Search results for: vehicle color recognition

Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

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Authors: Tzu-Jung Tseng, Pei-Hsuen Han, Tsung-Hsueh Lu

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Background: Choropleth mortality map is commonly used to identify areas with higher mortality risk. However, the use of choropleth map alone might result in the misinterpretation of differences in mortality rates between areas. Two areas with different color shades might not actually have a significant difference in mortality rates. The mortality rates estimated for an area with a small population would be less stable. We suggest of bringing the 95% confidence intervals (CI) into the choropleth mortality map to help users interpret the areal mortality rate difference more properly. Method: In the first choropleth mortality map, we used only three color to indicate standardized mortality ratio (SMR) for each district in Tainan, Taiwan. The red color denotes that the SMR of that district was significantly higher than the Tainan average; on the contrary, the green color suggests that the SMR of that district was significantly lower than the Tainan average. The yellow color indicates that the SMR of that district was not statistically significantly different from the Tainan average. In the second choropleth mortality map, we used traditional sequential color scheme (color ramp) for different SMR in 37 districts in Tainan City with bar chart of each SMR with 95% CI in which the users could examine if the line of 95% CI of SMR of two districts overlapped (nonsignificant difference). Results: The all-causes SMR of each district in Tainan for 2008 to 2013 ranged from 0.77 (95% CI 0.75 to 0.80) in East District to 1.39 Beimen (95% CI 1.25 to 1.52). In the first choropleth mortality map, only 16 of 37 districts had red color and 8 districts had green color. For different causes of death, the number of districts with red color differed. In the first choropleth mortality map we added a bar chart with line of 95% CI of SMR in each district, in which the users could visualize the SMR differences between districts. Conclusion: Through the use of 95% CI the users could interpret the aral mortality differences more properly.

Keywords: choropleth map, small area variation, standardized mortality ratio (SMR), Taiwan

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: Dongqi Li, Yunjia Huang, Tomo Inoue, Kohei Inoue

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With urbanization, urban cultural heritage is facing the impact and destruction of modernization and urbanization. Many historical areas are losing their historical information and regional cultural characteristics, so it is necessary to carry out systematic color planning for historical areas in conservation. As an early focus on urban color planning, Japan has a systematic approach to urban color planning. Hence, this paper selects five merchant towns from the category of important traditional building preservation areas in Japan as the subject of this study to explore the color structure and emotion of this type of historic area. First, the image semantic segmentation method identifies the buildings, roads, and landscape environments. Their color data were extracted for color composition and emotion analysis to summarize their common features. Second, the obtained Internet evaluations were extracted by natural language processing for keyword extraction. The correlation analysis of the color structure and keywords provides a valuable reference for conservation decisions for this historic area in the town. This paper also combines the color structure and Internet evaluation results with generative adversarial networks to generate predicted images of color structure improvements and color improvement schemes. The methods and conclusions of this paper can provide new ideas for the digital management of environmental colors in historic districts and provide a valuable reference for the inheritance of local traditional culture.

Keywords: historic districts, color planning, semantic segmentation, natural language processing

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Banu Hatice Gürcüm, Pınar Arslan, Mahmut Yalçın

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Fashion designers create lots of dresses, suits, shoes, and other clothing and accessories, which are purchased every year by consumers. Fashion trends, sketches of designs, accessories affect the apparel goods, but colors make the finishing touches to an outfit. In all fields of apparel men's, women's, and children's wear, including casual wear, suits, sportswear, formal wear, outerwear, maternity, and intimate apparel, color sells. Thus, specialization in color in apparel is a basic concern each season. The perception of color is the key to sales for every sector in textile business. Mechanism of color perception, cognition in brain and color emotion are unique subjects, which scientists have been investigating for many years. The parameters of color may not be corresponding to visual scales since human emotions induced by color are completely subjective. However, with a very few exception each manufacturer concern their top selling colors for each season through seasonal sales reports of apparel companies. This paper examines sensory and instrumental methods for quantifying color of fabrics and investigates the relationship between fabric color and sale numbers. 5 top selling colors for each season from 10 leading apparel companies in the same segment are taken. The compilation is based according to the sales of the companies for 5 to 10 years. The research’s main concern is the corelation with the magnitude of seasonal color selling figures and the CIE chromaticity coordinates. The colors are chosen from the globally accepted Pantone Textile Color System and the three-dimentional measurement system CIE L*a*b* (CIELAB) is used, L* representing the degree of lightness of color, a* the degree of color ranging from magenta to green, and b* the degree of color ranging from blue to yellow. The objective of this paper is to demonstrate the feasibility of relating color perceptance to a laboratory instrument yielding measurements in the CIELAB system. Our approach is to obtain a total of a hundred reference fabrics to be measured on a laboratory spectrophotometer calibrated to the CIELAB color system. Relationships between the CIE tristimulus (X, Y, Z) and CIELAB (L*, a*, b*) are examined and are reported herein.

Keywords: CIELAB, CIE tristimulus, color preference, fashion

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

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Authors: Ramandeep Kaur, Kamaljit Kaur, Preeti Ahluwalia, Poonam A. Sachdev

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Bread is an ideal vehicle to impart bioactive compounds to the consumers in a convenient manner. This study evaluated bread enriched with red sweet pepper powder (RSP) at 2, 4, 6, 8, 10% and compared to control bread (without RSP). The bread crumbs were assayed for bioactive, physical, nutritional, textural, color, and sensory properties. Bread supplemented with RSP improved its color, nutritional, and bioactive properties. The low moisture content and increased hardness were observed at higher levels of RSP. Color intensity (expressed as L*, a*, b* values) of bread with 2 and 4% RSP were lower than those of high levels, and the same trend was observed for protein, fibre and ash content of bread. Significant (p < 0.05) increases were recorded for bioactive compounds such as total phenols (0.145 to 235 mg GAE/g), antioxidant activity (56% to 78%) and flavonoids (0.112 to 0.379 mg/g) as the level of powder increased. Bread enriched with 8% RSP showed improved sensory profile as compared to control, whereas a further increase in RSP decreased the sensory and textural properties. Thus, RSP act as a natural colorant and functional food that enhanced the functional and nutritional properties of bread and can be used to customize bread for specific health needs.

Keywords: breads, bioactive compounds, red sweet pepper powder, sensory scores

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Authors: Van Nhan Nguyen, Harald Holone

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Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: automatic speech recognition, asr, air traffic control, atc

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Nadia Ben Youssef, Aicha Bouzid

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Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach.

Keywords: gradient, edge detection, color image, quaternion

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Authors: Ainagul Yermekova, Liudmila Goncharenko, Ali Baghirzade, Sergey Sybachin

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In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc.

Keywords: text detection, template method, recognition algorithm, structured method, feature method

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Authors: Alan S. Hoback

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Most people see human faces in car front and back ends because of the process of pareidolia. 96 people were surveyed to see how many of them saw a face in the vehicle styling. Participants were aged 18 to 72 years. 94% of the participants saw faces in the front-end design of production models. All participants that recognized faces indicated that most styles showed some degree of an angry expression. It was found that women were more likely to see faces in inanimate objects. However, with respect to whether women were more likely to perceive anger in the vehicle design, the results need further clarification. Survey responses were correlated to the design features of vehicles to determine what cues the respondents were likely looking at when responding. Whether the features looked anthropomorphic was key to anger perception. Features such as the headlights which could represent eyes and the air intake that could represent a mouth had high correlations to trends in scores. Results are compared among models, makers, by groupings of body styles classifications for the top 12 brands sold in the US, and by year for the top 20 models sold in the US in 2016. All of the top models sold increased in perception of an angry expression over the last 20 years or since the model was introduced, but the relative change varied by body style grouping.

Keywords: aggressive driving, face recognition, road rage, vehicle styling

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Authors: Gheida J. Shahrour, Martin J. Russell

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The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively.

Keywords: person recognition, topic recognition, culture recognition, 3D body movement signals, variability compensation

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

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Authors: S. Malarvizhi, S. Sadiq Basha, M. Santhosh Kumar, K. Saravanan, R. Sasikumar, R. Satheesh

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This is a web based application which provides authorization for the vehicle identification and registration. It also provides mutual authentication between the police and users in order to avoid misusage. The QR code generation in this application overcomes the difficulty in the manual registration of the vehicle documents. This generated QR code is placed in the number plates of the vehicles. The QR code is scanned using the QR Reader installed in the smart devices. The police officials can check the vehicle details and file cases on accidents, theft and traffic rules violations using QR code. In addition to vehicle insurance payments and renewals, the renewal alert is sent to the vehicle owner about payment deadline. The non-permitted vehicles can be blocked in the next check-post by sending the alert messages.

Keywords: QR code, QR reader, registration, authentication, idenfication

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Authors: Müjde Erol Genevois, Hatice Kocaman

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Growing concerns about the increasing consumption of fossil energy and the improved recognition of environmental protection require sustainable road transportation technology. Electric vehicles (EVs) can contribute to improve environmental sustainability and to solve the energy problem with the right infrastructure. The problem of where to locate electric vehicle charging station can be grouped as decision-making problems because of including many criteria and alternatives that have to be considered simultaneously. The purpose of this paper is to present an integrated AHP and TOPSIS model to rank the optimal sites of EVs charging station in Istanbul, Turkey. Ten different candidate points and three decision criteria are identified. The performances of each candidate points with respect to criteria are obtained according to AHP calculations. These performances are used as an input for TOPSIS method to rank the candidate points. It is obtained accurate and robust results by integrating AHP and TOPSIS methods.

Keywords: electric vehicle charging station (EVCS), AHP, TOPSIS, location selection

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Authors: Tuğrul Aksoy, Hüseyin Karabıyık

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Reloading vehicles are the vehicles which are generally equipped with a crane and used to carry a stowage from a point and locate onto the vehicle or vice versa. In this study, structural analysis of a reloading vehicle was performed under the loads which are predicted to be exposed under operating conditions via the finite element method. Among the finite element analysis results, the stress and displacement distributions of the vehicle and the contact pressure distributions of the guide rings within the stabilization legs were examined. Vehicle design was improved by strengthening certain parts according to the analysis results. The analyses performed for the final design were verified by the experiments involving strain gauge measurements.

Keywords: structural analysis, reloading vehicle, crane, strain gauge

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Authors: Mahwish Ghulam Rasool

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The design of a space is a complex process that involves multiple stages, from conceptualization, identifying design problems to understanding the context, materiality, and functionality of the space. Out of all the design elements, color is one of the most dominant and expressive factors that affect the spatial dynamics of the interior space. Color affects aesthetic comfort in space and has a lasting impact on human perception and psychology. Using color as a tool for creating spatial experiences is a new paradigm. Color semantics in spaces are not only used for surface treatment or aesthetics, but it also has more powerful functional characteristics. As interior spaces are evolving and becoming experiential with each decade, designers are looking for new processes to enhance the spatial and experiential quality of interior spaces. The relationship between color and interior typologies is a relatively new paradigm. This paper discusses the role of colors in interior spaces from various perspectives, exploring their impact on the formation of interior typologies and the use of colors in space design. The paper analyzes interior typologies worldwide, from residential to commercial interior spaces, where color semantics plays a prominent role in the design. The paper also emphasizes the design process and the creation of design language, unveiling the possibilities of applying colors in interior spaces that can be in harmony with the building context, space functionality, or in opposition to the existing building envelope or environment. The paper aims to contribute to the field of interior design education and practices. By using experimental and various research methodologies for investigation, it aims to fill the gap in the literature regarding color semantics and the relationship between interior typologies.

Keywords: color psychology, color semantics, interior environments, interior typologies

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Authors: Ahmed Emad, Ahmed Salah, Abdelrahman Magdy, Omar Rashid, Mohammed Adel

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This research paper discusses vehicle-to-vehicle technology as an important application of linear algebra. This communication technology represents an efficient and promising application to help to ensure the safety of the drivers by warning them when a crash possibility is close. The major link that combines our topic with linear algebra is the Laplacian matrix. Some main definitions used in the V2V were illustrated, such as VANET and its characteristics. The V2V technology could be applied in different applications with different traffic scenarios and various ways to warn car drivers. These scenarios were simulated programs such as MATLAB and Python to test how the V2V system would respond to the different scenarios and warn the car drivers exposed to the threat of collisions.

Keywords: V2V communication, vehicle to vehicle scenarios, VANET, FCW, EEBL, IMA, Laplacian matrix

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: Rameez Ahmad, Aqib Mehmood, Imran Khan

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Self-directed car (SDC) that can drive itself from one fact to another without support from a driver. Certain trust that self-directed car obligate the probable to transform the transportation manufacturing while essentially removing coincidences, and cleaning up the environment. This study realizes the effects that SDC (also called a self-driving, driver or robotic) vehicle travel demands and ride scheme is likely to have. Without the typical obstacles that allows detection of a audio vision based hardware and software construction (It (SDC) and cost benefits, the vehicle technologies, Gold (Generic Obstacle and Lane Detection) to a knowledge-based system to predict their potential and consider the shape, color, or balance) and an organized environment with colored lane patterns, lane position ban. Discovery the problematic consequence of (SDC) on GT (grand trunk road) road and brand the car further effectual.

Keywords: SDC, gold, GT, knowledge-based system

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Authors: Darshna Sharma, Suban K. Sahoo

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The detection of intracellular fluoride in human cancer cell HeLa was achieved by chemosensors derived from vitamin B6 cofactors using fluorescence imaging technique. These sensors were first synthesized by condensation of pyridoxal/pyridoxal phosphate with 2-amino(thio)phenol. The anion recognition ability was explored by experimental (UV-VIS, fluorescence and 1H NMR) and theoretical DFT [(B3LYP/6-31G(d,p)] methods in DMSO and mixed DMSO-H2O system. All the developed sensors showed both naked-eye detectable color change and remarkable fluorescence enhancement in the presence of F- and AcO-. The anion recognition was occurred through the formation of hydrogen bonded complexes between these anions and sensor, followed by the partial deprotonation of sensor. The detection limit of these sensors were down to micro(nano) molar level of F- and AcO-.

Keywords: chemosensors, fluoride, acetate, turn-on, live cells imaging, DFT

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Authors: Preeti Yadav, Dandeswar Bisoyi, Debkumar Chakrabati

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For many years organization have been exercising a creative technique of applying brand mascots, which results in making a visual ‘ambassador’ of a brand. The goal of mascot’s is just not confined to strengthening the brand identity, improving customer perception, but also acting as a vehicle of anthropomorphic translation towards the consumer. Such that it helps in embracing the power of recognition and processing the experiences happening in our daily lives. The study examines the relationship between the specific mascot features and brand attitude. It eliminates that mascot trust is an important mediator of the mascot features on brand attitude. Anthropomorphic characters turn out to be the key players despite the application of brand mascots in today’s marketing.

Keywords: advertising, mascot, branding, recall

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Authors: Sevil Igit, Merve Meric, Sarp Erturk

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In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach.

Keywords: face recognition, Daisy descriptor, One-Bit Transform, image registration

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Authors: Tanda Soala Gogo Sirait

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The existence of motor vehicle in Indonesia has a very essential role for daily mobility in urban areas. The motor vehicle market is growing tremendously particularly for the motorcycle. The growth of motorcycles ownership was 15 % annually from 2002 to 2012. It is predicted that the number of motorcycles still continue to grow in the future. The increase of motor vehicle has led to the creation of serious urban problems and Indonesian economy structure. Today, Indonesia's cities are well known as the jungle of motorcycle world. The volume of motor vehicle traffic in Indonesia cities is astounding, which more than 55% of motorised transport is on motorcycle and car. The reason it happens is due lack and inconvenient of the public transportation in Indonesia. Motor vehicle ownership by individual households tends to rising rapidly. Indonesian government still does not have a clear direction how to face this phenomenon.

Keywords: Indonesian economy, inconvenient public transportation, motor vehicle growth, urban problems

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Authors: Hassam Muazzam

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This paper devises an autonomous self-driven vehicle that is capable of taking a disabled person to his/her desired location using three different power sources (gasoline, solar, electric) without any control from the user, avoiding the obstacles in the way. The GPS co-ordinates of the desired location are sent to the main processing board via a GSM module. After the GPS co-ordinates are sent, the path to be followed by the vehicle is devised by Pythagoras theorem. The distance and angle between the present location and the desired location is calculated and then the vehicle starts moving in the desired direction. Meanwhile real-time data from ultrasonic sensors is fed to the board for obstacle avoidance mechanism. Ultrasonic sensors are used to quantify the distance of the vehicle from the object. The distance and position of the object is then used to make decisions regarding the direction of vehicle in order to avoid the obstacles using artificial neural network which is implemented using ATmega1280. Also the vehicle provides the feedback location at remote location.

Keywords: autonomous self-driven vehicle, obstacle avoidance, desired location, pythagoras theorem, neural network, remote location

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