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Relevant bibliographies by topics / Jaws Signal processing

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Academic literature on the topic 'Jaws Signal processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Jaws Signal processing"

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Wang, Hui Jun, and Guan Li. "A Design of the Bone Conduction Ultrasonic Hearing Device." Advanced Materials Research 1030-1032 (September 2014): 2330–33. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2330.

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In view of the fact that the traditional gas conduction hearing AIDS does not function to the patients with ear canal jams, this paper introduces a kind of ultrasonic hearing devices. Through the ultrasonic voice signals sent by the bone conduction modulation, the hearing-impaired patients can get a certain degree of hearing. The device, with TM320VC5410 as signal processing unit, modulates the voice signals with ultrasonic and transmit the signals through the bone conduction headphone to human auditory nerves. The experimental results show that the hearing devices can help patients with severe deafness recognize sound and voice. As a result, it is of high application value.

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Moore, Jonathan, and Adam Hill. "Dynamic Diffuse Signal Processing for Sound Reinforcement and Reproduction." Journal of the Audio Engineering Society 66, no. 11 (November 16, 2018): 953–65. http://dx.doi.org/10.17743/jaes.2018.0054.

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Snyder, Heidi Link, Igor Bačík, Jack R. Bennink, Grainne Kearns, Timothy W. Behrens, Thomas Bächi, Marian Orlowski, and Jonathan W. Yewdell. "Two Novel Routes of Transporter Associated with Antigen Processing (TAP)-independent Major Histocompatibility Complex Class I Antigen Processing." Journal of Experimental Medicine 186, no. 7 (October 6, 1997): 1087–98. http://dx.doi.org/10.1084/jem.186.7.1087.

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Jaw1 is an endoplasmic reticulum (ER) resident protein representative of a class of proteins post translationally inserted into membranes via a type II membrane anchor (cytosolic NH2 domain, lumenal COOH domain) in a translocon-independent manner. We found that Jaw1 can efficiently deliver a COOH-terminal antigenic peptide to class I molecules in transporter associated with antigen processing (TAP)-deficient cells or cells in which TAP is inactivated by the ICP47 protein. Peptide delivery mediated by Jaw1 to class I molecules was equal or better than that mediated by the adenovirus E3/19K glycoprotein signal sequence, and was sufficient to enable cytofluorographic detection of newly recruited thermostabile class I molecules at the surface of TAP-deficient cells. Deletion of the transmembrane region retargeted Jaw1 from the ER to the cytosol, and severely, although incompletely, abrogated its TAP-independent peptide carrier activity. Use of different protease inhibitors revealed the involvement of a nonproteasomal protease in the TAP-independent activity of cytosolic Jaw1. These findings demonstrate two novel TAP-independent routes of antigen processing; one based on highly efficient peptide liberation from the COOH terminus of membrane proteins in the ER, the other on delivery of a cytosolic protein to the ER by an unknown route.

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Abdullah, S., M. Loman, and N. Jamaluddin. "Signal Processing Method for Evaluating Fatigue Damage in a Piping System." Journal of Applied Sciences 9, no. 13 (June 15, 2009): 2381–89. http://dx.doi.org/10.3923/jas.2009.2381.2389.

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Zhang, Mingfeng, Hengwei Lu, Gang Ren, Sarah Smith, James Beauchamp, and Mark Bocko. "A Matlab-Based Signal Processing Toolbox for the Characterization and Analysis of Musical Vibrato." Journal of the Audio Engineering Society 65, no. 5 (May 26, 2017): 408–22. http://dx.doi.org/10.17743/jaes.2017.0010.

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Sun, Meng-Jia, Yingying Liu, Yaming Yan, Rui Li, Qiang Shi, Yong Sheng Zhao, Yu-Wu Zhong, and Jiannian Yao. "In Situ Visualization of Assembly and Photonic Signal Processing in a Triplet Light-Harvesting Nanosystem." Journal of the American Chemical Society 140, no. 12 (March 9, 2018): 4269–78. http://dx.doi.org/10.1021/jacs.7b12519.

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Castello, M. E., P. A. Aguilera, O. Trujillo-Cenoz, and A. A. Caputi. "Electroreception in Gymnotus carapo: pre-receptor processing and the distribution of electroreceptor types." Journal of Experimental Biology 203, no. 21 (November 1, 2000): 3279–87. http://dx.doi.org/10.1242/jeb.203.21.3279.

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This paper describes the peripheral mechanisms involved in signal processing of self- and conspecific-generated electric fields by the electric fish Gymnotus carapo. The distribution of the different types of tuberous electroreceptor and the occurrence of particular electric field patterns close to the body of the fish were studied. The density of tuberous electroreceptors was found to be maximal on the jaw (foveal region) and very high on the dorsal region of the snout (parafoveal region), decaying caudally. Tuberous type II electroreceptors were much more abundant than type I electroreceptors. Type I electroreceptors occurred exclusively on the head and rostral trunk regions, while type II electroreceptors were found along as much as 90 % of the fish. Electrophysiological data indicated that conspecific- and self-generated electric currents are ‘funnelled’ by the high conductivity and geometry of the body of the fish. These currents are concentrated at the peri-oral zone, where most electroreceptors are located. Moreover, within this region, field vector directions were collimated, constituting the most efficient stimulus for electroreceptors. It can be concluded that the passive properties of the fish tissue represent a pre-receptor device that enhances exafferent and reafferent electrical signals at the fovea-parafoveal region.

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Gil Jiménez, Víctor P., and M. Julia Fernández-Getino García. "Simple Design of Wireless Sensor Networks for Traffic Jams Avoidance." Journal of Sensors 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/380794.

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Intelligent transportation systems (ITS) are usually approached by exhaustive measuring and complex signal processing including medium-high cost hardware deployment. In this paper, a novel design of a wireless sensor network system using magnetometers and microphones for the detection and avoidance of traffic jams is described and analyzed. The system, which can also be used for traffic monitoring and surveillance, is simple, energy efficient, and accurate which allows to be implemented with a reduced hardware cost. In order to reduce the maintenance tasks, mini solar panels would also be installed for powering up the motes in the near future.

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Jebashini, P., R. Uma, P. Dhavachelv, and Hon Kah Wye. "A Survey and Comparative Analysis of Multiply-Accumulate (MAC) Block for Digital Signal Processing Application on ASIC and FPGA." Journal of Applied Sciences 15, no. 7 (June 15, 2015): 934–46. http://dx.doi.org/10.3923/jas.2015.934.946.

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Sonmezocak, Temel, and Serkan Kurt. "Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles." Biomedical Signal Processing and Control 69 (August 2021): 102905. http://dx.doi.org/10.1016/j.bspc.2021.102905.

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Dissertations / Theses on the topic "Jaws Signal processing"

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Mutirangura, Wantanee. "Computer-analysis of the cross-correlation of the bilateral jaw elevator activity during controlled submaximal clenching a thesis submitted in partial fulfillment ... restorative dentistry, occlusion /." 1988. http://books.google.com/books?id=H3s9AAAAMAAJ.

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Fu, Bin. "Classification of multi-channel EMGs for jaw motion recognition by signal processing and artificial neural networks." Thesis, 2004. http://hdl.handle.net/2429/17069.

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This thesis presents an EMG pattern recognition method to identify jaw movements. Pattern recognition is carried out using backpropagation artificial neural networks (BPN) trained by supervised learning. Different feature extraction methods have been implemented. Results are presented to support the feasibility of the suggested approach. Electromyography (EMG) is electrical activity of muscle. Bruxism is the involuntary and excessive clenching and grinding of teeth. It is one of the reasons that cause serious teeth damage and jaw muscle disorder and currently there is no definitive cure. Knowing actual jaw actions during bruxism will help in designing a more targeted treatment mode. EMG provides information about the neuromuscular activity from which it originates. When performing different muscle contractions, different EMGs are detected. Different muscle contractions are related to various movement tasks. Therefore it is possible to process the EMG to obtain movement classification. The purpose of this study is to design an algorithm to detect jaw motion (simulated bruxism) from the EMG signals. The process consists of feeding the EMG signals to a feature extraction block; and then the extracted features are supplied to an artificial neural network (ANN). The ANN classifies the motion into six categories: left, right and forward with two speeds, fast and slow. The application of this algorithm can provide the basis for a clinical evaluation of bruxism. In this study, three feature extraction techniques have been implemented for comparative analysis: EMG linear envelope (LE), autoregressive modelling (AR parameter estimation) and a hybrid approach of the AR model and discrete wavelet transform (DWT). The performance of the three methods has been evaluated. The ANN structure consists another part of the study. While both linear envelope method and AR model method were able to classify the direction of jaw movement, each confused with the fast and slow speed at the same direction to some extent. To solve the problem, a new ANN structure was designed for the linear envelope method by adding more features like the duration and the integral information of the waveform These features work as bias to the ANN structure and they not only yield high correct classification rates but also strengthen the robustness of the ANN. Finally the classification performance of the algorithm was checked in 10 healthy subjects and reasonable results were obtained. These were best with downsampled LE with the ANN (structure III).
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

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Book chapters on the topic "Jaws Signal processing"

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Aswathy S. U., Ajesh F., and Felix M. Philip. "Using IoTs-Based Monitoring System in a Smart Ambulance for E-Healtcare Applications." In Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality, 42–67. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4703-8.ch002.

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In today's world, traffic jams during rush hours are a major concern. During rush hours, emergency vehicles like ambulances get stuck in jams. The smart ambulance systems are most suitable to provide clearance to emergency vehicles during rush hours. Many systems are used to implement the smart ambulance systems. The primary objective is to identify the emergency vehicle and track its location so that wireless signals to the emergency vehicles can be provided. Conventional technologies use image processing systems to identify the emergency vehicle. But these systems have a drawback during bad weather conditions. Due to wind, rain, fog, etc., the image received by the camera is distorted by noise and it becomes difficult for the system to identify the desired vehicle. In this chapter, the authors discuss in detail the existing techniques on smart ambulance based on IoT using zig bee, GSM module, Adriano, Raspberry Pi, etc., its drawback, and its future scope.

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Conference papers on the topic "Jaws Signal processing"

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Zhang, Songze, Junjie Xie, and Hongjian Shi. "Jaw Segmentation from CBCT Images." In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). IEEE, 2018. http://dx.doi.org/10.1109/icdsp.2018.8631819.

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"FC-BASED SEGMENTATION OF JAW TISSUES." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002698804090414.

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Ndukwe, E., and G. Anitha. "The human jaw movement analysis by finite element analysis." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754214.

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DIOP, Abdou Khadre, Amadou Dahirou Gueye, Khaly Tall, and Sidi Mohamed Farssi. "Measuring Similarity in CCTV Systems for a Real-time Assessment of Traffic Jams." In 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2020. http://dx.doi.org/10.1109/icicsp50920.2020.9232098.

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Fernando, Heshan, Vedang Chauhan, and Brian Surgenor. "Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20102.

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This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

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He, Ying, Yong Zhao, Dongmei Jiang, and Hichem Sahli. "Speech driven photo-realistic face animation with mouth and jaw dynamics." In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2013. http://dx.doi.org/10.1109/apsipa.2013.6694186.

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Sanatkhani, Soroosh, and Prahlad G. Menon. "Three-Dimensional Cephalometric Analysis Using Computed Tomographic Imaging." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88259.

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Successful outcomes from the use of orthodontic devices are underpinned on their effective anchorage and the loading that they apply to the underlying facial structures. Anchorage plays an important role in determining the point of application of the corrective forces and subsequently the orientation of the resultant of these forces, which in-turn governs the outcome of treatment. Therefore, patient-specific design of anchors and their placement may benefit significantly from personalization using patient-specific and three-dimensional (3D) cephalometry. 3D cephalometry is therefore a first step to personalization of orthodontic treatment. In this feasibility study, we demonstrate the viability a novel image processing and surface analysis pipeline to quantify facial symmetry about the mid-sagittal facial plane, which may offer insight into optimal placement and orientation for implantation of orthodontic anchors, starting with patient-specific cone beam computed tomography (CBCT) images. Typical assessments of geometrical features/attributes of face include size, position, orientation, shape, and symmetry. Using 3D CBCT images in the DICOM image format, skull images were first segmented using a basic iso-contouring approach. To quantify symmetry, we split the skull along the mid-sagittal plane and used an iterative closest point (ICP) approach in order to rigidly co-register the left and right sides of the skull, optimizing for rotation, translation and scaling, after reflection of one half across the mid-sagittal plane. This was accomplished using an in-house plugin is developed for the open-source visualization toolkit (VTK) based 3D visualization tool, Paraview (Kitware Inc.). Finally, using a signed regional distance mapping plugin we were able to assess the regional asymmetry of regions of the skull (e.g. upper and lower jaw – specific targets for therapy) using colormaps of regional asymmetry (in terms of left-v/s-right side surface distance) and visualized the same as vector glyphs. The direction of these vectors is synonymous with anticipated regional forces required in order to achieve left-right symmetry, which in-turn may have value in surgical planning for orthodontic implantation. In sum, we demonstrate a workflow for computer-aided cephalometry to assess the symmetry of the skull, which shows promise for personalized orthodontic anchor design.

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