Search results for: interval features

Authors: Alessandra Marcelletti, Giovanni Trovato

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Corruption in public offices is found to be the reflection of country-specific features, however, the exact magnitude and the statistical significance of its determinants effect has not yet been identified. The paper aims to propose an estimation method to measure the impact of country fundamentals on corruption, showing that covariates could differently affect the extent of corruption across countries. Thus, we exploit a model able to take into account different factors affecting the incentive to ask or to be asked for a bribe, coherently with the use of the Corruption Perception Index. We assume that discordant results achieved in literature may be explained by omitted hidden factors affecting the agents' decision process. Moreover, assuming homogeneous covariates effect may lead to unreliable conclusions since the country-specific environment is not accounted for. We apply a Finite Mixture Model with concomitant variables to 129 countries from 1995 to 2006, accounting for the impact of the initial conditions in the socio-economic structure on the corruption patterns. Our findings confirm the hypothesis of the decision process of accepting or asking for a bribe varies with specific country fundamental features.

Keywords: Corruption, Finite Mixture Models, Concomitant Variables, Countries Classification

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Qi Wang

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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Aziza Barnawi

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Background: Regular exercise reduces risk factors associated with cardiovascular diseases. Over the past decade, exercise interventions have been introduced to reduce the risk of and prevent coronary artery disease (CAD). Elevated low-density lipoproteins (LDL) contribute to the formation of atherosclerosis, its manifestations on the endothelial narrow the coronary artery and affect the endothelial function. Therefore, flow-mediated dilation (FMD) technique is used to assess the function. The results of previous studies have been inconsistent and difficult to interpret across different types of exercise programs. The relationship between exercise therapy and lipid levels has been extensively studied, and it is known to improve the lipid profile and endothelial function. However, the effectiveness of exercise in altering LDL levels and improving blood flow is controversial. Objective: This review aims to explore the evidence and quantify the impact of exercise training on LDL levels and vascular function by FMD. Methods: Electronic databases were searched PubMed, Google Scholar, Web of Science, the Cochrane Library, and EBSCO using the keywords: “low and/or moderate aerobic training”, “blood flow”, “atherosclerosis”, “LDL mediated blood flow”, “Cardiac Rehabilitation”, “low-density lipoproteins”, “flow-mediated dilation”, “endothelial function”, “brachial artery flow-mediated dilation”, “oxidized low-density lipoproteins” and “coronary artery disease”. The studies were conducted for 6 weeks or more and influenced LDL levels and/or FMD. Studies with different intensity training and endurance training in healthy or CAD individuals were included. Results: Twenty-one randomized controlled trials (RCTs) (14 FMD and 7 LDL studies) with 776 participants (605 exercise participants and 171 control participants) met eligibility criteria and were included in the systematic review. Endurance training resulted in a greater reduction in LDL levels and their subfractions and a better FMD response. Overall, the training groups showed improved physical fitness status compared with the control groups. Participants whose exercise duration was ≥150 minutes /week had significant improvement in FMD and LDL levels compared with those with <150 minutes/week.Conclusion: In conclusion, although the relationship between physical training, LDL levels, and blood flow in CAD is complex and multifaceted, there are promising results for controlling primary and secondary prevention of CAD by exercise. Exercise training, including resistance, aerobic, and interval training, is positively correlated with improved FMD. However, the small body of evidence for LDL studies (resistance and interval training) did not prove to be significantly associated with improved blood flow. Increasing evidence suggests that exercise training is a promising adjunctive therapy to improve cardiovascular health, potentially improving blood flow and contributing to the overall management of CAD.

Keywords: exercise training, low density lipoprotein, flow mediated dilation, coronary artery disease

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Authors: Roshani Gusain, Deep Sinha, Karan Nayal, Anmol Kumar Mishra, Manav Singh

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In this research paper, we introduce "Mind Care Assistant - Companion App", which is a Flutter and Firebase-based mental health monitor. The app wants to improve and monitor the mental health of its users, it uses noninvasive ways to check for a change in their emotional state. By responding to questions, the app will provide individualized suggestions ᅳ tasks and mindfulness exercises ᅳ for users who are depressed or anxious. The app features a chat-bot that incorporates cognitive behavioural therapy (CBT) principles and combines natural language processing with machine learning to develop personalised responses. The feature of the app that makes it easy for us to choose between iOS and Android is cross-platform, which allows users from both mobile systems to experience almost no changes in their interfaces. With Firebase integration synchronized and real-time data storage, security is easily possible. The paper covers the architecture of the app, how it was developed and some important features. The primary research result presents the promise of a "Mind Care Assistant" in mental health care using new wait-for-health technology, proposing a full stack application to be able to manage depression/anxiety and overall Mental well-being very effectively.

Keywords: mental health, mobile application, flutter, firebase, Depression, Anxiety

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Authors: M. K. Dce

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The aim of this paper is to investigate the behavior of simply supported beams having rectangular section and subjected to uniformly distributed load (UDL). In this study five beams of span 5m, 6m, 7m and 8m have been considered. The width of all the beams is 400 mm and span to depth ratio has been taken as 12. The superimposed live load has been increased from 10 kN/m to 25 kN/m at the interval of 5 kN/m. The analysis of the beams has been carried out using the elastic beam theory. On the basis of present study it has been concluded that the maximum bending moment as well as deflection occurs at the mid-span of simply supported beam and its magnitude increases in proportion to magnitude of UDL. Moreover, the study suggests that the maximum moment is proportional to square of span and maximum deflection is proportional to fourth power of span.

Keywords: beam, UDL, bending moment, deflection, elastic beam theory

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Authors: Mumtaz Ali, Hamzullah Khan, Khalid Khanzada, Shahid Ayub, Aurangzeb Wazir

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Objective: To analyse the death of patients treated in neuro-surgical ICU for severe head injuries from different perspectives. The evaluation of the data so obtained to help improve the health care delivery to this group of patients in ICU. Study Design: It is a descriptive study based on retrospective analysis of patients presenting to neuro-surgical ICU in Lady Reading Hospital, Peshawar. Study Duration: It covered the period between 1st January 2009 to 31st December 2009. Material and Methods: The Clinical record of all the patients presenting with the clinical radiological and surgical features of severe head injuries, who expired in neuro-surgical ICU was collected. A separate proforma which mentioned age, sex, time of arrival and death, causes of head injuries, the radiological features, the clinical parameters, the surgical and non surgical treatment given was used. The average duration of stay and the demographic and domiciliary representation of these patients was noted. The record was analyzed accordingly for discussion and recommendations. Results: Out of the total 112 (n-112) patients who expired in one year in the neuro-surgical ICU the young adults made up the majority 64 (57.14%) followed by children, 34 (30.35%) and then the elderly age group: 10 (8.92%). Road traffic accidents were the major cause of presentation, 75 (66.96%) followed by history of fall; 23 (20.53%) and then the fire arm injuries; 13 (11.60%). The predominant CT scan features of these patients on presentation was cerebral edema, and midline shift (diffuse neuronal injuries). 46 (41.07%) followed by cerebral contusions. 28 (25%). The correctable surgical causes were present only in 18 patients (16.07%) and the majority 94 (83.92%) were given conservative management. Of the 69 (n=69) patients in which CT scan was repeated; 62 (89.85%) showed worsening of the initial CT scan abnormalities while in 7 cases (10.14%) the features were static. Among the non surgical cases both ventilatory therapy in 7 (6.25%) and tracheostomy in 39 (34.82%) failed to change the outcome. The maximum stay in the neuro ICU leading upto the death was 48 hours in 35 (31.25%) cases followed by 31 (27.67%) cases in 24 hours; 24 (21.42%) in one week and 16 (14.28%) in 72 hours. Only 6 (5.35%) patients survived more than a week. Patients were received from almost all the districts of NWFP except. The Hazara division. There were some Afghan refugees as well. Conclusion: Mortality following the head injuries is alarmingly high despite repeated claims about the professional and administrative improvement. Even places like ICU could not change the out come according to the desired aims and objectives in the present set up. A rethinking is needed both at the individual and institutional level among the concerned quarters with a clear aim at the more scientific grounds. Only then one can achieve the desired results.

Keywords: Glasgow Coma Scale, pediatrics, geriatrics, Peshawar

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Authors: Alluri Swaroopa, L. V. N. Prasad

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Alone with fast urbanization in world, traffic control problem became a big issue in urban construction. Having an efficient and reliable traffic control system is crucial to macro-traffic control. Traffic signal is used to manage conflicting requirement by allocating different sets of mutually compatible traffic movement during distinct time interval. Many approaches have been made proposed to solve this discrete stochastic problem. Recognizing the need to minimize right-of-way impacts while efficiently handling the anticipated high traffic volumes, the proposed alternative system gives effective design. This model allows for increased traffic capacity and reduces delays by eliminating a step in maneuvering through the freeway interchange. The concept proposed in this paper involves construction of bridges and ramps at intersection of four roads to control the vehicular congestion and to prevent traffic breakdown.

Keywords: bridges, junctions, ramps, urban traffic control

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Authors: Hala M. El-hanbuli, Mohammed F. Darweesh

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The incidence rates of any tumor vary hugely with geographical location. Basal Cell Carcinoma (BCC) is one of the most common skin cancer that has many histopathologic subtypes. Objective: The aim was to study the histopathological features of BCC cases that were received in the Pathology Department, Kasr El-Aini hospital, Cairo University, Egypt during the period from Jan 2004 to Dec 2013 and to evaluate the clinical characters through the patient data available in the request sheets. Methods: Slides and data of BCC cases were collected from the archives of the pathology department, Kasr El-Aini hospital. Revision of all available slides and histological classification of BCC according to WHO (2006) was done. Results: A total number of 310 cases of BCC representing about 65% from the total number of malignant skin tumors examined during the 10-years duration in the department. The age ranged from 8 to 84 years, the mean age was (55.7 ± 15.5). Most of the patients (85%) were above the age of 40 years. There was a slight male predominance (55%). Ulcerated BCC was the most common gross picture (60%), followed by nodular lesion (30%) and finally the ulcerated nodule (10%). Most of the lesions situated in the high-risk sites (77%) where the nose was the most common site (35%) followed by the periocular area (22%), then periauricular (15%) and finally perioral (5%). No lesion was reported outside the head. The tumor size was less than 2 centimeters in 65% of cases, and from 2-5 centimeters in the lesions' greatest dimension in the rest of cases. Histopathological reclassification revealed that the nodular BCC was the most common (68%) followed by the pigmented nodular (18.75%). The histologic high-risk groups represented (7.5%) about half of them (3.75%) being basosquamous carcinoma. The total incidence for multiple BCC and 2nd primary was 12%. Recurrent BCC represented 8%. All of the recurrent lesions of BCC belonged to the histologic high-risk group. Conclusion: Basal Cell Carcinoma is the most common skin cancer in the 10-year survey. Histopathological diagnosis and classification of BCC cases are essential for the determination of the tumor type and its biological behavior.

Keywords: basal cell carcinoma, high risk, histopathological features, statistical analysis

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Authors: Francine Siy, Stephen Buñi

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Safety plays a significant role in everyone’s life on a day-to-day basis. We wish ourselves and our loved ones their safety as we all venture out on our daily commute. The road is undeniably dangerous and unpredictable, with abundant traffic collisions and pedestrians experiencing various injuries. For bicycle users, the risk of accidents is even more exacerbated, and injuries may be severe. Even when cyclists try their best to be safe and protected, the possibility of encountering danger is always there. Despite being equipped with protective gear, safety is never guaranteed. Cyclists often settle for helmets and standard reflector vests to establish a presence on the road. There are different types of vests available, depending on the profession. However, traditional reflector vests, mostly seen on construction workers and traffic enforcers, were not designed for riders and their protection from injuries. With insufficient protection for riders, they need access to ergonomically designed equipment and accessories that suit the riders and cater to their needs. This research aimed to offer a protective vest with safety features for riders that is comfortable, effective, durable, and intuitive. This sheds light and addresses the safety of the biker population, which continuously grows through the years. The product was designed and developed by gathering data and using the cognitive mapping method to ensure that all qualitative and quantitative data were considered in this study to improve other existing products that do not have the proper design considerations. It is known that available equipment for cyclists is often sold separately or lacks the safety features for cyclists traversing open roads. Each safety feature like the headlights, reflectors, signal or rear lights, zipper pouch, body camera attachment, and wireless remote control all play a particular role in helping cyclists embark on their daily commute. These features aid in illumination, visibility, easy maneuvering, convenience, and security, allowing cyclists to go for a safer ride that is of use throughout the day. The product is designed and produced effectively and inexpensively without sacrificing the quality and purpose of its usage.

Keywords: bicycle accessory, protective gear, safety, transport, visibility

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Authors: Mohammed Habchi, Sidi Mohammed Mesli, Rafik Benallal, Mohammed Kotbi

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On the basis of four partial correlation functions and some geometric constraints obtained from neutron scattering experiments, a Reverse Monte Carlo (RMC) simulation has been performed in the study of the aqueous electrolyte LiCl6H2O at the glassy state. The obtained 3-dimensional model allows computing pair radial and orientational distribution functions in order to explore the structural features of the system. Unrealistic features appeared in some coordination peaks. To remedy to this, we use the Hybrid Reverse Monte Carlo (HRMC), incorporating an additional energy constraint in addition to the usual constraints derived from experiments. The energy of the system is calculated using an Environment Dependence Interaction Potential (EDIP). Ions effects is studied by comparing correlations between water molecules in the solution and in pure water at room temperature Our results show a good agreement between experimental and computed partial distribution functions (PDFs) as well as a significant improvement in orientational distribution curves.

Keywords: LiCl6H2O, glassy state, RMC, HRMC

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Authors: Xiang Shijie, Zhou Dong, Tian Dan

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Speaker recognition is a crucial task in the field of speech processing, aimed at identifying individuals based on their vocal characteristics. However, existing speaker recognition methods face numerous challenges. Traditional methods primarily rely on audio signals, which often suffer from limitations in noisy environments, variations in speaking style, and insufficient sample sizes. Additionally, relying solely on audio features can sometimes fail to capture the unique identity of the speaker comprehensively, impacting recognition accuracy. To address these issues, we propose a multi-modal network architecture that simultaneously processes both audio and text signals. By gradually integrating audio and text features, we leverage the strengths of both modalities to enhance the robustness and accuracy of speaker recognition. Our experiments demonstrate significant improvements with this multi-modal approach, particularly in complex environments, where recognition performance has been notably enhanced. Our research not only highlights the limitations of current speaker recognition methods but also showcases the effectiveness of multi-modal fusion techniques in overcoming these limitations, providing valuable insights for future research.

Keywords: feature fusion, memory network, multimodal input, speaker recognition

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Authors: Kenan Saatcioglu, Fatma Koc

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When the immigration is considered, it has been found that communities were continuously influenced by the immigrations from the date of the emergence of mankind until the day. The political, social and economic reasons seen at the various periods caused the communities go to new places from where they have lived before. Immigrations have occurred as a result of unequal opportunities among communities, social exclusion and imposition, compulsory homeland emerging politically, exile and war. Immigration is a social tool that is defined as a geographical relocation of people from a housing unit (city, village etc.) to another to spend all or part of their future lives. Immigrations have an effect on the history of humanity directly or indirectly, revealing new dimensions for communities to evaluate the concept of homeland. With these immigrations, communities carried their cultural values to their new settlements leading to a new interaction process. With this interaction process both migrant and native community cultures were reshaped and richer cultural values emerged. The clothes of these communities are amongst the most important visual evidence of this rich cultural interaction. As a result of these immigrations, communities affected each other culture’s clothing mutually and they started adding features of other cultures to the garments of its own, resulting new clothing cultures in time. The cultural and historical differences between these communities are seem to be the most influential factors of keeping the clothing cultures of the people alive. The most important and tragic of these immigrations took place after the Turkish War of Independence that was fought against Greece in 1922. The concept of forced immigration was a result of Lausanne Peace Treaty, which was signed between Turkish and Greek governments on 30th January 1923. As a result Greek Orthodoxes, who lived in Turkey (Anatolia and Thrace) and Muslim Turks, who lived in Greece were forced to immigrate. In this study, clothing features of Greek Orthodox woman immigrants who emigrated from Turkey to Greece in the period of the ‘1923 Greek-Turkish Population Exchange’ are aimed to be examined. In the study using the descriptive research method, before the ‘1923 Greek-Turkish Population Exchange’, the clothings belong to Greek Orthodox woman immigrants who lived in ‘Konya (Iconium)’ region in the Ottoman Empire, are discussed. In the study that is based on two different clothings belonging to ‘Konya (Iconium)’ region in the clothing collection archive at the ‘National Historical Museum’ in Greece, clothings of the Greek Orthodox woman immigrants are discussed with cultural norms, beliefs, values as well as in terms of form, ornamentation and dressing styles. Technical drawings are provided demonstrating formal features of the clothing parts that formed clothing integrity and their properties are described with the use of related literature in this study. This study is of importance that that it contains Greek Orthodox refugees’ clothings that are found in the clothing collection archive at the ‘National Historical Museum’ in Greece reflecting the cultural identities, providing information and documentation on the clothing features of the ‘1923 Greek-Turkish Population Exchange’.

Keywords: clothing, Greece, Greek Orthodoxes, immigration, national historical museum, Turkey

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Authors: Eyal Boers

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This paper shall demonstrate how films can offer a valuable and innovative approach to the study of images, stereotypes, and national identity. "Black Book" ("Zwartboek", 2006), a World War Two film directed by Paul Verhoeven, tells the story of Rachel Stein, a young Jewish woman who becomes a member of a resistance group in the Netherlands. The main hypothesis in this paper maintains that Rachel's character possesses both features of the Dutch prototype (a white, secular, sexual, freedom-loving individualist who seems "Dutch" enough to be accepted into a Dutch resistance group and even infiltrate the local Nazi headquarters) and features which can be defined as specifically Jewish (a black-haired victim persecuted by the Nazis, transforming herself into a gentile, while remaining loyal to her fellow Jews and ultimately immigrating to Israel and becoming a Hebrew teacher in a Kibbutz). Finally, this paper claims that Rachel's "Dutchness" is symptomatic of Dutch nostalgia in the 21st century for the Jews as "others" who blend into dominant Dutch culture, while Rachel's "Jewish Otherness" reflects a transnational identity – one that is always shifting and traverses cultural and national boundaries. In this sense, a film about Dutch Jews in the Second World War reflects on issues of identity in the 21st Century.

Keywords: Dutch, film, stereotypes, identity

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Authors: B. Farahani, R. Fadil, A. Aboonabi, B. Hoffmann, J. Loscheider, K. Tavakolian, S. Arzanpour

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Wheelchair users spend long hours in a sitting position, and selecting the right cushion is highly critical in preventing pressure ulcers in that demographic. Pressure mapping systems (PMS) are typically used in clinical settings by therapists to identify the sitting profile and pressure points in the sitting area to select the cushion that fits the best for the users. A PMS is a flexible mat composed of arrays of distributed networks of flexible sensors. The output of the PMS systems is a color-coded image that shows the intensity of the pressure concentration. Therapists use the PMS images to compare different cushions fit for each user. This process is highly subjective and requires good visual memory for the best outcome. This paper aims to develop an image processing technique to analyze the images of PMS and provide an objective measure to assess the cushions based on their pressure distribution mappings. In this paper, we first reviewed the skeletal anatomy of the human sitting area and its relation to the PMS image. This knowledge is then used to identify the important features that must be considered in image processing. We then developed an algorithm based on those features to analyze the images and rank them according to their fit to the users' needs.

Keywords: dynamic cushion, image processing, pressure mapping system, wheelchair

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Authors: M. Kollar, A. Zieba

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In this paper, we present a method for a time synchronization between the two eNodeBs (eNBs) in E-UTRAN (Evolved Universal Terrestrial Radio Access) network. The two eNBs are cooperating in so-called inter eNB CA (Carrier Aggregation) case and connected via asymmetrical IP network. We solve the problem by using broadcasting signals generated in E-UTRAN as synchronization signals. The results show that the time synchronization with the proposed method is possible with the error significantly less than 1 ms which is sufficient considering the time transmission interval is 1 ms in E-UTRAN. This makes this method (with low complexity) more suitable than Network Time Protocol (NTP) in the mobile applications with generated broadcasting signals where time synchronization in asymmetrical network is required.

Keywords: IP scheduled throughput, E-UTRAN, Evolved Universal Terrestrial Radio Access Network, NTP, Network Time Protocol, assymetric network, delay

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Authors: Rajpal Kaur, Pooja Choudhary

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Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

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Authors: Kay Thinzar Phu, Lwin Lwin Oo

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In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Akeel Noori Al-Mulla Hwaish

Abstract:

The environmental impact, economics, social and cultural factors, and the processes by which people define history and meaning had influenced the dynamic shape and character of the traditional Islamic Arabic city. Therefore, in regard to the period when Islam was at its peak (7th- 13th Centuries), Islamic city wasn’t the highly dynamited at the scale of buildings and city planning that demonstrates a distinguished city as an ‘Islamic’ as appeared after centuries when the function of the buildings and their particular arrangement and planning scheme in relation to one another that defined an Islamic city character. The architectural features of the urban fabric of the traditional Arabic Islamic city are a ‎reflection of the spiritual, social, and cultural characteristics of the people. It is a ‎combination of Islamic values ‘Din’ and life needs ‘Dunia’ as Prophet Muhammad built the first Mosque in ‎Madinah in the 1st year of his migration to it, then the Suq or market on 2nd of Hijrah, attached to ‎the mosque to signify the birth of a new Muslims community which considers both, ‎‎’Din’ and ‘Dunia’ and initiated nucleus for what which called after that as an ‘Islamic’ city. This research will discuss the main characteristics and components of the traditional Arab cities and demonstrate the impact of the Islamic values on shaping the planning layout and general built environment features of the early traditional Arab cities.

Keywords: urban, Islamic, Arabic, city

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Authors: Ajitesh Pal, Arpan Datta Roy, H. K. Pratihari

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The different illegal firearms crudely made by skilled gunsmith from scrap materials are popularly known as country made firearms. Such firearms along with improvised ammunition are clandestinely marketed at the cheaper price without any license to the extremist group, criminal, poachers and firearm lovers. As per National Crime Records Bureau (NCRB), MHA, Govt of India about 80% firearm cases are committed by country made/improvised firearms. The ballistic division of the laboratory has examined a good number of cases. The analysis of firearm cases received for forensic examination revealed that 7.65mm calibre pistols mostly improvised firearm are commonly used in firearm related crime cases. In the present communication, physical parameters and other characteristics features of some 7.65mm calibre pistols have been discussed in detail. The detailed study on country made (CM) firearm will help to prepare a database related to type of material used, origin of the raw material and tools used for inscription. The study also includes to establish the chemistry of propellants & head stamp pattern. The database will be helpful to the firearm examiners, researchers, students pursuing study on forensic science as reference material.

Keywords: improvised pistol, stringent gun law, working mechanism, parameters, database

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Sanjay Saraf

Abstract:

The oral cavity can be the site for early manifestations of mucocutaneous disorders (MD) or the only site for occurrence of these disorders. It can also exhibit oral lesions with simultaneous associated skin lesions. The MD involving the oral mucosa commonly presents with signs such as ulcers, vesicles and bullae. The unique environment of the oral cavity may modify these signs of the disease, thereby making the clinical diagnosis an arduous task. In addition to the unique environment of oral cavity, the overlapping of the signs of various mucocutaneous disorders, also makes the clinical diagnosis more intricate. The aim of this review is to present the oral signs of dermatological disorders having common oral involvement and emphasize their importance in early detection of the systemic disorders. The aim is also to highlight the necessity of oral examination by a dermatologist while examining the skin lesions. Prior to the oral examination, it must be imperative for the dermatologists and the dental clinicians to have the knowledge of oral anatomy. It is also important to know the impact of various diseases on oral mucosa, and the characteristic features of various oral mucocutaneous lesions. An initial clinical oral examination is may help in the early diagnosis of the MD. Failure to identify the oral manifestations may reduce the likelihood of early treatment and lead to more serious problems. This paper reviews the oral manifestations of immune mediated dermatological disorders with common oral manifestations.

Keywords: dermatological investigations, genodermatosis, histological features, oral examination

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Authors: A. Baqqal, O. Abduhamid, H. Abdul-Hameed, T. Messager, G. Ayoub

Abstract:

In this contribution, the effect of crystal content on the cyclic response of semi-crystalline polyethylene is studied over a large strain range. Experimental observations on a high-density polyethylene with 72% crystal content and an ultralow density polyethylene with 15% crystal content are reported. The cyclic stretching does appear a thermoplastic-like response for high crystallinity and an elastomeric-like response for low crystallinity, both characterized by a stress-softening, a hysteresis and a residual strain, whose amount depends on the crystallinity and the applied strain. Based on the experimental observations, a unified viscoelastic-viscoplastic constitutive model capturing the polyethylene cyclic response features is proposed. A two-phase representation of the polyethylene microstructure allows taking into consideration the effective contribution of the crystalline and amorphous phases to the intermolecular resistance to deformation which is coupled, to capture the strain hardening, to a resistance to molecular orientation. The polyethylene cyclic response features are captured by introducing evolution laws for the model parameters affected by the microstructure alteration due to the cyclic stretching.

Keywords: cyclic loading unloading, polyethylene, semi-crystalline polymer, viscoelastic-viscoplastic constitutive model

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

Abstract:

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

Procedia PDF Downloads 144