A novel approach to multi-sensor data synchronisation using mobile phones

Author(s):  
Jonas Wåhslén ◽  
Ibrahim Orhan ◽  
Thomas Lindh ◽  
Martin Eriksson
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.


2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.


Author(s):  
Urvish Trivedi ◽  
Jonielle McDonnough ◽  
Muhaimen Shamsi ◽  
Andrez Izurieta Ochoa ◽  
Alec Braynen ◽  
...  

Detecting humans and objects during walking has been a very difficult problem for people with visual impairment. To safely avoid collision with any object or human and to navigate from one location to another, it is significant to know how far and what kind of obstacle the user is facing. In recent years, many researches have shown that providing different vibration stimulation can be very useful to convey important information to the user. In this paper, we present our stereovision system with high definition camera to detect and identify humans and obstacles in real time and compare it with a modified version of existing wearable haptic belt that uses high-performance Ultrasonic sensors. The aim of this paper is to present the practicability of stereovision system over cane and assistive technology such as vibrotactile belt. The study is based on two assistive technologies. The first one consists of the vibrotactile belt connected to ultrasonic sensors and an accelerometer which returns user movement & speed information to the microcontroller. The microcontroller initiates expressive vibrotactile stimulation based on sensor data. Data gathered from this technology will be used as the baseline data for comparison with our stereovision system. Second, we present a novel approach to detect the type of obstacle using object recognition algorithm and the best approach to avoid it using the stereovision feedback. Data gathered from this technology with be comparted against the baseline data from the vibrotactile belt. In addition, we present the results of the comparative study which shows that stereovision system has plethora of advantages over vibrotactile belt.


Author(s):  
Alexander Astaras ◽  
Hadas Lewy ◽  
Christopher James ◽  
Artem Katasonov ◽  
Detlef Ruschin ◽  
...  

In this chapter the authors describe a novel approach to healthcare delivery for the elderly as adopted by USEFIL, a research project which uses unobtrusive, multi-parametric sensor data collection to support seniors. The system is based on everyday devices such as an in-mirror camera, smart TV, wrist-mountable personal communicator and a tablet computer strategically distributed around the house. It exploits sensor data fusion, intelligent decision support for carers, remote alerting, secure data communications and storage. A combined quantitative and qualitative knowledgebase was established and analysed, target groups were established among elderly prospective users and scenarios were built around each group. Use cases have been prioritised according to quantitative functional and non-functional criteria. Our research findings suggest that an unobtrusive system such as USEFIL could potentially make a significant difference in the quality of life of elderly people, improve the focus of provided healthcare and support their daily independent living activities.


2010 ◽  
Vol 2 (3) ◽  
pp. 28-42 ◽  
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.


2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Saleh Altowaijri ◽  
Mohamed Ayari ◽  
Yamen El Touati

By nature, some jobs are always in closed environments and employees may stay for long periods. This is the case for many professional activities such as military watch tours of borders, civilian buildings and facilities that need efficient control processes. The role assigned to personnel in such environments is usually sensitive and of high importance, especially in terms of security and protection. With this in mind, we proposed in our research a novel approach using multi-sensor technology to monitor many safety and security parameters including the health status of indoor workers, such as those in watchtowers and at guard posts. In addition, the data gathered for those employees (heart rate, temperature, eye movement, human motion, etc.) combined with the room’s sensor data (temperature, oxygen ratio, toxic gases, air quality, etc.) were saved by appropriate cloud services, which ensured easy access to the data without ignoring the privacy protection aspect of such critical material. This information can be used later by specialists to monitor the evolution of the worker’s health status as well as its cost-effectiveness, which gives the possibility to improve productivity in the workplace and general employee health.


Author(s):  
Andrzej T. Tunkiel ◽  
Tomasz Wiktorski ◽  
Dan Sui

Abstract There is an ever-increasing amount of data being recorded in oilfield operations. During drilling a well a large number of parameters is being monitored and saved, often reaching several hundreds. We are seemingly monitoring everything, from basic parameters such as Weight on Bit, Torque, and Rate of Penetration (ROP), to the exhaust temperature of engine no. 3. Unfortunately, the quality of collected data does not match the quantity. Critical sensors, such as gamma and inclination, are often lagging many meters behind the bit. Despite best efforts, sensors stop working, hard drives corrupt files, and data mud pulse telemetry uplinks fail. Methods of infilling data spanning many meters or minutes are necessary. We present a novel approach that enables reliable prediction of data lagging behind the bit through deep neural networks by merging trend-based prediction with traditional neural network approach. We were able to predict continuous inclination data in a curved section of a well with an average absolute error of only 0.4 degrees up to 20 meters from last known value.


2011 ◽  
Vol 3 (3) ◽  
pp. 14-30 ◽  
Author(s):  
Jakob Eg Larsen ◽  
Arkadiusz Stopczynski

This paper reports on the authors’ experiences with an exploratory prototype festival-wide social network. Unique 2D barcodes were applied to wristbands and mobile phones to uniquely identify the festival participants at the CO2PENHAGEN music festival in Denmark. The authors describe experiences from initial use of a set of social network applications involving participant profiles, a microblog and images shared on situated displays, and competitions created for the festival. The pilot study included 73 participants, each creating a unique profile. The novel approach had potential to enable anyone at the festival to participate in the festival-wide social network, as participants did not need any special hardware or mobile client application to be involved. The 2D barcodes was found to be a feasible low-cost approach for unique participant identification and social network interaction. Implications for the design of future systems of this nature are discussed.


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