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Handbook Of Human Vibration

IN view of the number of different lines of approach, a borderline subject is apt to amass a vast literature, some of it difficult of access to investigators This is particularly true of seismology ; the phenomena of earthquakes have attracted the attention of many famous geologists, and the recording of earthquake tremors involves the methods of experimental physics, while the interpretation of the records of earthquakes has provided a great stimulus to the development of the theory of the propagation of disturbances in elastic media and has ultimately thrown considerable light on the composition of the interior of the earth Many books have been written on seismology in its various aspects, but many of the most interesting modern geophysical developments have hitherto been available only in the original papers It is therefore of considerable interest to read the elegant exposition that Prof K E Bullen has presented in the book under review An Introduction to the Theory of Seismology By Prof K E Bullen Pp xiv + 276 (Cambridge: At the University Press, 1947) 18s net

An Introduction to the Theory of Seismology

Machine vision systems are used in industrial production areas to produce products with fast, perfect and high precision. These systems allow users to make highly accurate and non-contact measurements and can detect deficiencies in the production process. In this work, a machine vision based non-contact defect detection algorithm for printed circuit boards (PCBs) has been developed. In this approach, which detects and controls the holes on the PCB, first a reference image is taken from the system and feature extraction process is applied to this image. In this real-time working approach, the reference image is matched with the incoming test images and the missing holes on the PCB are precisely detected. Furthermore, it has been determined that the error amount is less than 2 μM in experimental studies. This approach, which works independently of color, position and direction, enables the defect detection process to be done very quickly and precisely.

Machine vision based defect detection approach using image processing

A smart helmet is a wearable device that has attracted attention in various fields, especially in applied sciences, where extensive studies have been conducted in the past decade. In this study, the current status and trends of smart helmet research were systematically reviewed. Five research questions were set to investigate the research status of smart helmets according to the year and application field, as well as the trend of smart helmet development in terms of types of sensors, microcontrollers, and wireless communication technology. A total of 103 academic research articles published in the past 11 years (2009–2020) were analyzed to address the research questions. The results showed that the number of smart helmet applications reported in literature has been increasing rapidly since 2018. The applications have focused mostly on ensuring the safety of motorcyclists. A single-board-based modular concept unit, such as the Arduino board, and sensor for monitoring human health have been used the most for developing smart helmets. Approximately 85% of smart helmets have been developed to date using wireless communication technology to transmit data obtained from smart helmets to other smart devices or cloud servers.

Applications of Smart Glasses in Applied Sciences: A Systematic Review

Machine vision applications are low cost and high precision measurement systems which are frequently used in production lines. With these systems that provide contactless control and measurement, production facilities are able to reach high production numbers without errors. Machine vision operations such as product counting, error control, dimension measurement can be performed through a camera. In this paper, a machine vision application is proposed, which can perform object-independent product counting. The proposed approach is based on Otsu thresholding and Hough transformation and performs automatic counting independently of product type and color. Basically one camera is used in the system. Through this camera, an image of the products passing through a conveyor is taken and various image processing algorithms are applied to these images. In this approach using images obtained from a real experimental setup, a real-time machine vision application was installed. As a result of the experimental studies performed, it has been determined that the proposed approach gives fast, accurate and reliable results.

An Image Processing based Object Counting Approach for Machine Vision Application.

The increase in population leads to increment in pollution and accident as well. Technological development makes great efforts to detect the accident and monitor the increasing pollution. This paper is based on enhancement in the smart vehicle system. Different units implemented in this paper which enhance the vehicular system. The main objective is to detect accidents in real time and minimize the response time of medical help. For accident avoidance, tire pressure is measured whereas in accident detection is implemented with the help of node MCU. MQ7 is used in order to monitor the pollution. The proposed system is useful in reducing the vehicular accidents and pollution monitoring will help to know the environmental status.

Vehicle Collision Detection and Avoidance with Pollution Monitoring System Using IoT

These Vibration amplitude may be measured as a displacement, a velocity, or acceleration. Vibration amplitude measurements may either be relative, or absolute. The basic scheme of vibration measurement is outlined first. Descriptions are given of transducers devices which transform physical variables into equivalent electrical signals and of vibration pickups. The increasing demands of higher productivity and economical design lead to higher operating speeds of machinery and efficient use of materials through lightweight structures. How we can measure vibration or earthquake signal of any device is describe in this system. These trends make the occurrence of resonant conditions more frequent during the operation of machinery and reduce the reliability of the system. Hence the periodic measurement of vibration characteristics of machinery and structures becomes essential to ensure adequate safety margins. Accelerometers develop a voltage from a piezoelectric crystal that has a mass mounted upon it. When the mass fixed to the crystal vibrates from the motion of the device upon which the sensor is attached, the crystal generates a voltage proportional to the force applied by the mass as it vibrates with the machinery. While no external excitation is required for the sensor to produce its voltage signal, the signal is small (self- generated) and requires a preamplifier.

Vibration Measurement System with Accelerometer Sensor Based on ARM

Sign language is widely used by individuals with hearing impairment to communicate with each other conveniently using hand gestures. However, non-sign-language speakers find it very difficult to communicate with those with speech or hearing impairment since it interpreters are not readily available at all times. Many countries have their own sign language, such as American Sign Language (ASL) which is mainly used in the United States and the English-speaking part of Canada. The proposed system helps non-sign-language speakers in recognising gestures used in American Sign Language. The system described in this paper is implemented using MATLAB. In this approach, firstly, the signs are captured using a webcam. The images captured are then processed further and the features are extracted from the captured images using PCA. Comparison of the features is done using Euclidean Distance with the training sets. Minimum Euclidean distance helps to recognise the character. This system will enable non-sign-language speakers to better understand and communicate with those with impaired hearing.

Online hand gesture recognition & classification for deaf & dumb

Object counting is a popular operation in computer vision. It is also useful for visual inspection. When counting objects for visual inspection count of objects with similar features is calculated. In object counting feature extraction from input image is done and count is calculated by comparing these features values with cutoff values. These cutoff values are statically specified and used for particular application. In this paper an automatic counting method is proposed which requires only tolerance to be specified to calculate count of object. The proposed method calculates cutoff values at runtime and therefore it can be easily applied to different applications. The object count generated by proposed method is useful for visual inspection of input samples. The proposed method has basic four steps as Input image, Preprocessing, Segmentation and Counting. In fourth step ‘Counting’ cutoff values are calculated to generate final object count. Proposed automatic counting method is tested on two applications and it shows high accuracy.

Automated object counting for visual inspection applications

Smart helmet safety system is the idea that has been developed for the social responsibil-ity towards the society. Recently the number of bike riders has increased so there is over-all increase in the number of motor bike acci-dents. The system assists in reducing major number of accidents. It consists of microcon-troller along with IR and PIR sensors for hel-met authentication followed by alcohol detec-tion with the help of MQ3 sensor. As soon as the bike rider try to start the bike it shall rst authenticate if the person is wearing the hel-met through IR and PIR sensors. Secondly, MQ3 sensor detects the presence of alcohol, if detected the bike wont start. It also detects fall detection through accelerometer. The project focuses on bike rider safety and reduction of accidents which are fatal.

http://esatjournals.net/ijret/2016v05/i05/IJRET20160505052.pdf

R. D. Komati

Papers

Vehicle Collision Detection and Avoidance with Pollution Monitoring System Using IoT

Vibration Measurement System with Accelerometer Sensor Based on ARM

Online hand gesture recognition & classification for deaf & dumb

Automated object counting for visual inspection applications

Smart helmet safety system using atmega 32