite optical fundamental supermodes at 1542.858 and 1554.464 nm with a Qo larger than 4 × 104. Because of the optomechanical backaction arising from an optical force, abundant optomechanical phenomena in the unresolved sideband are observed in the movable nanobeam. Moreover, benefiting from the in-plane movement of the flexible nanobeam, we achieved a maximum displacement of the movable nanobeam as 1468 fm/Hz1/2. These characteristics indicate that this optomechanical nanocavity is capable of ultrasensitive motion measurements.

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7

Huang, Guanhao, Alberto Beccari, Nils J. Engelsen, and Tobias J. Kippenberg. "Room-temperature quantum optomechanics using an ultralow noise cavity." Nature 626, no. 7999 (February 14, 2024): 512–16. http://dx.doi.org/10.1038/s41586-023-06997-3.

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AbstractAt room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscillations are controlled by the material rigidity, the observation of these effects has been hindered by low mechanical quality factors, optical cavity frequency fluctuations3, thermal intermodulation noise4,5 and photothermal instabilities. Here we overcome these challenges with a phononic-engineered membrane-in-the-middle system. By using phononic-crystal-patterned cavity mirrors, we reduce the cavity frequency noise by more than 700-fold. In this ultralow noise cavity, we insert a membrane resonator with high thermal conductance and a quality factor (Q) of 180 million, engineered using recently developed soft-clamping techniques6,7. These advances enable the operation of the system within a factor of 2.5 of the Heisenberg limit for displacement sensing8, leading to the squeezing of the probe laser by 1.09(1) dB below the vacuum fluctuations. Moreover, the long thermal decoherence time of the membrane oscillator (30 vibrational periods) enables us to prepare conditional displaced thermal states of motion with an occupation of 0.97(2) phonons using a multimode Kalman filter. Our work extends the quantum control of solid-state macroscopic oscillators to room temperature.

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Raju, Rajeswari. "Jaundice Detection System." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 1.5 (November 15, 2019): 127–31. http://dx.doi.org/10.30534/ijatcse/2019/2581.52019.

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9

Tatarnikov, Denis A., and Aleksey V. Godovykh. "Radiation Detection System." Advanced Materials Research 1040 (September 2014): 980–84. http://dx.doi.org/10.4028/www.scientific.net/amr.1040.980.

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<span><p class="TTPAbstract"><span lang="EN-US">This paper is devoted to the project of making own radiation detection system with some unique features and to make the system more independent for their components, highly-scalable and flexible platform. We develop programs for </span><span lang="DE">collecting and displaying the gamma data on the plot from all of the connected detectors to the system, record them for further post-processing</span><span lang="EN-US"> and </span><span lang="DE">displaying them to user as a breadcrumb on the map.</span></p>

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10

Rana, Harshil, and Reema Pandya. "Pest Detection System." International Journal of Computer Sciences and Engineering 9, no. 12 (December 31, 2021): 23–25. http://dx.doi.org/10.26438/ijcse/v9i12.2325.

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11

LeBrasseur, Nicole. "Peroxide detection system." Journal of Cell Biology 175, no. 5 (December 4, 2006): 676a. http://dx.doi.org/10.1083/jcb.1755iti4.

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12

Srour, Nassay, and Michael V. Scanlon. "Acoustic detection system." Journal of the Acoustical Society of America 90, no. 3 (September 1991): 1713. http://dx.doi.org/10.1121/1.401692.

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13

Yoshida, Takashi, Takashi Yoshioka, and Yasuhiko Enda. "Underwater detection system." Journal of the Acoustical Society of America 83, no. 2 (February 1988): 841. http://dx.doi.org/10.1121/1.396096.

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14

Tsuji, Akio. "Obstacle detection system." Journal of the Acoustical Society of America 83, no. 1 (January 1988): 400. http://dx.doi.org/10.1121/1.396231.

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15

Yokioka, Takashi, Nobuo Fujita, and Hiroyaki Hamato. "Underwater detection system." Journal of the Acoustical Society of America 84, no. 4 (October 1988): 1577. http://dx.doi.org/10.1121/1.396575.

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16

Gershon, Diane. "Detection system digest." Nature 364, no. 6438 (August 1993): 653–55. http://dx.doi.org/10.1038/364653a0.

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17

Shetty, Likith. "Diabetes Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 2179–83. http://dx.doi.org/10.22214/ijraset.2023.55549.

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Abstract: Diabetes is a chronic illness that interferes with your body'sability to control blood sugar or glucose levels. Our body' cells depend on glucose as a major energy source, and the hormone insulin, which the pancreas produces, aids in controlling glucose levels. This research suggests a machine learning based diabetes detection system. The method makes use of a dataset of patient records that detail several clinical traits and whether diabetes is present or not. The most crucial characteristics for predicting diabetes are determined via a feature selection technique. The dataset is then used to train and evaluate machine learning techniques such as Random Forest, Support Vector Machine, KNN and Logistic Regression. The model’s performance can be evaluated using certain metrics. The outcomes demonstrate that the suggested system beats the numerous baseline models and has good predictive accuracies for diabetes using Random Forest algorithm. This approach may be helpful for detecting diabetes in early stage and can helpin improving the outcome of patients.

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Mudaraddi, Kiran, Keerthi T, Likitha Yadav, M. Varsha, and Nivedita M. "Weapon Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3482–87. http://dx.doi.org/10.22214/ijraset.2023.52381.

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Abstract: Security is always a main concern in every sphere, due to a rise in crime rate in a crowded event or suspicious lonely areas. Anomalydetection and observance have major applications of computer vision to gear various problems. Due to demand in the protection of safety, security of private properties placement of surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence observance. Detection of weapon and militant using convolution neural network (CNN). Proposed implementation uses two types of datasets. One dataset contains pre-labelled images. And the other one labelled manually contains a set of images. Results are tabulated, both algorithms achieve good efficiency, but their operation in real situations can be based on the trade-off between speed and efficiency. Crime is defined as an act dangerous not only to the person involved, but also to the community as a whole. It is to predict the crime using image dataset and finally calculate accurate performance of the detector. The propose algorithms that are able to alert the human operator when a weapon and militant is visible in the image. It is mainly focusedon limiting the number of false alarms in order to allow for real life application of the system. For future work, it is planned to use in live applicationand to improve the detection and reduce the crime.

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19

Khamkar, Pratik. "Vehicle Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4937–41. http://dx.doi.org/10.22214/ijraset.2023.51370.

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Abstract: Now a days there are increase in accidents everyday so to minimize the accidents rate some system must be used to avoid the accident and save the lives of peoples. We are aware of many security systems that are available in the market still they are not used in certain areas and suitable for environmental condition. We try to provide a solution by making a cheap system which has the capability to sensing the motion of vehicles. The idea behind this project is that when the car came in the range of sensor it gives signal to other car, to stop or go. This project involves the use of Arduino Uno, IR sensor, LED light & Buzzer. The IR sensor detect any motion in its range and triggers the buzzer. With this system we can avoid the collision of vehicle and life of people.

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20

Soma, Shridevi, Meghana Suryan, Nandini Jattur, and Amruta Rasalkar. "Fire Detection System." International Journal of Computer Applications 185, no. 24 (July 28, 2023): 22–26. http://dx.doi.org/10.5120/ijca2023922993.

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21

Jain, Ashish, Anmol Kumar Pandey, and Aniket Saini. "Plagiarism Detection System." International Journal of Computer Applications 185, no. 5 (April 28, 2023): 1–3. http://dx.doi.org/10.5120/ijca2023922652.

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22

Alagi, Prof Sangeeta, Vishakha Shukla, Vinay Sonawne, Kaveri Shinde, Rahil Shaikh, and Sumit Narwade. "ASD Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 1297–300. http://dx.doi.org/10.22214/ijraset.2023.56797.

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Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that presents a wide range of challenges for affected individuals and their families. Early detection and intervention are crucial in improving outcomes for individuals with ASD. This abstract introduces the development of an innovative ASD detection system, which combines advanced technology and machine learning techniques to assist in early diagnosis. The ASD detection system leverages various data sources, including behavioral observations, medical records, and genetic information, to create a comprehensive profile of individuals at risk for ASD. It utilizes sophisticated machine learning algorithms to analyze and interpret this data, aiming to identify subtle patterns and markers associated with ASD. The system's user-friendly interface allows healthcare professionals to input and access data easily, streamlining the diagnostic process.

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23

Bhandare, Prof Mrs H. N. "Threat Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 30, 2024): 1–13. http://dx.doi.org/10.55041/ijsrem28413.

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This research paper is about to control the engine valves of an one cylinder 4-stroke engine with a computer controlled electromagnetic actuator. There are many possibilities in electromagnetic devices. We chose a push solenoid to actuate the engine valve. For controlling the solenoid, we chose a user interface with control options. The user interface communicates serially with a microprocessor. The microprocessor monitors and reports the engine’s performance and control the opening/closing of the engine valves. The ultimate goal is improved efficiency, decrease pollutants, and produce maximum power throughout the RPM range with a camless engine. Key Words: Behavior Analysis, Signature- Based Detection, Intrusion Prevention, Log Analysis.

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24

Singh, Amit, Jay Prakash, Gaurav Kumar, Praphula Kumar Jain, and Loknath Sai Ambati. "Intrusion Detection System." Journal of Database Management 35, no. 1 (February 14, 2024): 1–25. http://dx.doi.org/10.4018/jdm.338276.

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The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.

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Journal, IJSREM. "Depression Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27074.

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The Depression Detection System is an innovative technology designed to assist in the early detection and monitoring of depression. This system utilizes advanced machine learning algorithms and data analysis techniques to analyze various indicators andpatterns associated with depression, such as speech, facial expressions, and behavior. By continuously monitoring these signals, the system can identify potential signs of depression and provide valuable insights to healthcare professionals for accuratediagnosis and timely intervention. Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. The system operates by collecting data from various sources, including audio recordings of conversations, video recordings of facial expressions, and user- generated content from social media platforms. Through natural language processing and computer vision techniques, it extracts relevant features and patterns that may indicate depressive symptoms, such as changes in speech patterns, facial expressions of sadness or despair, or negative sentiment expressed in written content. Once the data is analyzed, the system generates comprehensive reports and visualizations, highlighting potential depressive indicators and their severity levels. These reports can assist healthcare providers in making informed decisions regarding further assessment and treatment planning. Additionally, the system can track the progression of depression over time, enabling clinicians to monitor the effectiveness of interventions and adjust treatment strategies accordingly. The Depression Detection System aims to complement traditional diagnostic methods by providing an objective and continuous assessment of depressive symptoms. By leveraging cutting-edge technology, this system has the potential to improve early detection rates, enhance patient outcomes, and facilitate timely interventions in the field of mental health. However, it is important to note that the system should always be used as a supportive tool and not as a replacement for professional clinical judgment and human interaction.

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26

Kuramochi, Yui, Yu Watanabe, and Masahito Ueda. "Simultaneous continuous measurement of photon-counting and homodyne detection on a free photon field: dynamics of state reduction and the mutual influence of measurement backaction." Journal of Physics A: Mathematical and Theoretical 46, no. 42 (October 7, 2013): 425303. http://dx.doi.org/10.1088/1751-8113/46/42/425303.

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27

Jabez, J., and B. Muthukumar. "Intrusion Detection System (IDS): Anomaly Detection Using Outlier Detection Approach." Procedia Computer Science 48 (2015): 338–46. http://dx.doi.org/10.1016/j.procs.2015.04.191.

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28

Barello, Philip, and Md Shafaeat Hossain. "Multimodal person detection system." Multimedia Tools and Applications 80, no. 9 (January 14, 2021): 13389–406. http://dx.doi.org/10.1007/s11042-020-10307-8.

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29

Kartha, Sreelakshmi. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 818–22. http://dx.doi.org/10.22214/ijraset.2021.34304.

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30

J. S, Jayasenan, and Mrs Smitha P. S. "Driver Drowsiness Detection System." IOSR journal of VLSI and Signal Processing 4, no. 1 (2014): 34–37. http://dx.doi.org/10.9790/4200-04113437.

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31

Sunder, Adithya, Nithin Kumar, Santhosh B Rao, Shashank P, and Mary Vidya John. "BREAST CANCER DETECTION SYSTEM." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (August 12, 2022): 233–35. http://dx.doi.org/10.26562/ijirae.2022.v0908.16.

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Machine learning and soft computing methods have been used in several studies to treat breast cancer. Countless groups assert that their algorithms are superior to those of others in terms of speed, simplicity, or accuracy. This study develops a system that can correctly identify between benign and malignant breast tumours using machine learning techniques. The research's aim was to make the learning algorithm better. To select the ideal features and parameter values for machine learning classifiers in this scenario, we used regression. Accuracy, precision and recall were the performance metrics.

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32

Jersha Felix, V., Purohitam Adithya Udaykiran, and K. Ganesan. "Fuel Adulteration Detection System." Indian Journal of Science and Technology 8, S2 (January 1, 2015): 90. http://dx.doi.org/10.17485/ijst/2015/v8is2/59076.

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33

H, Abhimanyu, and Biju Balakrishnan. "Heart Attack Detection System." IJARCCE 8, no. 2 (February 28, 2019): 204–6. http://dx.doi.org/10.17148/ijarcce.2019.8237.

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S, Manjunath, Banashree P, Shreya M, Sneha Manjunath Hegde, and Nischal H P. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 129–35. http://dx.doi.org/10.22214/ijraset.2022.42109.

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Abstract: Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the relationship between facial features and a driver’s drowsy state, variables that reflect facial features have been established. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver’s level of drowsiness. Keywords: Drowsiness, OpenCV, Dlib, facial features, video processing

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35

Chana, Bindiya. "nEXO light detection system." Journal of Physics: Conference Series 2156, no. 1 (December 1, 2021): 012203. http://dx.doi.org/10.1088/1742-6596/2156/1/012203.

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Abstract nEXO is a future 5-tonne scale liquid xenon time projection chamber (TPC) experiment looking for hypothetical neutrinoless double beta decay of isotope 136Xe. To attain the projected half-life sensitivity of 1028 years, it aims to achieve an energy resolution of 1% or better at the Q-value (Qββ = 2.458 MeV) of the decay. nEXO plans to employ silicon photomultipliers (SiPMs) on the lateral surface of the cylindrical TPC to detect the light signals. Newly developed SiPMs sensitive to vacuum ultraviolet (VUV) light will be directly used for the detection of scintillation photons (λ = 175nm) in liquid xenon. For achieving the target energy resolution, the light detection system must have high photon detection efficiency, low correlated avalanche noise and low dark noise rate. The SiPM devices from two vendors are considered for the light detection system in the experiment. The primary goal of this research project is to characterize the VUV-SiPMs and measure their various features like gain, crosstalk, afterpulsing, dark noise rate, reflectivity and photon detection efficiency. Along with all these measurements, a monitoring tool will be required to test the large number of SiPMs before installing them in the detector. Current-voltage(IV) curve characterisation is being explored as a quick quality-testing tool for the performance of SiPM.

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Gangesh, Manisha. "Diabetic Retinopathy Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1498–502. http://dx.doi.org/10.22214/ijraset.2021.38233.

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Abstract: Diabetic Retinopathy is a diabetes problem that affects the eye. Injury to the blood vessels of the light sensitive tissue inside the rear of the eye (retina) is that the most reason for diabetic retinopathy. To begin with, Diabetic Retinopathy may have no symptoms or just cause minor vision problems. It has the potential to lead to blindness. Machine learning approaches can be used for the early detection of Diabetic Retinopathy. This paper proposes an automated Diabetic Retinopathy detection system that can detect the presence of Diabetic Retinopathy from retinal images. This work uses ResNet50 for the detection and classification of Diabetic Retinopathy. ResNet50 is a type of neural network used as a backbone for many computer-vision tasks. This paper proposes a machine learning model which is developed using ResNet50, then the model will be deployed as a user-friendly web application where the user can upload the retinal images as input to the system then system will detect the presence of Diabetic Retinopathy and classifies it into the stage or class which the particular image belongs to. Keywords: Diabetic Retinopathy, ResNet50, Proliferative diabetic retinopathy, non-proliferative diabetic retinopathy.

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Akshay, Akshat Divya, Anchit Bhushan, Nihal Anand, Rishabh Khemka, and Sumithra Devi K.A. "HONEYPOT: Intrusion Detection System." International Journal of Education, Science, Technology, and Engineering 3, no. 1 (April 24, 2020): 13–18. http://dx.doi.org/10.36079/lamintang.ijeste-0301.66.

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The number of computers connecting to the internet is getting increased day by day, while the number of computers connected is increasing then it is obvious that the amount of network-based attacks will also increase. In this way, we use a honeypot that is a framework trap that is set to act against unapproved utilization of PCs and data frameworks. Around the globe, a huge number of individuals get to the web each day, honeypot which can likewise be called Intrusion Detection Technology is another time of security innovation that screens device to avoid malicious sports. The whole factor of this research paper is an Intrusion Detection System and Intrusion Prevention System, elements accomplished via honeypot and honeytrap methodologies. A great deal of research went into this review paper and the discoveries propose that the honeypots are drawing in light of a legitimate concern for analysts as a significant security system that can be actualized to stop or occupy the assaults the system assaults and give a chance to find out increasingly more about the source and nature of these assaults. Hence we can say that a honeypot can be utilized as an examination apparatus to accumulate increasingly more data about the expanding number of system assaults that are going on consistently.

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Suryawanshi, Yashaswini. "Driver’s Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 1798–802. http://dx.doi.org/10.22214/ijraset.2021.34668.

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Jog, Prof Pranjal. "Automatic Alcohol Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1273–79. http://dx.doi.org/10.22214/ijraset.2021.37555.

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Abstract: In pretty much every industry and field, innovation keeps on disturbing old frameworks and opening up new pathways. Not more so than in the field of law enforcement, where analysts, designers, and tech virtuosos are chipping away at further developed apparatuses not exclusively to uphold DUI, yet additionally to forestall it. Maybe the most encouraging of these drives is the Alcohol Safety Detection System, fostering an innovation that will consequently keep an intoxicated driver from driving an engine vehicle, an attempt will be made to fabricate a locking mechanism for vehicles so it would not begin without an Alcohol detection system. This paper portrays a driver alcohol concentration detection framework dependent on breath testing, created utilizing a microcontroller Compatible Compiler, that permits the program of microcontroller boards. The framework can gauge the liquor from the breath test and control the activity of the vehicle start framework to forestall smashed driving. Additionally, the utilization of virtual instrumentation gives high adaptability, in contrast to traditional methods. Drunken driving has become a significant problem in present-day culture. It is a typical reason for vehicle crashes including human mistakes. This venture focused on developing a system to prevent, in anticipation of making everyday traffic safe. Keywords: Alcohol safety detection system, MQ3 sensor, Arduino UNO.

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(Kumbhar), Prof A. S. Salavi. "Brain Tumor Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1903–17. http://dx.doi.org/10.22214/ijraset.2021.38286.

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Abstract: Brain tumor is an abnormal growth of brain cells within the brain. Detection of brain tumor is a challenging problem, due to complex structure of the brain. The automatic segmentation has great potential in clinical medicine by freeing physicians from the burden of manual labeling; whereas only a quantitative measurement allows to track and modeling precisely the disease. Magnetic resonance (MR) images are an awfully valuable tool to determine the tumor growth in brain. But, accurate brain image segmentation is a complicated and time consuming process. MR is generally more sensitive in detecting brain abnormalities during the early stages of disease, and is excellent in early detection of cases of cerebral infarction, brain tumors, or infections. So, in this project we put forward a method for automatic brain tumor diagnostics using MR images. The proposed system identifies and segments the tumor portions of the images successfully. Keywords: MR, 2D Image, BrainTumor

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Sonwane, Prof A. T. "Face Mask Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 639–42. http://dx.doi.org/10.22214/ijraset.2021.38855.

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Abstract: There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. The risk of transmission is highest in public places. However, there are only a few research studies about face mask detection based on image analysis. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network, TensorFlow and OpenCV to detect face masks on people. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society. Keywords: COVID-19, Tensorflow, OpenCV, Face Mask, Image Processing, Computer Vision

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V, Kiranmayee. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1226–31. http://dx.doi.org/10.22214/ijraset.2021.36124.

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Drowsiness of drivers are among the critical reasons for accidents. This can be a relatively smaller number still, as among the multiple causes that can lead to an accident. Drowsiness, in general, is not easy to measure unlike drugs and alcohol, which have tests and indicators that are available easily. In this paper, we are presenting a module for Advanced Driver Assistance System (ADAS) to reduce drowsiness related accidents. The system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to track, analyze and locate both the drivers eyes and face to measure PERCLOS, a scientifically supported measure of drowsiness asso- ciated with slow eye closure.

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Khansali, Yatharth. "Face Mask Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1400–1402. http://dx.doi.org/10.22214/ijraset.2021.36584.

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COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.

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Tripathi, Mrs Garima. "LPG Leakage Detection System." International Journal for Research in Applied Science and Engineering Technology 6, no. 7 (July 31, 2018): 79–82. http://dx.doi.org/10.22214/ijraset.2018.7012.

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Boob, Snehal, and Priyanka Jadhav. "Wireless Intrusion Detection System." International Journal of Computer Applications 5, no. 8 (August 10, 2010): 9–13. http://dx.doi.org/10.5120/934-1312.

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Harlow, Charles, and Yu Wang. "Automated Accident Detection System." Transportation Research Record: Journal of the Transportation Research Board 1746, no. 1 (January 2001): 90–93. http://dx.doi.org/10.3141/1746-12.

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Chandrashekar, Raksha. "Milk Adulteration Detection System." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 371–77. http://dx.doi.org/10.22214/ijraset.2019.6063.

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Harlow, Charles, and Yu Wang. "Acoustic Accident Detection System." Journal of Intelligent Transportation Systems 7, no. 1 (January 2002): 43–56. http://dx.doi.org/10.1080/713930746.

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Penev, Ivaylo. "Robot Self-Detection System." Advances in Science, Technology and Engineering Systems Journal 3, no. 6 (2018): 391–402. http://dx.doi.org/10.25046/aj030647.

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Maccabee, Bruce S. "Underwater object detection system." Journal of the Acoustical Society of America 91, no. 5 (May 1992): 3081. http://dx.doi.org/10.1121/1.402901.

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