A Prosodic Turkish text-to-speech synthesizer

Naturalness in Text-to-Speech systems is very important in achieving high quality waveform. The naturalness of the waveform is highly correlated with phonetic coverage and prosodic features such as, duration and F0 contour. Duration determines the timing for the synthesized phoneme, whereas F0 contour determines fundamental frequency component of the waveform. This thesis presents the development of a prosodic Text-to-Speech System for Turkish Language using the Festival Tool [31]. We describe a complete realization of a new male voice, covering allophones of Turkish using duration and F0 parameters. The duration of the allophones and the word stress have been studied extensively. Sentence stress and phrasal stress are also discussed by in less detail. Carrier words are designed approximately for all allophone-allophone combinations. 1680 carrier words are recorded in a sound-proof recording studio. LPC (linear predictive coding) and RES (residual) parameters are computed. The text normalisation module is implemented for abbreviations and numbers. Durations for the allophones are entered. Sentence level and word level F0 generation modules are implemented. By increasing the number of phonemes and giving prosody we obtained a more natural sounding Text-to-Speech System for Turkish Language

Video based detection of driver fatigue

This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. We have collected two datasets including facial and head movement measures. Head motion is collected through an accelerometer for the first dataset (UYAN-1) and an automatic video based head pose detector for the second dataset (UYAN-2). We use outputs of the automatic classifiers of the facial action coding system (FACS) for detecting drowsiness. These facial actions include blinking and yawn motions, as well as a number of other facial movements. These measures are passed to a learning-based classifier based on multinomial logistic regression. In UYAN-1 the system is able to predict sleep and crash episodes during a driving computer game with 0.98 performance area under the receiver operator characteristic curve for across subjects tests. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis reveals new information about human facial behavior during drowsy driving. In UYAN-2 fine discrimination of drowsy states are also explored on a separate dataset. The degree to which individual facial action units can predict the difference between moderately drowsy to acutely drowsy is studied. Signal processing techniques and machine learning methods are employed to build a person independent acute drowsiness detection system. Temporal dynamics are captured using a bank of temporal filters. Individual action unit predictive power is explored with an MLR based classifier. Best performing five action units have been determined for a person independent system. The system is able to obtain 0.96 performance of area under the receiver operator characteristic curve for a more challenging dataset with the combined features of the best performing 5 action units. Moreover the analysis reveals new markers for different levels of drowsiness

An online handwriting recognition system for Turkish

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon

BUSINESS INTELLIGENCE FOR A SUPPLY CHAIN MANAGEMENT SYSTEM

ABSTRAC

Bilgi Yönetimi ve Örgütsel Bilgelik İlişkisi Üzerine Sektörel Bir Değerlendirme: Metal ve Makine Sanayi Örneği

Bilgi çağıolarak nitelendirilen ve 1950'li yıllardan günümüze kadar gelen dönemde bilgi, özellikle örgütler açısından sermaye ve somut varlıklarından çok daha önemli bir hale gelmiştir. Bununla beraber bilginin rekabet avantajısağlayacak ve örgütü vizyonuna ulaştıracak bir güç haline gelmesi için örgütün sahip olduğu tüm veri, enformasyon ve bilginin elde edilme, korunma, yorumlanma, paylaşılma ve uygulanmasısüreçlerinin etkin biçimde yönetilmesi gerekmektedir. Bilgi yönetimi olarak adlandırılan bu yönetim dizisi, değer yaratıcıbilginin verimli bir biçimde teknolojik uygulamalara aktarılmasındaki süreçlerin tanımlanması, modellenmesi ve örgütün amaçlarıdoğrultusunda bilginin kullanılmasıiçin yapılmasıgerekenleri belirten bir yol haritasıdır. Bilgi yönetimi, örgüt başarısınıkoruyan, örgütün başarıya ulaşmasına katkısağlayan, örgütsel öğrenmeyi hızlandıran, süreçlerde daha hızlıiyileştirme olanağısağlayan, entelektüel varlıklardan en üst seviyede yararlanan, örtük bilgiden açık bilgiye kadar her türlü bilgiyi kapsayan bir disiplindir. Bilgi yönetimi her ne kadar örgüt başarısıiçin mutlak bir öğe olsa da yakın gelecekte bilgi yönetiminin yerini örgütsel bilgelik olgusunun alacağısöylenebilir. Örgütsel bilgelik, temelde veri, enformasyon, bilgi hiyerarşisinin bir sonraki basamağıolarak kabul edilen bilgeliğin örgütsel bağlamda ele alınmasıdır. Örgütlerin gelecekteki başarılarının temelini oluşturan örgütsel bilgelik, örgütün bilge olma durumu ve niteliğini; beklenmedik durumlara akılcıtepkiler verebilmesini; örgütün mevcut ve yeni bilgisini yönetmesini; tüm paydaşlarına karşı, iyi, ahlâklıve örnek davranışlarısergilemesini ifade etmektedir. Örgütsel bilgelik, işetiği, sürdürülebilirlik, dönüşümsel liderlik, işyeri demokrasisi, kurumsal vatandaşlık ve sosyal sorumluluk gibi örgütsel fonksiyonlara katkısağlarken her bir fonksiyonun bilgi, anlayış, daha iyisini yapma gibi unsurlarla kendi özelliklerinden daha fazla katma değer yaratmalarınıda amaçlamaktadır. Bu bağlamda bu çalışmanın temel amacıişletmelerin bilgi yönetimi uygulamalarıile örgütsel bilgelik algısıarasındaki ilişkinin sektörel bazda analiz edilmesidir. Bu doğrultuda çalışmada öncelikle bilgi yönetimi uygulamalarıve örgütsel bilgelik algısıve aralarındaki nedensel ilişki bağlamında literatür bilgisi verilmiştir. Çalışmanın araştırma kısmında ise Konya Sanayi Odasına kayıtlımetal ve makine sektöründe faaliyet gösteren işletmeler üzerinde yürütülen alan araştırmasına yönelik bulgular değerlendirilmiştir. Sektörel bazda yapılan bu değerlendirmeden elde edilen en önemli bulgu, bilgi yönetimi uygulamalarıile örgütsel bilgelik algısıarasında pozitif bir ilişkinin olduğu ve bilgi yönetiminin örgütsel bilgelik ölçeği ile açıklanabileceği şeklindedir

Optical coherens tomography angiography findings in adult patients with sickle cell anemia

Introduction: Sickle hemoglobin (HbS) is characterized by a mutation in the beta globin gene, which contains a single nucleotide (GA G à GTG) that replaces glutamine with valine at amino acid position six. These hemoglobins are insoluble at low oxygen concentrations and tend to crystallize. Interactions between sickle red blood cells and vascular endothelium often lead to vaso-occlusion and tissue ischemia. Detecting sickle cell retinopathy in its early stages is important to identify proliferative changes and prevent its long-term consequences, including vitreous hemorrhage, retinal detachment, and vision loss. Optical coherence tomography angiography OCTA is an easy-to-apply imaging method that does not require the use of dyes that accurately show retinal microvascularization. Purpose: It was aimed to measure macular vascular density (VD) and foveal avascular zone (FAZ) in patients with sickle cell anemia by OCTA and to compare with healthy controls. Methods: Eighteen right eyes (group 1) of 18 adult patients with sickle cell anemia followed in the hematology clinic of Dicle University, and 25 right eyes of 25 age- and sex-matched healthy individuals (group 2) were included in the study. Those with systemic diseases other than sickle cell disease, eye diseases such as retinal vascular disease, maculopathy, glaucoma, and those with high refractive errors were excluded from the study. Macular superficial capillary plexus (SCP) and deep capillary plexus (DCP) vascular density (VD) and FAZ measurements were made with OCTA (RTVue-XR Avanti; Optovue Inc., Fremont, CA, USA). Patients with normal fundus examination and no other systemic disease were included in the study. Vascular density measurements were made in the macula with a 3x3 mm scanning mode. Vascular density in superficial and deep capillary plexus was compared in 8 sectors; as the whole image, parafovea, superior hemi, inferior hemi, temporal, superior, nasal and inferior. Image quality below 8/10 were excluded from the study. Results: The mean age of group 1 was 24.73±6.60, group 2 was 23.70±3.19 (p=0.572). The female/male ratio was 10/8 in group 1 and 12/13 in group 2 (p=0.500). In patients with sickle cell anemia, there was a significant lower VD in DCP in all sectors compared to the control group (p<0.001) (Figure 1). There was a significant lower VD in the superficial capillary plexus only in the temporal region in group 1 compared to group 2 (p=0.015). Superficial FAZ was found to be statistically significantly larger in group 1 than group 2 (p=0.001). Deep FAZ width was found to be similar in both groups (p=0.145) Conclusion: Sickle cell anemia causes a significant decrease in vascular density in the deep capillary plexus. Knowing exactly the status of retinal vascular density and FAZ in sickle cell anemia will guide the pathophysiology of retinopathy and will help prevent retinopathy at an early stage

Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

Discrimination of moderate and acute drowsiness based on spontaneous facial expressions

It is important for drowsiness detection systems to identify different levels of drowsiness and respond appropriately at each level. This study explores how to discriminate moderate from acute drowsiness by applying computer vision techniques to the human face. In our previous study, spontaneous facial expressions measured through computer vision techniques were used as an indicator to discriminate alert from acutely drowsy episodes. In this study we are exploring which facial muscle movements are predictive of moderate and acute drowsiness. The effect of temporal dynamics of action units on prediction performances is explored by capturing temporal dynamics using an overcomplete representation of temporal Gabor Filters. In the final system we perform feature selection to build a classifier that can discriminate moderate drowsy from acute drowsy episodes. The system achieves a classification rate of .96 A’ in discriminating moderately drowsy versus acutely drowsy episodes. Moreover the study reveals new information in facial behavior occurring during different stages of drowsiness

A novel approach for sustainable supply Chain management with analyzing the effective governance under fuzzy uncertainty

Nowadays, knowledge has become one of the most important tools of power, distributing public services that accept the audience as citizens and not consumers and provide the principle of services without financial worries. Moreover, the urban products have a specific production and distribution channel that should be assessed. In this research, a mathematical framework is proposed for designing the supply chain network of urban products. The main contribution of this research is to incorporate the effect of public service into urban products' supply chain planning. In this regard, a mixed-integer mathematical model is proposed. In this mathematical model, an attempt is made to minimize the costs of the product distribution system by considering the effects of production, maintenance, and distribution. Moreover, fuzzy uncertainty has been applied to adapt the mathematical model to real conditions. The numerical results show that if manufacturers and distributors want to strengthen their institutions and maintain their leadership roles as in the past, they can optimize their distribution network structure to achieve the best possible performance. Moreover, technological advances and innovations in production and distribution systems can create a huge leap in profitability

Evaluation of Viral Agents Detected in Children Admitted to Hospital Due to Lower Respiratory Infection

INTRODUCTION: Background\Aim: Viruses are among the most common causes of acute respiratory tract infections. In this study we aimed to investigate the viral pathogens detected in the nasopharyngeal swab specimens obtained from children in our pediatric ward being followed up due to acute lower respiratory tract infection and to analyse the distribution of the pathogens by age and months. METHODS: Method: This research was carried out between January 2019-January 2020, 289 patients (44.2% female, 55.7% male) admitted for acute respiratory tract infection were included. Patient records were reviewed retrospectively. Viral agents distribution was analysed according to age, sex and months (seasons/seasonall variation). RESULTS: Findings: In 117 (40.5%) of 289 patients viruses were not detected (negative) in the respiratory tract, in 172 (59.5%) they were detected (positive). In 148 (86%) patients a single agent, in 22 (12.8%) patients two agents, in 1 (0.6%) patient three agents, in 1 (0.6%) patient four agents were found. The most common virus detected was rinovirüs (HRV) (23.9%), the second most common was found to be respiratory syncytial virus A (RSVA) (16.3%). The most common agent in ages 0-3 was HRV, after 3 years the most common agent was influenza B virüs (IBV). IBV was the most common during the winter, HRV was the most common in the other seasons. DISCUSSION AND CONCLUSION: Results: In our study 59.5% of admitted children were found to have at least one respiratory virus. Multiplex PCR is a sensitive and specific method which detects viruses that are undetectable by classical methods, gives results in a shorter time compared to classical methods and also a method in which more than one specimen can be processed at the same time. With a faster method diagnosis of viruses, inappropriate use of antibiotics and development of antibiotic resistance can be prevented