Relevant bibliographies by topics / Phone recognition

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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 'Phone recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Phone recognition"

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Lin, Jhe-Syuan, and Wen-Shing Sun. "A Hidden Fingerprint Device on an Opaque Display Panel." Applied Sciences 10, no. 6 (March 23, 2020): 2188. http://dx.doi.org/10.3390/app10062188.

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In recent years, fingerprint recognition has become more and more widely used in mobile phones. A fingerprint recognition device hidden under an opaque display panel designed based on a waveguide and frustrated total internal reflection (FTIR) is proposed and demonstrated herein. In order to meet the demand for a high screen ratio for mobile phone displays, we use a symmetrical zoom-in and zoom-out coupler design. With this comprehensive coupler and waveguide design, not only can fingerprint recognition be achieved using an opaque display panel, but it also meets the appearance requirements for a mobile phone with a high screen ratio.
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Zeng, Hong, Yidan Hu, Jin Fan, Haiyang Hu, Zhigang Gao, and Qiming Fang. "Arm Motion Recognition and Exercise Coaching System for Remote Interaction." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9849720.

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Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first designimproved k-meansalgorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.
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Yang, Gang, and Jia Ni Luo. "A Real-Time Face Recognition System for Android Smart Phone." Advanced Materials Research 756-759 (September 2013): 4006–10. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4006.

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With the widely application of face recognition and the rapid development of Android OS, technique of face detection and recognition based on Android platform becomes increasingly attractive. This paper presents a real-time face recognition system on Android platform. The system realizes face detection by applying AdaBoost algorithm and face recognition by utilizing Eigenfaces. This paper also came up with some methods to speed up the face detection and recognition process and improve the correct rate of face recognition. Experimental results show that this system is able to realize real-time face detection and recognition on Android smart phones. In addition, all the work is completed on the smart phone without using any other terminals or tools.
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Yousef, Rana Mohammad, Omar Adwan, and Murad Abu-Leil. "An Enhanced Mobile Phone Dialler Application for Blind and Visually Impaired People." International Journal of Engineering & Technology 2, no. 4 (November 14, 2013): 270. http://dx.doi.org/10.14419/ijet.v2i4.1101.

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This paper presents the development of a new mobile phone dialler application which is designed to help blind and visually impaired people make phone calls. The new mobile phone dialler application is developed as a windows phone application to facilitate entering information to touch screen mobile phones by blind people. This application is advantageous through its innovative concept, its simplicity and its availability at an affordable cost. Feedback from users showed that this new application is easy to use and solves many problems of voice recognition applications such as inaccuracy, slowness and interpretation of unusual voices. In addition, this application has increased the users ability to dial phone numbers more independently and less stressfully.
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WANG, KONGQIAO, YANMING ZOU, and HAO WANG. "1D BAR CODE READING ON CAMERA PHONES." International Journal of Image and Graphics 07, no. 03 (July 2007): 529–50. http://dx.doi.org/10.1142/s0219467807002805.

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The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.
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Hải Dương, Nguyễn, and Nguyễn Hồng Quang. "Vietnamese speech recognition on mobile phone." Journal of Science, Educational Science 60, no. 7A (2015): 180–88. http://dx.doi.org/10.18173/2354-1075.2015-0065.

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Balaraman, Mridul, Sorin Dusan, and James L. Flanagan. "Supplementary features for improving phone recognition." Journal of the Acoustical Society of America 116, no. 4 (October 2004): 2479. http://dx.doi.org/10.1121/1.4784901.

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Kwapisz, Jennifer R., Gary M. Weiss, and Samuel A. Moore. "Activity recognition using cell phone accelerometers." ACM SIGKDD Explorations Newsletter 12, no. 2 (March 31, 2011): 74–82. http://dx.doi.org/10.1145/1964897.1964918.

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van Alphen, Paul. "Phone recognition in continuous speech (Dutch)." Journal of the Acoustical Society of America 87, S1 (May 1990): S107. http://dx.doi.org/10.1121/1.2027812.

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Xing, Jian, Miao Yu, Shupeng Wang, Yaru Zhang, and Yu Ding. "Automated Fraudulent Phone Call Recognition through Deep Learning." Wireless Communications and Mobile Computing 2020 (August 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8853468.

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Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.
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Dissertations / Theses on the topic "Phone recognition"

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Olausson, Erik. "Face Recognition for Mobile Phone Applications." Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11850.

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Att applicera ansiktsigenkänning direkt på en mobiltelefon är en utmanande uppgift, inte minst med tanke på den begränsade minnes- och processorkapaciteten samt den stora variationen med avseende på ansiktsuttryck, hållning och ljusförhållande i inmatade bilder.

Det är fortfarande långt kvar till ett färdigutvecklat, robust och helautomatiskt ansiktsigenkänningssystem för den här miljön. Men resultaten i det här arbetet visar att genom att plocka ut feature-värden från lokala regioner samt applicera en välgjord warpstrategi för att minska problemen med variationer i position och rotation av huvudet, är det möjligt att uppnå rimliga och användbara igenkänningsnivåer. Speciellt för ett halvautomatiskt system där användaren har sista ordet om vem personen på bilden faktiskt är.

Med ett galleri bestående av 85 personer och endast en referensbild per person nådde systemet en igenkänningsgrad på 60% på en svårklassificerad serie testbilder. Totalt 73% av gångerna var den rätta individen inom de fyra främsta gissningarna.

Att lägga till extra referensbilder till galleriet höjer igenkänningsgraden rejält, till nästan 75% för helt korrekta gissningar och till 83,5% för topp fyra. Detta visar att en strategi där inmatade bilder läggs till som referensbilder i galleriet efterhand som de identifieras skulle löna sig ordentligt och göra systemet bättre efter hand likt en inlärningsprocess.

Detta exjobb belönades med pris för "Bästa industrirelevanta bidrag" vid Svenska sällskapet för automatiserad bildanalys årliga konferens i Lund, 13-14 mars 2008.


Applying face recognition directly on a mobile phone is a challenging proposal due to the unrestrained nature of input images and limitations in memory and processor capabilities.

A robust, fully automatic recognition system for this environment is still a far way off. However, results show that using local feature extraction and a warping scheme to reduce pose variation problems, it is possible to capitalize on high error tolerance and reach reasonable recognition rates, especially for a semi-automatic classification system where the user has the final say.

With a gallery of 85 individuals and only one gallery image per individual available the system is able to recognize close to 60 % of the faces in a very challenging test set, while the correct individual is in the top four guesses 73% of the time.

Adding extra reference images boosts performance to nearly 75% correct recognition and 83.5% in the top four guesses. This suggests a strategy where extra reference images are added one by one after correct classification, mimicking an online learning strategy.

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Qin, Yinghao. "The Smart Phone as a Mouse." The University of Waikato, 2006. http://hdl.handle.net/10289/2289.

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With the development of hardware, mobile phone has become a feature-rich handheld device. Built-in camera and Bluetooth technology are supported in most current mobile phones. A real-time image processing experiment was conducted with a SonyEricsson P910i smartphone and a desktop computer. This thesis describes the design and implementation of a system which uses a mobile phone as a PC mouse. The movement of the mobile phone can be detected by analyzing the images captured by the onboard camera and the mouse cursor in the PC can be controlled by the movement of the phone.
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Ghosh, Anubhab. "Normalizing Flow based Hidden Markov Models for Phone Recognition." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286594.

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The task of Phone recognition is a fundamental task in Speech recognition and often serves a critical role in bench-marking purposes. Researchers have used a variety of models used in the past to address this task, using both generative and discriminative learning approaches. Among them, generative approaches such as the use of Gaussian mixture model-based hidden Markov models are always favored because of their mathematical tractability. However, the use of generative models such as hidden Markov models and its hybrid varieties is no longer in fashion owing to a large inclination to discriminative learning approaches, which have been found to perform better. The only downside is that these approaches do not always ensure mathematical tractability or convergence guarantees as opposed to their generative counterparts. So, the research problem was to investigate whether there could be a process of augmenting the modeling capability of generative Models using a kind of neural network based architectures that could simultaneously prove mathematically tractable and expressive. Normalizing flows are a class of generative models that have been garnered a lot of attention recently in the field of density estimation and offer a method for exact likelihood computation and inference. In this project, a few varieties of Normalizing flow-based hidden Markov models were used for the task of Phone recognition on the TIMIT dataset. It was been found that these models and their mixture model varieties outperformed classical generative model varieties like Gaussian mixture models. A decision fusion approach using classical Gaussian and Normalizing flow-based mixtures showed competitive results compared to discriminative learning approaches. Further analysis based on classes of speech phones was carried out to compare the generative models used. Additionally, a study of the robustness of these algorithms to noisy speech conditions was also carried out.
Uppgiften för fonemigenkänning är en grundläggande uppgift i taligenkänning och tjänar ofta en kritisk roll i benchmarkingändamål. Forskare har använt en mängd olika modeller som använts tidigare för att hantera denna uppgift genom att använda både generativa och diskriminerande inlärningssätt. Bland dem är generativa tillvägagångssätt som användning av Gaussian-blandnings modellbaserade dolda Markov-modeller alltid föredragna på grund av deras matematiska spårbarhet. Men användningen av generativa modeller som dolda Markov-modeller och dess hybridvarianter är inte längre på mode på grund av en stor lutning till diskriminerande inlärningsmetoder, som har visat sig fungera bättre. Den enda nackdelen är att dessa tillvägagångssätt inte alltid säkerställer matematisk spårbarhet eller konvergensgarantier i motsats till deras generativa motsvarigheter. Således var forskningsproblemet att undersöka om det kan finnas en process för att förstärka modelleringsförmågan hos generativa modeller med hjälp av ett slags neurala nätverksbaserade arkitekturer som samtidigt kunde visa sig matematiskt spårbart och uttrycksfullt. Normaliseringsflöden är en klass generativa modeller som nyligen har fått mycket uppmärksamhet inom området för densitetsberäkning och erbjuder en metod för exakt sannolikhetsberäkning och slutsats. I detta projekt användes några få varianter av Normaliserande flödesbaserade dolda Markov-modeller för uppgiften att fonemigenkänna i TIMIT-datasatsen. Det visade sig att dessa modeller och deras blandningsmodellvarianter överträffade klassiska generativa modellvarianter som Gaussiska blandningsmodeller. Ett beslutssmältningsstrategi med klassiska Gaussiska och Normaliserande flödesbaserade blandningar visade konkurrenskraftiga resultat jämfört med diskriminerande inlärningsmetoder. Ytterligare analys baserat på klasser av talsignaler utfördes för att jämföra de generativa modellerna som användes. Dessutom genomfördes en studie av robustheten hos dessa algoritmer till bullriga talförhållanden.
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Stearns, Cameron P. cstearns. "A SYSTEM FOR CELL PHONE ANTI-THEFT THROUGH GAIT RECOGNITION." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1216.

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Studies show that smartphone thefts are a significant problem in the United States. [30] With many upcoming proposals to decrease the theft-rate of such devices, investigating new techniques for preventing smartphone theft is an important area of research. The prevalence of new biometric identification techniques for smartphones has led some researchers to propose biometric anti-theft measures for such devices, similar to the current fingerprint authentication system for iOS. Gait identification, a relatively recent field of study, seems to be a good fit for anti-theft because of the non-intrusive nature of passive pattern recognition in walking. In this paper, we reproduce and extend a modern gait recognition technique proposed in Cell Phone-Based Biometrics by testing the technique outside of the laboratory on real users under everyday conditions. We propose how this technique can be applied to create an anti-theft system, and we discuss future developments that will be necessary before such research is ready to be implemented in a release-quality product. Because previous studies have also centered around the ability to differentiate between individual users from a group, we will examine the accuracy of identifying whether or not a specific user is currently using a system. The system proposed in this paper shows results as high as 91% for cross-fold accuracy for some users; however, the predictive accuracy for a single day’s results ranged from 0.8% accuracy to 92.9% accuracy, showing an unreliability that makes such a system unlikely to be useful under the pressure of real-world conditions.
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Chou, Christine S. (Christine Susan). "Language identification through parallel phone recognition dc by Christine S. Chou." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/34056.

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Mohapatra, Prateeti. "Deriving Novel Posterior Feature Spaces For Conditional Random Field - Based Phone Recognition." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236784133.

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Mohammed, Abdulmalik. "Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html.

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In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.
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Zhang, Zelun. "User mobility detection using foot force sensors and mobile phone GPS." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9116.

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A user (or human) mobility context is defined as a type of user context that describes a type of whole body posture (e.g., standing versus sitting) and/or a type of travel or transportation mode (e.g., walking, cycling, travel by bus, etc). Such a context can be derived from low-level sensor data and spatial contexts, including location coordinates, 3D-orientation, direction (with respect to magnetic north), velocity and acceleration. Different value-added services can be adapted to users’ mobility contexts such as assessing how eco-friendly our travel is, and adapting travel information services such as maps to different transportation modes. Current sensor-based methods for user mobility detection have several key limitations: narrow range of recognition, coarse user mobility recognition capability, and low recognition accuracy. In this thesis, a new Foot-Force and GPS (FF+GPS) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with mobile phone GPS. The novelty of this approach is that it provides a more comprehensive recognition capability in terms of reliably recognising various fine-grained human postures and transportation modes. In addition, by comparing the new FF+GPS method with both an accelerometer (ACC) method (62% accuracy) and an ACC+GPS based method (70% accuracy) as baseline methods, it obtains a higher accuracy (90%) with less computational complexity, when tested on a dataset obtained from ten individuals. In addition, the new FF+GPS method has been further extended and evaluated. More specifically, the trade-off between the computation and resources needed to support lower versus higher number of features and sensors has been investigated. The improved FF+GPS method reduced the number of classification features from 31 to 12, reduced the number of FF sensors from 8 to 4, and reduced the use of GPS in mobility activity recognition.
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Wong, Kim-Yung Eddie. "Automatic spoken language identification utilizing acoustic and phonetic speech information." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/37259/1/Kim-Yung_Wong_Thesis.pdf.

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Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.
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Martin, Terrence Lance. "Towards improved speech recognition for resource poor languages." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/35771/1/Terrence_Martin_Thesis.pdf.

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In recent times, the improved levels of accuracy obtained by Automatic Speech Recognition (ASR) technology has made it viable for use in a number of commercial products. Unfortunately, these types of applications are limited to only a few of the world’s languages, primarily because ASR development is reliant on the availability of large amounts of language specific resources. This motivates the need for techniques which reduce this language-specific, resource dependency. Ideally, these approaches should generalise across languages, thereby providing scope for rapid creation of ASR capabilities for resource poor languages. Cross Lingual ASR emerges as a means for addressing this need. Underpinning this approach is the observation that sound production is largely influenced by the physiological construction of the vocal tract, and accordingly, is human, and not language specific. As a result, a common inventory of sounds exists across languages; a property which is exploitable, as sounds from a resource poor, target language can be recognised using models trained on resource rich, source languages. One of the initial impediments to the commercial uptake of ASR technology was its fragility in more challenging environments, such as conversational telephone speech. Subsequent improvements in these environments has gained consumer confidence. Pragmatically, if cross lingual techniques are to considered a viable alternative when resources are limited, they need to perform under the same types of conditions. Accordingly, this thesis evaluates cross lingual techniques using two speech environments; clean read speech and conversational telephone speech. Languages used in evaluations are German, Mandarin, Japanese and Spanish. Results highlight that previously proposed approaches provide respectable results for simpler environments such as read speech, but degrade significantly when in the more taxing conversational environment. Two separate approaches for addressing this degradation are proposed. The first is based on deriving better target language lexical representation, in terms of the source language model set. The second, and ultimately more successful approach, focuses on improving the classification accuracy of context-dependent (CD) models, by catering for the adverse influence of languages specific phonotactic properties. Whilst the primary research goal in this thesis is directed towards improving cross lingual techniques, the catalyst for investigating its use was based on expressed interest from several organisations for an Indonesian ASR capability. In Indonesia alone, there are over 200 million speakers of some Malay variant, provides further impetus and commercial justification for speech related research on this language. Unfortunately, at the beginning of the candidature, limited research had been conducted on the Indonesian language in the field of speech science, and virtually no resources existed. This thesis details the investigative and development work dedicated towards obtaining an ASR system with a 10000 word recognition vocabulary for the Indonesian language.
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Books on the topic "Phone recognition"

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Manjunath, K. E. Multilingual Phone Recognition in Indian Languages. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-80741-2.

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Manjunath, K. E. Multilingual Phone Recognition in Indian Languages. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-80741-2.

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E, Manjunath K. Multilingual Phone Recognition in Indian Languages. Springer International Publishing AG, 2021.

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Aboumerhi, Hassan, and Tariq M. Malik. Interscalene Catheters: Complications and Management. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190271787.003.0044.

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About 4.5 million people visit physicians for shoulder pain every year. Most shoulder surgeries are performed in an ambulatory setting and pain control can be problematic during the recovery period. Continuous interscalene block, which is quite effective for postprocedural pain relief, is not risk free. Some postprocedure concerns can be resolved easily over the phone, but others require additional examination, imaging, or even surgical intervention. Effective and safe management of a brachial plexus catheter requires a complete perioperative plan, open communication with the patient and family, and recognition and early treatment of complications. Also needed is a good working relationship between nurses, anesthesia care givers, and orthopedic surgeons.
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Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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Nass, Clifford. Voice Activated: Psychology and Design of Voice Interfaces for the Web, Phones, and Wireless. University of Chicago Press, 2001.

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Nass, Clifford. Voice Activated: Psychology and Design of Voice Interfaces for the Web, Phones, and Wireless. University of Chicago Press, 2001.

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Morgan Wortham, Simon. Impossible Divisions: Fanon, Hegel and Psychoanalysis. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474429603.003.0002.

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This chapter concentrates on Fanon’s Black Skin, White Masks, where the Hegelian theme of mutual recognition as the origin of man’s self-consciousness and potential freedom is tested against the complex circumstances of colonialism. Fanon’s idea that the ‘Negro slave’ is recognized by the ‘White Master’ in a situation that is ‘without conflict’ suggests a possibly double, or self-resistant, meaning: the colonial situation after slavery ushers in something like a phony war; but also colonialism’s historical interpretation is not exhausted by the Hegelian master-slave logic. Through this double possibility of the colonial, one wonders whether after Hegel it is historical interpretation or the historical process itself that has gone awry. Such dynamic tensions suggest an impossibly divided dialectics at work throughout Fanon’s corpus. The section of Fanon’s ‘The Negro and Recognition’ devoted to a critique of Adler points to an earlier footnote in Black Skin, White Masks which offers a lengthy engagement with Lacan, allowing us to reread the politics of racial difference into the scene of the Lacanian mirror-stage. Here, the resistant ‘other’ of psychoanalysis unlocks the possibility of another ‘politics’ capable of addressing, by better recognising, some of its most significant impasses.
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Book chapters on the topic "Phone recognition"

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Xie, Chunyu, Shangzhen Luan, Hainan Wang, and Baochang Zhang. "Gesture Recognition Benchmark Based on Mobile Phone." In Biometric Recognition, 432–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_48.

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Rao, K. Sreenivasa, and Manjunath K.E. "Articulatory Features for Phone Recognition." In SpringerBriefs in Electrical and Computer Engineering, 17–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49220-9_3.

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Lévy, Christophe, Georges Linarès, Pascal Nocera, and Jean-François Bonastre. "Embedded Mobile Phone Digit-Recognition." In Advances for In-Vehicle and Mobile Systems, 71–84. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-45976-9_7.

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Ljolje, Andrej. "Phone Recognition Using High Order Phonotactic Constraints." In Speech Recognition and Understanding, 205–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76626-8_23.

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Rao, K. Sreenivasa, and Manjunath K.E. "Excitation Source Features for Phone Recognition." In SpringerBriefs in Electrical and Computer Engineering, 47–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49220-9_4.

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Manjunath, K. E. "Articulatory Features for Multilingual Phone Recognition." In SpringerBriefs in Speech Technology, 57–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80741-2_5.

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Galiano, Isabel, Francisco Casacuberta, and Emilio Sanchis. "Modelling Phone-Context in Spanish by Using SCMGGI Models." In Speech Recognition and Coding, 268–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57745-1_38.

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Zhang, Yongliang, Bing Zhou, Hongtao Wu, and Conglin Wen. "2D Fake Fingerprint Detection Based on Improved CNN and Local Descriptors for Smart Phone." In Biometric Recognition, 655–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_72.

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Hisham, P. M., D. Pravena, Y. Pardhu, V. Gokul, B. Abhitej, and D. Govind. "Improved Phone Recognition Using Excitation Source Features." In Advances in Intelligent Systems and Computing, 147–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23036-8_13.

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Feng, Yunfei, Carl K. Chang, and Hanshu Chang. "An ADL Recognition System on Smart Phone." In Inclusive Smart Cities and Digital Health, 148–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39601-9_13.

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Conference papers on the topic "Phone recognition"

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Dey, Abhishek, Wendy Lalhminghlui, Priyankoo Sarmah, K. Samudravijaya, S. R. Mahadeva Prasarma, Rohit Sinha, and S. R. Nirrnala. "Mizo Phone Recognition System." In 2017 14th IEEE India Council International Conference (INDICON). IEEE, 2017. http://dx.doi.org/10.1109/indicon.2017.8487726.

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Gauvain, Jean-Luc, Abdel Messaoudi, and Holger Schwenk. "Language recognition using phone latices." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-28.

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Hnatiuc, Mihaela, Mirel Paun, and Joseph Dussart. "Path Recognition using Mobile Phone." In 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD). IEEE, 2019. http://dx.doi.org/10.1109/sped.2019.8906550.

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Walker, B. D., B. C. Lackey, J. S. Muller, and P. J. Schone. "Language-reconfigurable universal phone recognition." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-87.

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Gauvain, Jean-Luc, and Lori F. Lamel. "Speaker-independent phone recognition using BREF." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1992. http://dx.doi.org/10.3115/1075527.1075608.

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Bogomolov, Andrey, Bruno Lepri, and Fabio Pianesi. "Happiness Recognition from Mobile Phone Data." In 2013 International Conference on Social Computing (SocialCom). IEEE, 2013. http://dx.doi.org/10.1109/socialcom.2013.118.

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Chen, Hongkai, Sazia Mahfuz, and Farhana Zulkernine. "Smart Phone Based Human Activity Recognition." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983009.

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Lamel, L. F., and J. L. Gauvain. "Cross-lingual experiments with phone recognition." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319353.

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Mohamed, Abdel-rahman, and Geoffrey Hinton. "Phone recognition using Restricted Boltzmann Machines." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495651.

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Li, Xinjian, Juncheng Li, Florian Metze, and Alan W. Black. "Hierarchical Phone Recognition with Compositional Phonetics." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1803.

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Reports on the topic "Phone recognition"

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Digalakis, V., M. Ostendorf, and J. R. Rohlicek. Fast Search Algorithms for Connected Phone Recognition Using the Stochastic Segment Model. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada459580.

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Bilyk, Zhanna I., Yevhenii B. Shapovalov, Viktor B. Shapovalov, Anna P. Megalinska, Fabian Andruszkiewicz, and Agnieszka Dołhańczuk-Śródka. Assessment of mobile phone applications feasibility on plant recognition: comparison with Google Lens AR-app. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4403.

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The paper is devoted to systemizing all mobile applications used during the STEM-classes and can be used to identify plants. There are 10 mobile applications that are plant identifiers worldwide. These applications can be divided into three groups, such as plant identifiers that can analyze photos, plant classification provides the possibility to identify plants manually, plants-care apps that remind water of the plant, or change the soil. In this work, mobile apps such as Flora Incognita, PlantNet, PlantSnap, PictureThis, LeafSnap, Seek, PlantNet were analyzed for usability parameters and accuracy of identification. To provide usability analysis, a survey of experts of digital education on installation simplicity, level of friendliness of the interface, and correctness of picture processing. It is proved that Flora Incognita and PlantNet are the most usable and the most informative interface from plant identification apps. However, they were characterized by significantly lower accuracy compared to Google Lens results. Further comparison of the usability of applications that have been tested in the article with Google Lens, proves that Google Lens characterize by better usability and therefore, Google Lens is the most recommended app to use to provide plant identification during biology classes.
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