Search results for: foot recognition

Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Sameh Abaza, Mohamed Ibrahim, Tarek Mahmoud

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Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary.

Keywords: HMM, K-Means, Sobel, accuracy, face recognition

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Nathalia Andrea Calderon Lesmes, Eduardo Barragan Parada, Diego Fernando Villegas Bermudez

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Spasticity in the plantiflexor muscles as a product of stroke (CVA-Cerebrovascular accident) restricts the mobility and independence of the affected people. Commonly, physiotherapists are in charge of manually performing the rehabilitation therapy known as Sustained Mechanical Stretching, rotating the affected foot of the patient in the sagittal plane. However, this causes a physical wear on the professional because it is a fatiguing movement. In this article, a mechanical device is developed to implement this rehabilitation therapy more efficiently. The device consists of a worm-crown mechanism that is driven by a crank to gradually rotate a platform in the sagittal plane of the affected foot, in order to achieve dorsiflexion. The device has a range of sagittal rotation up to 150° and has velcro located on the footplate that secures the foot. The design of this device was modeled by using CAD software and was checked structurally with a general purpose finite element software to be sure that the device is safe for human use. As a measurement system, a goniometer is used in the lateral part of the device and load cells are used to measure the force in order to determine the opposing torque exerted by the muscle. Load cells sensitivity is 1.8 ± 0.002 and has a repeatability of 0.03. Validation of the effectiveness of the device is measured by reducing the opposition torque and increasing mobility for a given patient. In this way, with a more efficient therapy, an improvement in the recovery of the patient's mobility and therefore in their quality of life can be achieved.

Keywords: biomechanics, mechanical device, plantiflexor muscles, rehabilitation, spastic hemiplegia, sustained mechanical stretching

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Authors: Brock Liden, Eric Janowitz

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Introduction: Diabetic peripheral neuropathy (DPN) affects 70% of individuals with diabetes1. DPN causes a loss of protective sensation, which can lead to tissue damage and diabetic foot ulcer (DFU) formation2. These ulcers can result in infections and lower-extremity amputations of toes, the entire foot, and the lower leg. Even after a DFU is healed, recurrence is common, with 49% of DFU patients developing another ulcer within a year and 68% within 5 years3. This case series examines the use of sensory insoles and newly available plantar data (pressure, temperature, step count, adherence) and remote patient monitoring in patients at risk of DFU. Methods: Participants were provided with custom-made sensory insoles to monitor plantar pressure, temperature, step count, and daily use and were provided with real-time cues for pressure offloading as they went about their daily activities. The sensory insoles were used to track subject compliance, ulceration, and response to feedback from real-time alerts. Patients were remotely monitored by a qualified healthcare professional and were contacted when areas of concern were seen and provided coaching on reducing risk factors and overall support to improve foot health. Results: Of the 40 participants provided with the sensory insole system, 4 presented with a DFU. Based on flags generated from the available plantar data, patients were contacted by the remote monitor to address potential concerns. A standard clinical escalation protocol detailed when and how concerns should be escalated to the provider by the remote monitor. Upon escalation to the provider, patients were brought into the clinic as needed, allowing for any issues to be addressed before more serious complications might arise. Conclusion: This case series explores the use of innovative sensory technology to collect plantar data (pressure, temperature, step count, and adherence) for DFU detection and early intervention. The results from this case series suggest the importance of sensory technology and remote patient monitoring in providing proactive, preventative care for patients at risk of DFU. This robust plantar data, with the addition of remote patient monitoring, allow for patients to be seen in the clinic when concerns arise, giving providers the opportunity to intervene early and prevent more serious complications, such as wounds, from occurring.

Keywords: diabetic foot ulcer, DFU prevention, digital therapeutics, remote patient monitoring

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Authors: Maria Pavlova

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In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

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Authors: Shih-Yu Lo

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Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

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Authors: Mina Youssef Makram Ibrahim

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Engineering identity contributes to the professional and academic sustainability of female engineers. Recognizability is an important factor that shapes an engineer's identity. People who are deprived of real recognition often fail to create a positive identity. This study draws on Hornet’s recognition theory to identify factors that influence female civil engineers' sense of recognition. Over the past decade, a survey was created and distributed to 330 graduate students in the Department of Civil, Civil and Environmental Engineering at Iowa State University. Survey items include demographics, perceptions of a civil engineer's identity, and factors that influence recognition of a civil engineer's identity, such as B. Opinions about society and family. Descriptive analysis of survey responses revealed that perceptions of civil engineering varied significantly. The definitions of civil engineering provided by participants included the terms structure, design and infrastructure. Almost half of the participants said the main reason for studying Civil Engineering was their interest in the subject, and the majority said they were proud to be a civil engineer. Many study participants reported that their parents viewed them as civil engineers. Institutional and operational treatment was also found to have a significant impact on the recognition of women civil engineers. Almost half of the participants reported feeling isolated or ignored at work because of their gender. This research highlights the importance of recognition in developing the identity of women engineers.

Keywords: civil service, hiring, merit, policing civil engineering, construction, surveying, mapping, pile civil service, Kazakhstan, modernization, a national model of civil service, civil service reforms, bureaucracy civil engineering, gender, identity, recognition

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Authors: Amin Derakhshan, Mohammad Mikaili, Mohammad Ali Khalilzadeh, Amin Mohammadian

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This paper proposes a stress recognition system from multi-modal bio-potential signals. For stress recognition, Support Vector Machines (SVM) and LDA are applied to design the stress classifiers and its characteristics are investigated. Using gathered data under psychological polygraph experiments, the classifiers are trained and tested. The pattern recognition method classifies stressful from non-stressful subjects based on labels which come from polygraph data. The successful classification rate is 96% for 12 subjects. It means that facial thermal imaging due to its non-contact advantage could be a remarkable alternative for psycho-physiological methods.

Keywords: stress, thermal imaging, face, SVM, polygraph

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

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Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Taekyong Lee, Joon-Young Kim, Jae-Min Cha

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Piping and instrumentation diagram (P&ID) is an essential design drawing describing the interconnection of process equipment and the instrumentation installed to control the process. P&IDs are modified and managed throughout a whole life cycle of a process plant. For the ease of data transfer, P&IDs are generally handed over from a design company to an engineering company as portable document format (PDF) which is hard to be modified. Therefore, engineering companies have to deploy a great deal of time and human resources only for manually converting P&ID images into a computer aided design (CAD) file format. To reduce the inefficiency of the P&ID conversion, various symbols and texts in P&ID images should be automatically recognized. However, recognizing information in P&ID images is not an easy task. A P&ID image usually contains hundreds of symbol and text objects. Most objects are pretty small compared to the size of a whole image and are densely packed together. Traditional recognition methods based on geometrical features are not capable enough to recognize every elements of a P&ID image. To overcome these difficulties, state-of-the-art deep learning models, RetinaNet and connectionist text proposal network (CTPN) were used to build a system for recognizing symbols and texts in a P&ID image. Using the RetinaNet and the CTPN model carefully modified and tuned for P&ID image dataset, the developed system recognizes texts, equipment symbols, piping symbols and instrumentation symbols from an input P&ID image and save the recognition results as the pre-defined extensible markup language format. In the test using a commercial P&ID image, the P&ID information recognition system correctly recognized 97% of the symbols and 81.4% of the texts.

Keywords: object recognition system, P&ID, symbol recognition, text recognition

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Authors: Hirokatsu Kawashima

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One important element of teaching/learning listening is intensive listening such as listening for precise sounds, words, grammatical, and semantic units. Several classroom-based investigations have been conducted to explore the usefulness of auditory recognition of phonological, grammatical and semantic errors in such a context. The current study reports the results of one such investigation, which targeted auditory recognition of phonological, grammatical, and semantic errors at the sentence level. 56 Japanese EFL learners participated in this investigation, in which their recognition performance of phonological, grammatical and semantic errors was measured on a 9-point scale by learners’ self-evaluation from the perspective of 1) two types of similar English sound (vowel and consonant minimal pair words), 2) two types of sentence word order (verb phrase-based and noun phrase-based word orders), and 3) two types of semantic consistency (verb-purpose and verb-place agreements), respectively, and their general listening proficiency was examined using standardized tests. A number of findings have been made about the interactive relationships between the three types of auditory error recognition and general listening proficiency. Analyses based on the OPLS (Orthogonal Projections to Latent Structure) regression model have disclosed, for example, that the three types of auditory error recognition are linked in a non-linear way: the highest explanatory power for general listening proficiency may be attained when quadratic interactions between auditory recognition of errors related to vowel minimal pair words and that of errors related to noun phrase-based word order are embraced (R2=.33, p=.01).

Keywords: auditory error recognition, intensive listening, interaction, investigation

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Tala Toonsi, Marah Alagha, Lina Alnowaiser, Hala Rajab

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This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users.

Keywords: photomath, image recognition, app, OCR, artificial intelligence, mathematical equations.

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Authors: M. Abdullah, Al-Wadud

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Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Chung Yan Natalia Yeung, Man Kit Indy Ho, Kin Yu Stan Chan, Ho Pui Kipper Lam, Man Wah Genie Tong, Tze Chung Jim Luk

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Excessive foot pronation is a well-known risk factor of knee and foot injuries such as patellofemoral pain, patellar and Achilles tendinopathy, and plantar fasciitis. Low-Dye taping (LDT) application is not uncommon for basketball players to control excessive foot pronation for pain control and injury prevention. The primary potential benefits of using LDT include providing additional supports to medial longitudinal arch and restricting the excessive midfoot and subtalar motion in weight-bearing activities such as running and landing. Meanwhile, restrictions provided by the rigid tape may also potentially limit functional joint movements and sports performance. Coaches and athletes need to weigh the potential benefits and harmful effects before making a decision if applying LDT technique is worthwhile or not. However, the influence of using LDT on basketball-related performance such as explosive and reactive strength is not well understood. Therefore, the purpose of this study was to investigate the change of drop jump (DJ) and countermovement jump (CMJ) performance before and after LDT application for collegiate male basketball players. In this within-subject crossover study, 12 healthy male basketball players (age: 21.7 ± 2.5 years) with at least 3-year regular basketball training experience were recruited. Navicular drop (ND) test was adopted as the screening and only those with excessive pronation (ND ≥ 10mm) were included. Participants with recent lower limb injury history were excluded. Recruited subjects were required to perform both ND, DJ (on a platform of 40cm height) and CMJ (without arms swing) tests in series during taped and non-taped conditions in the counterbalanced order. Reactive strength index (RSI) was calculated by using the flight time divided by the ground contact time measured. For DJ and CMJ tests, the best of three trials was used for analysis. The difference between taped and non-taped conditions for each test was further calculated through standardized effect ± 90% confidence intervals (CI) with clinical magnitude-based inference (MBI). Paired samples T-test showed significant decrease in ND (-4.68 ± 1.44mm; 95% CI: -3.77, -5.60; p < 0.05) while MBI demonstrated most likely beneficial and large effect (standardize effect: -1.59 ± 0.27) in LDT condition. For DJ test, significant increase in both flight time (25.25 ± 29.96ms; 95% CI: 6.22, 44.28; p < 0.05) and RSI (0.22 ± 0.22; 95% CI: 0.08, 0.36; p < 0.05) were observed. In taped condition, MBI showed very likely beneficial and moderate effect (standardized effect: 0.77 ± 0.49) in flight time, possibly beneficial and small effect (standardized effect: -0.26 ± 0.29) in ground contact time and very likely beneficial and moderate effect (standardized effect: 0.77 ± 0.42) in RSI. No significant difference in CMJ was observed (95% CI: -2.73, 2.08; p > 0.05). For basketball players with pes planus, applying LDT could substantially support the foot by elevating the navicular height and potentially provide acute beneficial effects in reactive strength performance. Meanwhile, no significant harmful effect on CMJ was observed. Basketball players may consider applying LDT before the game or training to enhance the reactive strength performance. However since the observed effects in this study could not generalize to other players without excessive foot pronation, further studies on players with normal foot arch or navicular height are recommended.

Keywords: flight time, pes planus, pronated foot, reactive strength index

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: S. B. Rathna Kumar, Pranjali A Ujwane, Panchanan Mohanty

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The present study aimed to develop a battery for assessing speech recognition performance by adults in Marathi. A total of four word lists were developed by considering word frequency, word familiarity, words in common use, and phonemic balance. Each word list consists of 25 words (15 monosyllabic words in CVC structure and 10 monosyllabic words in CVCV structure). Equivalence analysis and performance-intensity function testing was carried using the four word lists on a total of 150 native speakers of Marathi belonging to different regions of Maharashtra (Vidarbha, Marathwada, Khandesh and Northern Maharashtra, Pune, and Konkan). The subjects were further equally divided into five groups based on above mentioned regions. It was found that there was no significant difference (p > 0.05) in the speech recognition performance between groups for each word list and between word lists for each group. Hence, the four word lists developed were equally difficult for all the groups and can be used interchangeably. The performance-intensity (PI) function curve showed semi-linear function, and the groups’ mean slope of the linear portions of the curve indicated an average linear slope of 4.64%, 4.73%, 4.68%, and 4.85% increase in word recognition score per dB for list 1, list 2, list 3 and list 4 respectively. Although, there is no data available on speech recognition tests for adults in Marathi, most of the findings of the study are in line with the findings of research reports on other languages. The four word lists, thus developed, were found to have sufficient reliability and validity in assessing speech recognition performance by adults in Marathi.

Keywords: speech recognition performance, phonemic balance, equivalence analysis, performance-intensity function testing, reliability, validity

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Authors: Siti Rahayu, Khairulniza Mohd Puat, Kesavan R., Mohammad Harris A., Jalila, Kunalan G., Fazir Mohamad

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The greatest challenge has been in establishing and maintaining the dedicated nursing team. Continuity is served when nurses are assigned exclusively for managing wound, where they can continue to build expertise and skills. In addition, there is a growing incidence of chronic wounds and recognition of the complexity involved in caring for these patients. We would like to share 4 cases with different techniques of wound management. 1st case, 39 years old gentleman with underlying rheumatoid arthritis with chronic periprosthetic joint infection of right total knee replacement presented with persistent drainage over right knee. Patient was consulted for two stage revision total knee replacement. However, patient only agreed for debridement and retention of implant. After debridement, large medial and lateral wound was treated with Instillation Negative Pressure Wound Therapy Dressings. After several cycle, the wound size reduced, and conventional dressing was applied. 2nd case, 58 years old gentleman with underlying diabetes presented with right foot necrotizing fasciitis with gangrene of 5th toe. He underwent extensive debridement of foot with rays’ amputation of 5th toe. Post debridement patient was started on Instillation Negative Pressure Wound Therapy Dressings. After several cycle of VAC, the wound bed was prepared, and he underwent split skin graft over right foot. 3 rd case, 60 years old gentleman with underlying diabetes mellitus presented with right foot necrotizing soft tissue infection. He underwent rays’ amputation and extensive wound debridement. Upon stabilization of general condition, patient was discharge with regular wound dressing by same nurse and doctor during each visit to clinic follow up. After 6 months of follow up, the wound healed well. 4th case, 38-year-old gentleman had alleged motor vehicle accident and sustained closed fracture right tibial plateau. Open reduction and proximal tibial locking plate were done. At 2 weeks post-surgery, the patient presented with warm, erythematous leg and pus discharge from the surgical site. Empirical antibiotic was started, and wound debridement was done. Intraoperatively, 50cc pus was evacuated, unhealthy muscle and tissue debrided. No loosening of the implant. Patient underwent multiple wound debridement. At 2 weeks post debridement wound healed well, but the proximal aspect was unable to close immediately. This left the proximal part of the implant to be exposed. Patient was then put on VAC dressing for 3 weeks until healthy granulation tissue closes the implant. Meanwhile, antibiotic was change according to culture and sensitivity. At 6 weeks post the first debridement, the wound was completely close, and patient was discharge home well. At 3 months post operatively, patient wound and fracture healed uneventfully and able to ambulate independently. Complex wounds are too serious to be dealt with. Team managing complex wound need continuous support through the provision of educational tools to support their professional development, engagement with local and international expert, as well as highquality products that increase efficiencies in services

Keywords: VAC (Vacuum Assisted Closure), empirical- initial antibiotics, NPWT- negative pressure wound therapy, NF- necrotizing fasciitis, gangrene- blackish discoloration due to poor blood supply

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Florian Kleber, Martin Kampel

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The increasing demand of gallium, indium and rare-earth elements for the production of electronics, e.g. solid state-lighting, photovoltaics, integrated circuits, and liquid crystal displays, will exceed the world-wide supply according to current forecasts. Recycling systems to reclaim these materials are not yet in place, which challenges the sustainability of these technologies. This paper proposes a multispectral imaging system as a basis for a vision based recognition system for valuable components of electronics waste. Multispectral images intend to enhance the contrast of images of printed circuit boards (single components, as well as labels) for further analysis, such as optical character recognition and entire printed circuit board recognition. The results show that a higher contrast is achieved in the near infrared compared to ultraviolet and visible light.

Keywords: electronics waste, multispectral imaging, printed circuit boards, rare-earth elements

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Authors: G. Suja, J. Sreekumar, A. N. Jyothi, V. S. Santhosh Mithra

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Worldwide concerns regarding food safety, environmental degradation and threats to human health have generated interest in alternative systems like organic farming. Tropical tuber crops, cassava, sweet potato, yams, and aroids are food-cum-nutritional security-cum climate resilient crops. These form stable or subsidiary food for about 500 million global population. Cassava, yams (white yam, greater yam, and lesser yam) and edible aroids (elephant foot yam, taro, and tannia) are high energy tuberous vegetables with good taste and nutritive value. Seven on-station field experiments at ICAR-Central Tuber Crops Research Institute, Thiruvananthapuram, India and seventeen on-farm trials in three districts of Kerala, were conducted over a decade (2004-2015) to compare the varietal response, yield, quality and soil properties under organic vs conventional system in these crops and to develop a learning system based on the data generated. The industrial, as well as domestic varieties of cassava, the elite and local varieties of elephant foot yam and taro and the three species of Dioscorea (yams), were on a par under both systems. Organic management promoted yield by 8%, 20%, 9%, 11% and 7% over conventional practice in cassava, elephant foot yam, white yam, greater yam and lesser yam respectively. Elephant foot yam was the most responsive to organic management followed by yams and cassava. In taro, slight yield reduction (5%) was noticed under organic farming with almost similar tuber quality. The tuber quality was improved with higher dry matter, starch, crude protein, K, Ca and Mg contents. The anti-nutritional factors, oxalate content in elephant foot yam and cyanogenic glucoside content in cassava were lowered by 21 and 12.4% respectively. Organic plots had significantly higher water holding capacity, pH, available K, Fe, Mn and Cu, higher soil organic matter, available N, P, exchangeable Ca and Mg, dehydrogenase enzyme activity and microbial count. Organic farming scored significantly higher soil quality index (1.93) than conventional practice (1.46). The soil quality index was driven by water holding capacity, pH and available Zn followed by soil organic matter. Organic management enhanced net profit by 20-40% over chemical farming. A case in point is the cost-benefit analysis in elephant foot yam which indicated that the net profit was 28% higher and additional income of Rs. 47,716 ha-1 was obtained due to organic farming. Cost-effective technologies were field validated. The on-station technologies developed were validated and popularized through on-farm trials in 10 sites (5 ha) under National Horticulture Mission funded programme in elephant foot yam and seven sites in yams and taro. The technologies are included in the Package of Practices Recommendations for crops of Kerala Agricultural University. A learning system developed using artificial neural networks (ANN) predicted the performance of elephant foot yam organic system. Use of organically produced seed materials, seed treatment in cow-dung, neem cake, bio-inoculant slurry, farmyard manure incubated with bio-inoculants, green manuring, use of neem cake, bio-fertilizers and ash formed the strategies for organic production. Organic farming is an eco-friendly management strategy that enables 10-20% higher yield, quality tubers and maintenance of soil health in tuber crops.

Keywords: eco-agriculture, quality, root crops, healthy soil, yield

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Authors: Vaibhav V. Dixit, Shubham Ajay Karwa

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Diabetic foot ulcers speak to a critical medical problem. Right now, clinicians and medical caretakers primarily construct their injury evaluation in light of visual examination of wound size and mending status, while the patients themselves rarely have a chance to play a dynamic part. Henceforth, love quantitative and practical examination technique that empowers the patients and their parental figures to take a more dynamic part in every day wound care possibly can quicken wound recuperating, spare travel cost and diminish human services costs. Considering the commonness of cell phones with a high-determination computerized camera, evaluating wounds by breaking down pictures of ceaseless foot ulcers is an alluring choice. In this paper, we propose a novel injury picture examination framework actualized using feature extraction and color segmentation. Here we are using the Normalized minimum distance classifier for classifying the output.

Keywords: diabetic, Gabor wavelet, normalized minimum distance classifier, quantiable parameters

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Authors: C. G. Shashi Kumar, G. Arun Maiya, H. Manjunath Hande, K. V. Rajagopal

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Background: Diabetes mellitus (DM) is a metabolic disorder with involvement of neurovascular and muscular system. Studies have documented that the gait parameter is altered in type 2 diabetes mellitus with peripheral neuropathy. However, there is a dearth of literature regarding the gait characteristics in type 2 diabetes mellitus (T2DM) without peripheral neuropathy. Therefore, the present study is focused on identifying gait changes in early type 2 diabetes mellitus without peripheral neuropathy. Objective: To analyze the gait characteristics in Type 2 diabetes mellitus without peripheral neuropathy. Methods: After obtaining ethical clearance from Institutional Ethical Committee (IEC), 36 T2DM without peripheral neuropathy and 32 matched healthy subjects were recruited. Gait characteristics (step duration, gait cycle length, gait cycle duration, stride duration, step length, double stance duration) of all the subjects were analyzed using Windtrack dynamic foot scanner. Data were analyzed using Independent‘t’ test to find the difference between the groups (step duration, gait cycle length, gait cycle duration) and Mann-Whitney test was used to analyze the step length and double stance duration to find difference between the groups. Level of significance was kept at P<0.05. Results: Result analysis showed significant decrease in step duration, gait cycle length, gait cycle duration, step length, double stance duration in T2DM subjects as compared to healthy subjects. We also observed a mean increase in stride duration in T2DM subjects compared to healthy subjects.

Keywords: type 2 diabetes mellitus, dynamic foot scan, gait characteristics, medical and health sciences

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Authors: Brahim Fares Zaidi

Abstract:

Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: ARSDS, HTK, HMM, MFCC, PLP

Procedia PDF Downloads 79

Authors: Hadeer M. Sayed, Hesham E. El Deeb, Shereen A. Taie

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In the big data era, we are facing a diversity of datasets from different sources in different domains that describe a single life event. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. Multimodal fusion is the concept of integrating information from multiple modalities in a joint representation with the goal of predicting an outcome through a classification task or regression task. In this paper, multimodal fusion techniques are classified into two main classes: model-agnostic techniques and model-based approaches. It provides a comprehensive study of recent research in each class and outlines the benefits and limitations of each of them. Furthermore, the audiovisual speech recognition task is expressed as a case study of multimodal data fusion approaches, and the open issues through the limitations of the current studies are presented. This paper can be considered a powerful guide for interested researchers in the field of multimodal data fusion and audiovisual speech recognition particularly.

Keywords: multimodal data, data fusion, audio-visual speech recognition, neural networks

Procedia PDF Downloads 85