Relevant bibliographies by topics / Sensor Classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sensor Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Sensor Classification"

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FURTADO, Luiz Felipe de Almeida, Thiago Sanna Freire SILVA, Pedro José Farias FERNANDES, and Evelyn Márcia Leão de Moraes NOVO. "Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques." Acta Amazonica 45, no. 2 (June 2015): 195–202. http://dx.doi.org/10.1590/1809-4392201401439.

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Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.
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Latifah Husni, Nyayu, Ade Silvia, Siti Nurmaini, and Irsyadi Yani. "Metal Oxides Semiconductor Sensors for Odor Classification." International Journal of Reconfigurable and Embedded Systems (IJRES) 6, no. 3 (November 1, 2017): 133. http://dx.doi.org/10.11591/ijres.v6.i3.pp133-149.

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<span>The performance of gas sensor will differ and vary due to the surrounding environment changing, the way of implementation, and the position of the sensors to the source. To reach a good result on gas sensors implementation, a performance test on sensors is needed. The results of the tests are useful for characterizing the properties of the particular material or device. This paper discusses the performances of metal oxides semiconductor (MOS) sensors. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability of the sensor when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method in order to see the sensors' sensitivity to the uncertainty disturbances, such as wind. The result shows that metal oxides semiconductor sensor was responsive to the 3 sources not only in static but also dynamic conditions. The expected outcome of this study is to predict the MOS sensors' performance when they are applied in robotic implementation. This performance was considered as the training datasets of the sensor for odor classification in this research. From the experiments, It was got, in dynamic experiment, the senrors has average of precision of 93.8-97%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the sensors were selective to the odor they sensed.</span>
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Abou Assi, Rawad, and Mohamed K. Watfa. "DEFACTO: Distributed Event ClassiFicATiOn in Wireless Sensor Networks." International Journal of Engineering and Technology 1, no. 1 (2009): 40–44. http://dx.doi.org/10.7763/ijet.2009.v1.7.

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Grybas, Heather, and Russell G. Congalton. "A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests." Remote Sensing 13, no. 13 (July 4, 2021): 2631. http://dx.doi.org/10.3390/rs13132631.

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Unmanned aerial systems (UASs) have recently become an affordable means to map forests at the species level, but research into the performance of different classification methodologies and sensors is necessary so users can make informed choices that maximize accuracy. This study investigated whether multi-temporal UAS data improved the classified accuracy of 14 species examined the optimal time-window for data collection, and compared the performance of a consumer-grade RGB sensor to that of a multispectral sensor. A time series of UAS data was collected from early spring to mid-summer and a sequence of mono-temporal and multi-temporal classifications were carried out. Kappa comparisons were conducted to ascertain whether the multi-temporal classifications significantly improved accuracy and whether there were significant differences between the RGB and multispectral classifications. The multi-temporal classification approach significantly improved accuracy; however, there was no significant benefit when more than three dates were used. Mid- to late spring imagery produced the highest accuracies, potentially due to high spectral heterogeneity between species and homogeneity within species during this time. The RGB sensor exhibited significantly higher accuracies, probably due to the blue band, which was found to be very important for classification accuracy and lacking in the multispectral sensor employed here.
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White, R. M. "A Sensor Classification Scheme." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 34, no. 2 (March 1987): 124–26. http://dx.doi.org/10.1109/t-uffc.1987.26922.

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SONG, JIFENG, and YUNJIAN GE. "BINARY TREE BASED CLASSIFICATION METHOD FOR THE MATERIAL LAYER AND MULTILINK CONVERSION MODEL OF SIGNAL PROPAGATION PROCESS OF INFORMATION ACQUISITION." International Journal of Information Acquisition 08, no. 01 (March 2011): 65–74. http://dx.doi.org/10.1142/s0219878911002318.

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This research proposed a binary tree based classification method, which classifies sensory mechanisms according to material movements. This classification is a beneficial attempt that tries to explain the working mechanisms of sensors from a global view for the material layer of the discipline of information acquisition science and technology. Almost all types of mechanisms have been expressed with the Boolean symbol. The binary expression method presented in the paper also describes the deference of sensors' performances, including accuracy and dynamic performance. One actual application was analyzed to verify the reliability of the relation concluded from the binary classification presented in this paper. The results showed that this method could be used to forecast the performances of new sensor to a certain extent, especially in the inertial sensor field. A multilink conversion conception of sensing mechanisms is put forward to characterize the error propagation phenomenon. Corresponding mathematical expressions have been set up, which indicates a way to decrease total error by reducing signal conversion links. This deduction is supported by the binary tree based classification method.
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Jayasinghe, Udeni, William S. Harwin, and Faustina Hwang. "Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification." Sensors 20, no. 1 (December 21, 2019): 82. http://dx.doi.org/10.3390/s20010082.

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Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.
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Commault, Christian, Jean-Michel Dion, Trong Hieu Do, and Do Hieu Trinh. "Sensor classification for observability preservation under sensor failure." IFAC Proceedings Volumes 42, no. 8 (2009): 408–13. http://dx.doi.org/10.3182/20090630-4-es-2003.00068.

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Rahmantyo, Wikan Haryo, and Danang Lelono. "Analisis Respons Sensor Electroni Tongue terhadap Sampel Ganja menggunakan Support Vector Machine." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 2 (October 31, 2019): 141. http://dx.doi.org/10.22146/ijeis.49173.

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Electronic tongue sensors consisting of 16 sensor array made of TOMA and OA lipids that have been used to classify samples of pure cannabis, cannabis mixed with tea and cannabis mixed with tobacco does not involve the feature selection technique so that a lot of duplicated data is generated from data sampling. Feature selection is performed using PCA. Data analysis resulted in loading values shows the contribution of each sensor, and the similarity in sensor performance in characterizing samples, then analyzed using the correlation test so that the sensors that produce redundant information are known. Validation is performed using the SVM method and the classification performance is compared to the original sensor.The sensor optimization produces a subset of features with 6 sensors (Sensor 7, Sensor 10, Sensor 12, Sensors 13, Sensor 14 and Sensor 15) in the cannabis-tea sample test and a feature subset with 3 sensors (Sensor 3, Sensor 7 and Sensor 14) in the cannabis-tobacco sample test. Sensor optimization that has been done produced classification accuracy by 100% and shorten the running time by a difference of 0.578 microseconds in the test of cannabis-tea samples and a difference of 1.696 microseconds in the test of cannabis-tobacco samples.
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Mori, Taketoshi, Ryo Urushibata, Hiroshi Noguchi, Masamichi Shimosaka, Hiromi Sanada, and Tomomasa Sato. "Sensor Arrangement for Classification of Life Activities with Pyroelectric Sensors - Arrangement to Save Sensors and to Quasi-Maximize Classification Precision." Journal of Robotics and Mechatronics 23, no. 4 (August 20, 2011): 494–504. http://dx.doi.org/10.20965/jrm.2011.p0494.

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This paper deals with the sensor arrangement for activity classification systems with a group of pyroelectric sensors to realize a system with high classification performance using as few sensors as possible. It targets the people living alone. We convert this discrete optimization problem, which means whether or not the system select a sensor position candidate, to continuous, convex, and sparse optimization problem, and solve it efficiently by extended multi-class LPBoost via column generation. For some examinations, we showed the advantage of this algorithm. We also confirmed the significance of automatic arrangement system by comparing the arrangement obtained by this algorithm with the arrangement obtained by human judge.
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Dissertations / Theses on the topic "Sensor Classification"

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Barua, Shaibal. "Multi-sensor Information Fusion for Classification of Driver's Physiological Sensor Data." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-18880.

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Physiological sensor signals analysis is common practice in medical domain for diagnosis andclassification of various physiological conditions. Clinicians’ frequently use physiologicalsensor signals to diagnose individual’s psychophysiological parameters i.e., stress tiredness,and fatigue etc. However, parameters obtained from physiological sensors could vary becauseof individual’s age, gender, physical conditions etc. and analyzing data from a single sensorcould mislead the diagnosis result. Today, one proposition is that sensor signal fusion canprovide more reliable and efficient outcome than using data from single sensor and it is alsobecoming significant in numerous diagnosis fields including medical diagnosis andclassification. Case-Based Reasoning (CBR) is another well established and recognizedmethod in health sciences. Here, an entropy based algorithm, “Multivariate MultiscaleEntropy analysis” has been selected to fuse multiple sensor signals. Other physiologicalsensor signals measurements are also taken into consideration for system evaluation. A CBRsystem is proposed to classify ‘healthy’ and ‘stressed’ persons using both fused features andother physiological i.e. Heart Rate Variability (HRV), Respiratory Sinus Arrhythmia (RSA),Finger Temperature (FT) features. The evaluation and performance analysis of the system have been done and the results ofthe classification based on data fusion and physiological measurements are presented in thisthesis work.
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Dennis, Jacob Henry. "On Quaternions and Activity Classification Across Sensor Domains." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51196.

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Activity classification based on sensor data is a challenging task. Many studies have focused on two main methods to enable activity classification; namely sensor level classification and body-model level classification. This study aims to enable activity classification across sensor domains by considering an e-textile garment and provide the groundwork for transferring the e-textile garment to a vision-based classifier. The framework is comprised of three main components that enable the successful transfer of the body-worn system to the vision-based classifier. The inter-class confusion of the activity space is quantified to allow an ideal prediction of known class accuracy for varying levels of error within the system. Methods for quantifying sensor and garment level error are undertaken to identify challenges specific to a body-worn system. These methods are then used to inform decisions related to the classification accuracy and threshold of the classifier. Using activities from a vision-based system known to the classifier, a user study was conducted to generate an observed set of activities from the body-worn system. The results indicate that the vision-based classifier used is user-independent and can successfully handle classification across sensor domains.
Master of Science
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Wang, Beng Wei. "Analysis and classification of traffic in wireless sensor networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FWang.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2007.
Thesis Advisor(s): John C. McEachen. "March 2007." Includes bibliographical references (p. 61-63). Also available in print.
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Sun, Yang. "Intelligent wireless sensor network based vehicle detection and classification /." Full text available from ProQuest UM Digital Dissertations, 2007. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=1&did=1414125751&SrchMode=1&sid=3&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1219779610&clientId=22256.

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Abdelbar, Mahi Othman Helmi Mohamed Helmi Hussein. "Applications of Sensor Fusion to Classification, Localization and Mapping." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82955.

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Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such asWireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework.
Ph. D.
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Hameed, Tariq, Ahsan Ashfaq, and Rabid Mehmood. "Intelligent Sensor." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310.

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The task is to build an intelligent sensor that can instruct a Lego robot to perform certain tasks. The sensor is mounted on the Lego robot and it contains a digital camera which takes continuous images of the front view of the robot. These images are received by an FPGA which simultaneously saves them in an external storage device (SDRAM). At one time only one image is saved and during the time it is being saved, FPGA processes the image to extract some meaningful information. In front of digital camera there are different objects. The sensor is made to classify various objects on the basis of their color. For the classification, the requirement is to implement color image segmentation based object tracking algorithm on a small Field Programmable Gate array (FPGA). For the color segmentation in the images, we are using RGB values of the pixels and with the comparison of their relative values we get the binary image which is processed to determine the shape of the object. A histogram is used to retrieve object‟s features and saves results inside the memory of FPGA which can be read by an external microcontroller with the help of serial port (RS-232).
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Tyni, Elin, and Johanna Wikberg. "Classification of Wi-Fi Sensor Data for a Smarter City : Probabilistic Classification using Bayesian Statistics." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159797.

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As cities are growing with an increasing number of residents, problems with the traffic such as congestion and larger emission arise. The city planners have challenges with making it as easy as possible for the residents to commute and in as large scale as possible to avoid vehicles. Before any improvements or reconstructions can be made, the traffic situation has to be mapped. The results from a probabilistic classification on Wi-Fi sensor data collected in an area in the southern part of Stockholm showed that some streets are more likely to be trafficked by cyclists than pedestrians while other streets showed the opposite. The goal of this thesis was to classify observations as either pedestrians or as cyclists. To do that, Bayesian statistics was applied to perform a classification. Results from a cluster analysis performed with K-means algorithm were used as prior information to a probabilistic classification model. To be able to validate the results from this unsupervised statistical learning problem, several model diagnostic methods were used. The final model passes all limits of what is considered to be a stable model and shows clear signs of convergence. The data was collected using Wi-Fi sensors which detect a device passing by when the device is searching the area for a network to connect to. This thesis will focus on data from three months. Using Wi-Fi sensors as a data collection method makes it possible to track a device. However, many manufacturers produce network interface controllers that generate randomized addresses when the device is connecting to a network, which makes it difficult to track the majority of the devices. Therefore, Wi-Fi sensor data could be seen as not suitable for this type of study. Hence it is suggested that other methods should be used in the future.
I takt med att städer växer med ökat antal invånare uppståar det problem i trafiken såsom trängsel och utsläpp av partiklar. Trafikplanerare ställs inför utmaningar i form av hur de kan underlätta pendling för invånarna och hur de, i så stor utsträckning som möjligt, kan minska fordon i tätorten. Innan potentiella förbättringar och ombyggnationer kan genomföras måste trafiken kartläggas. Resultatet från en sannolikhetsklassificering på Wi-Fi sensordata insamlat i ett område i södra delen av Stockholm visar att vissa gator är mer trafikerade av cyclister än fotgängare medan andra gator visar på motsatt föhållande. Resultatet ger en indikation på hur proportionen mellan de två grupperna kan se ut. Målet var att klassificera varje observation som antingen fotgängare eller cyklist. För att göra det har Bayesiansk statistik applicerats i form av en sannolikhetsklassifikation. Reslutatet från en klusteranalys genomförd med ”K-means clustering algorithm” användes som prior information till klassificeringsmodellen. För att kunna validera resultatet från detta ”unsupervised statistical learning” -problem, användes olika metoder för modelldiagnostik. Den valda modellen uppfyller alla krav för vad som anses vara rimligt f ̈or en stabil modell och visar tydliga tecken på konvergens. Data samlades in med Wi-Fi sensorer som upptäcker förbipasserande enheter som söker efter potentiella nätverk att koppla upp sig mot. Denna metod har visat sig inte vara den mest optimala, eftersom tillverkare idag producerar nätverkskort som genererar en slumpad adress varje gång en enhet försöker ansluta till ett nätverk. De slumpade adresserna gör det svårt att följa majoriteten av enheterna mellan sensorera, vilket gör denna typ av data olämplig för denna typ av studie. Därf ̈or föreslås att andra metoder för att samla in data används i framtiden.
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Finkele, R. "A polarimetric millimetre wave sensor system for road surface classification." Thesis, Cranfield University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284920.

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Svanström, Fredrik. "Drone Detection and Classification using Machine Learning and Sensor Fusion." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42141.

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This thesis explores the process of designing an automatic multisensordrone detection system using machine learning and sensorfusion. Besides the more common video and audio sensors, the systemalso includes a thermal infrared camera. The results show thatutilizing an infrared sensor is a feasible solution to the drone detectiontask, and even with slightly lower resolution, the performance isjust as good as a video sensor. The detector performance as a functionof the sensor-to-target distance is also investigated. Using sensor fusion, the system is made more robust than the individualsensors. It is observed that when using the proposed sensorfusion approach, the output system results are more stable, and thenumber of false detections is mitigated. A video dataset containing 650 annotated infrared and visible videosof drones, birds, airplanes and helicopters is published. Additionally,an audio dataset with the classes drones, helicopters and backgroundsis also published.
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Jonsson, Patrik. "Surface Status Classification, Utilizing Image Sensor Technology and Computer Models." Doctoral thesis, Mittuniversitetet, Avdelningen för elektronikkonstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-24828.

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There is a great need to develop systems that can continuously provide correct information about road surface status depending on the prevailing weather conditions. This will minimize accidents and optimize transportation. In this thesis different methods for the determination of the road surface status have been studied and analyzed, and suggestions of new technology are proposed. Information about the road surface status is obtained traditionally from various sensors mounted directly in the road surface. This information must then be analyzed to create automated warning systems for road users and road maintenance personnel. The purpose of this thesis is to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Another purpose is also to investigate how existing technologies can be used to obtain a more accurate description of the current road conditions. Furthermore, the aim is to develop non-contact technologies able to determine and classify road conditions over a larger area, since there is no system available today that can identify differences in road surface status in the wheel tracks and between the wheel tracks. Literature studies have been carried out to find the latest state of the art research and technology, and the research work is mainly based on empirical studies. A large part of the research has involved planning and setting up laboratory experiments to test and verify hypotheses that have emerged from the literature studies. Initially a few traditional road-mounted sensors were analyzed regarding their ability to determine the road conditions and the impact on their measured values when the sensors were exposed to contamination agents such as glycol and oil. Furthermore, non-contact methods for determining the status of the road surface have been studied. Images from cameras working in the visible range, together data from the Swedish Transportation Administration road weather stations, have been used to develop computerized road status classification models that can distinguish between a dry, wet, icy and snowy surface. Field observations have also been performed to get the ground truth for developing these models. In order to improve the ability to accurately distinguish between different surface statuses, measurement systems involving sensors working in the Near-Infrared (NIR) range have been utilized. In this thesis a new imaging method for determining road conditions with NIR camera technology is developed and described. This method was tested in a field study performed during the winter 2013-2014 with successful results. The results show that some traditional sensors could be used even with future user-friendly de-icing chemicals. The findings from using visual camera systems and meteorological parameters to determine the road status showed that they provide previously unknown information about road conditions. It was discovered that certain road conditions such as black ice is not always detectable using this technology. Therefore, research was performed that utilized the NIR region where it proved to be possible to detect and distinguish different road conditions, such as black ice. NIR camera technology was introduced in the research since the aim of the thesis was to find a method that provides information on the status of the road over a larger area. The results show that if several images taken in different spectral bands are analyzed with the support of advanced computer models, it is possible to distinguish between a dry, wet, icy and snowy surface. This resulted in the development of a NIR camera system that can distinguish between different surface statuses. Finally, two of these prototype systems for road condition classification were evaluated. These systems were installed at E14 on both sides of the border between Sweden and Norway. The results of these field tests show that this new road status classification, based on NIR imaging spectral analysis, provides new information about the status of the road surface, compared to what can be obtained from existing measurement systems, particularly for detecting differences in and between the wheel tracks.
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Books on the topic "Sensor Classification"

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Mallick, Mahendra, Vikram Krishnamurthy, and Ba-Ngu Vo, eds. Integrated Tracking, Classification, and Sensor Management. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118450550.

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Wang, Yue. Search and Classification Using Multiple Autonomous Vehicles: Decision-Making and Sensor Management. 2nd ed. London: Springer London, 2012.

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Brest, France) International Conference on Detection and Classification of Underwater Targets (2012. Proceedings of the 2012 International Conference on Detection and Classification of Underwater Targets. Newcastle upon Tyne, UK: Cambridge Scholars Publishing, 2014.

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Achkasov, Evgeniy, Yuriy Vinnik, and Svetlana Dunaevskaya. Immunopathogenesis of acute pancreatitis. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1089245.

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The monograph devoted to the study of the role of the immune system in the development and progression of acute pancreatitis consistently covers the issues of etiology, classification, diagnosis and modern treatment principles. Special attention is paid to the issues of non-specific immune protection, indicators of immune status, types of generation of reactive oxygen species in macrophage-granulocyte cells depending on the severity of acute pancreatitis. The section for assessing the structural and functional state of lymphocytes in the development of acute pancreatitis by evaluating the blebbing of the plasma membrane of the cell is presented. It is intended for General surgeons, anesthesiologists, resuscitators, residents who are trained in the specialty "Surgery". It can be useful for doctors of other specialties and senior students of higher medical schools.
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Cassiodorus, Senator, ca. 487-ca. 580., Halporn James W, and Vessey Mark, eds. Institutions of divine and secular learning: And, On the soul. Liverpool: Liverpool University Press, 2004.

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Taxonomic revision of the Chiliotrichum group sensu stricto (Compositae: Astereae). Washington, D.C: Smithsonian Institution Scholarly Press, 2009.

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Krishnamurthy, Vikram, Ba-Ngu Vo, and Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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Integrated Tracking Classification And Sensor Management Theory And Applications. IEEE Computer Society Press, 2012.

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Krishnamurthy, Vikram, Ba-Ngu Vo, and Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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Krishnamurthy, Vikram, Ba-Ngu Vo, and Mahendra Mallick. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. Wiley & Sons, Incorporated, John, 2012.

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Book chapters on the topic "Sensor Classification"

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Grattan, K. T. V., and Y. N. Ning. "Classification of optical fiber sensors." In Optical Fiber Sensor Technology, 1–35. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5787-6_1.

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Wang, Shifeng. "Multiple-Sensor Based Road Terrain Classification." In Road Terrain Classification Technology for Autonomous Vehicle, 79–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6155-5_6.

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Mallick, Mahendra, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, and Yanjun Yan. "Angle-Only Filtering in Three Dimensions." In Integrated Tracking, Classification, and Sensor Management, 1–42. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch01.

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Gning, Amadou, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic. "Particle Filtering Combined with Interval Methods for Tracking Applications." In Integrated Tracking, Classification, and Sensor Management, 43–74. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch02.

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Vo, Ba-Ngu, Ba-Tuong VO, and Daniel Clark. "Bayesian Multiple Target Filtering Using Random Finite Sets." In Integrated Tracking, Classification, and Sensor Management, 75–126. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch03.

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Blom, Henk A. P. "The Continuous Time Roots of the Interacting Multiple Model Filter." In Integrated Tracking, Classification, and Sensor Management, 127–62. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch04.

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Mallick, Mahendra, Stefano Coraluppi, and Craig Carthel. "Multitarget Tracking Using Multiple Hypothesis Tracking." In Integrated Tracking, Classification, and Sensor Management, 163–203. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch05.

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Mertens, Michael, Michael Feldmann, Martin Ulmke, and Wolfgang Koch. "Tracking and Data Fusion for Ground Surveillance." In Integrated Tracking, Classification, and Sensor Management, 203–54. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch06.

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Hernandez, Marcel. "Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications." In Integrated Tracking, Classification, and Sensor Management, 255–310. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch07.

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Davey, Samuel J., Mark G. Rutten, and Neil J. Gordon. "Track-Before-Detect Techniques." In Integrated Tracking, Classification, and Sensor Management, 311–62. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch08.

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Conference papers on the topic "Sensor Classification"

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Tamilselvan, Prasanna, Pingfeng Wang, and Byeng D. Youn. "Multi-Sensor Health Diagnosis Using Deep Belief Network Based State Classification." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48352.

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Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN) based state classification. The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and collecting sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnostics using DBN based health state classification is compared with four existing classification methods and demonstrated with two case studies.
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Xue, Xin, V. Sundararajan, and Luis Gonzalez. "Gear Condition Monitoring and Classification Using Wireless Sensor Networks." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14895.

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Current research in wireless sensor networks has chiefly focused on environmental monitoring applications. Wireless sensors are emerging as viable instrumentation techniques for industrial applications because of their flexibility, non-intrusive operation, safety and their low cost, low power characteristics. We describe a prototype gear condition monitoring system incorporating wireless sensors. Measurements of strain on gear teeth, vibration and temperature were undertaken using strain gage, accelerometer, and thermistors, respectively. The sensors interface to a sensor board that is connected to a microprocessor and a radio. Gear faults diagnosis using conventional classification techniques such as principle component analysis (PCA), Fisher linear discriminant analysis (LDA) and Nearest-Neighbor Rule (NNR) is studied in this paper. Two sets of vibration data, one set of strain data, and three sets of temperature data are used to classify a running gear under normal condition and a running gear with simulated crack teeth. Feature level data fusion is used to test the classification performance of simple but less effective features to study the fusion effects. The results show high performance of strain features, high quality of the classifier and obvious fusion effect which increases the classification performance.
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Manservigi, Lucrezia, Mauro Venturini, Giuseppe Fabio Ceschini, Giovanni Bechini, and Enzo Losi. "A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90055.

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Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS), previously developed by the authors, is improved in this paper to detect and classify different fault classes. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called I-DCIDS, can identify seven classes of fault, i.e. out of range, stuck signal, dithering, standard deviation, trend coherence, spike and bias. Fault detection is performed by means of basic mathematical laws that require some user-defined input parameters, i.e. acceptability thresholds and windows of observation. This paper presents in detail the I-DCIDS methodology for sensor fault detection and classification. Moreover, this paper reports some examples of application of the methodology to simulated data to highlight its capability to detect sensor faults which can be commonly encountered in field applications.
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Petchartee, Somrak, and Gareth Monkman. "Contact Classification using Tactile Arrays." In 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE, 2007. http://dx.doi.org/10.1109/issnip.2007.4496848.

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Rooks, Tyler F., Andrea S. Dargie, and Valeta Carol Chancey. "Machine Learning Classification of Head Impact Sensor Data." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-12173.

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Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.
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Kay, Steven, Quan Ding, and Muralidhar Rangaswamy. "Sensor integration for classification." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757820.

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Toh, Kar-Ann. "Stretchy multivariate polynomial classification." In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2015. http://dx.doi.org/10.1109/issnip.2015.7106898.

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Faiz, Farina. "Multilabel classification in human activity recognition." In SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384419.3430578.

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Toh, Kar-Ann. "Pattern classification adopting multivariate polynomials." In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2014. http://dx.doi.org/10.1109/issnip.2014.6827591.

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Fox, Maxine R., Raghu G. Raj, and Ram M. Narayanan. "Quantized wavelet scattering networks for signal classification." In Radar Sensor Technology XXIII, edited by Kenneth I. Ranney and Armin Doerry. SPIE, 2019. http://dx.doi.org/10.1117/12.2519659.

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Reports on the topic "Sensor Classification"

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Dasigi, V. R., R. C. Mann, and V. Protopopescu. Multi-sensor text classification experiments -- a comparison. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/638201.

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Everett, Mark E., and Cam Nguyen. Multi-Sensor CSEM Technology for Buried Target Classification. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada450482.

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Carin, Lawrence. Multi-Sensor Physics-Based Classification of Unexploded Ordnance. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada433734.

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Dasigi, V. R., and R. C. Mann. Toward a multi-sensor-based approach to automatic text classification. Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/130610.

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Prouty, Mark, David C. George, and Donald D. Snyder. MetalMapper: A Multi-Sensor TEM System for UXO Detection and Classification. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada578954.

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Oden, Charles P. Low-Cost Ultra-Wideband EM Sensor for UXO Detection and Classification. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada579916.

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Miyamoto, Robert, David W. Krout, and Jack McLaughlin. Distributed Environmentally-Adaptive Detection, Classification, and Localization Using a Cooperative Sensor Network. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada538746.

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Goo, Gee-In. Broadband (Ultra Wideband) Sensor System for Active and Passive Detection and Classification of Targets. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada388126.

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Nelson, Carl V., Deborah P. Mendat, Toan B. Huynh, Liane C. Ramac-Thomas, James D. Beaty, and Joseph N. Craig. Three-Dimensional Steerable Magnetic Field (3DSMF) Sensor System for Classification of Buried Metal Targets. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada476165.

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Nelson, Carl V., Deborah P. Mendat, Toan B. Huynh, Liane C. Ramac-Thomas, James D. Beaty, and Joseph N. Craig. Three-Dimensional Steerable Magnetic Field (3DSMF)Sensor System for Classification of Buried Metal Targets. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada469950.

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