Search results for: AI-driven vehicle recognition

Authors: Michael Mickson

Abstract:

The recent development of unmanned aerial vehicles (UAVs) has been increasing the number of technical solutions that can be used to identify, map, and manage the effects of geohazards. UAVs are generally cheaper and more versatile than traditional remote-sensing techniques, and they can be therefore considered as a good alternative for the acquisition of imagery and other remote sensing data before, during and after a natural hazard event. This study aims to use UAV for investigating areas susceptible to high mobility flows such as debris flow in Dominica, especially after the 2017 Hurricane Maria. The use of UAVs in identifying, mapping and managing of natural hazards helps to mitigate the negative effects of natural hazards on livelihood, properties and the built environment.

Keywords: unmanned aerial vehicle (UAV), geohazards, remote sensing, mapping, Dominica

Procedia PDF Downloads 134

Authors: Hee-Choul Kwon, Heeyong Kwon

Abstract:

Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

Procedia PDF Downloads 281

Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

Abstract:

This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

Procedia PDF Downloads 89

Authors: Ng Liang Shen, Hau Yuan Wen

Abstract:

Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

Procedia PDF Downloads 377

Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

Abstract:

We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models for Generative Named Entity Recognition (GNER). Alongside Retrieval Augmented Generation (RAG), and supported by task-specific prompting, our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self-verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

Procedia PDF Downloads 58

Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

Abstract:

We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

Procedia PDF Downloads 135

Authors: Xin Jiao, Xin Zhang, Jian Fan, Zhenwei Cai, Yiming Xu

Abstract:

Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents an innovative approach to tower crane inspection utilizing Unmanned Aerial Vehicles (UAVs) and an Intelligent Inspection APP System. The system leverages UAVs equipped with high-definition cameras to conduct efficient and comprehensive inspections, reducing manual labor, inspection time, and risk. By integrating advanced technologies such as Real-Time Kinematic (RTK) positioning and digital image processing, the system enables precise route planning and collection of safety hazards images. A case study conducted on a construction site demonstrates the practicality and effectiveness of the proposed method, showcasing its potential to enhance tower crane safety. On-site testing of UAV intelligent inspections reveals key findings: efficient tower crane hazard inspection within 30 minutes, with a full-identification capability coverage rates of 76.3%, 64.8%, and 76.2% for major, significant, and general hazards respectively and a preliminary-identification capability coverage rates of 18.5%, 27.2%, and 19%, respectively. Notably, UAVs effectively identify various tower crane hazards, except for those requiring auditory detection. The limitations of this study primarily involve two aspects: Firstly, during the initial inspection, manual drone piloting is required for marking tower crane points, followed by automated flight inspections and reuse based on the marked route. Secondly, images captured by the drone necessitate manual identification and review, which can be time-consuming for equipment management personnel, particularly when dealing with a large volume of images. Subsequent research efforts will focus on AI training and recognition of safety hazard images, as well as the automatic generation of inspection reports and corrective management based on recognition results. The ongoing development in this area is currently in progress, and outcomes will be released at an appropriate time.

Keywords: tower crane, inspection, unmanned aerial vehicle (UAV), intelligent inspection app system, safety management

Procedia PDF Downloads 46

Authors: Fatima Muhammad Shitu

Abstract:

This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied.

Keywords: keyword, lexical, semantics, strategy

Procedia PDF Downloads 320

Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

Abstract:

Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

Procedia PDF Downloads 175

Authors: M. Tan, N. N. Wan, N. Ramli, N. H. Hassan

Abstract:

Car drivers would always like to have custom wheel on their car for two reasons; to improve their car's aesthetic beauty and to improve their car handling. As the size of the rims or wheels played an important role in influencing the way of car handles around turns, this paper aims to present the optimality of rim size that drivers should have known while changing their rim. There are three factors that drivers should have considered while changing their rim: rim size, its weight and material of which they are made. Using mathematical analysis, this paper will focus on only one factor, which is rim size. Factors that are considered in calculating the optimum rim size are the vehicle rim radius, tire height and weight, and aspect ratio. This paper has found that there are limitations in percentage change in rim size from the original tire size. Failure to have the right offset size may cause problems in maneuvering the vehicle.

Keywords: mathematical analysis, optimum wheel size, percentage change, custom wheel

Procedia PDF Downloads 497

Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

Abstract:

In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

Procedia PDF Downloads 374

Authors: Mahshid Arabi

Abstract:

In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

Procedia PDF Downloads 52

Authors: Tamir Michal

Abstract:

The subject of Bedouin dispersion has accompanied the State of Israel from the day of its establishment. From a legal point of view, this subject has offered a launchpad for creative judicial decisions. Thus, for example, the first court decision in Israel to recognize affirmative action (Avitan), dealt with a petition submitted by a Jew appealing the refusal of the State to recognize the Petitioner’s entitlement to the long-term lease of a plot designated for Bedouins. The Supreme Court dismissed the petition, holding that there existed a public interest in assisting Bedouin to establish permanent urban settlements, an interest which justifies giving them preference by selling them plots at subsidized prices. In another case (The Forum for Coexistence in the Negev) the Supreme Court extended equitable relief for the purpose of constructing a bridge, even though the construction infringed the Law, in order to allow the children of dispersed Bedouin to reach school. Against this background, the recent verdict, delivered during the Protective Edge military campaign, which dismissed a petition aimed at forcing the State to spread out Protective Structures in Bedouin villages in the Negev against the risk of being hit from missiles launched from Gaza (Abu Afash) is disappointing. Even if, in arguendo, no selective discrimination was involved in the State’s decision not to provide such protection, the decision, and its affirmation by the Court, is problematic when examined through the prism of the Theory of Recognition. The article analyses the issue by tools of theory of Recognition, according to which people develop their identities through mutual relations of recognition in different fields. In the social context, the path to recognition is cognitive respect, which is provided by means of legal rights. By seeing other participants in Society as bearers of rights and obligations, the individual develops an understanding of his legal condition as reflected in the attitude to others. Consequently, even if the Court’s decision may be justified on strict legal grounds, the fact that Jewish settlements were protected during the military operation, whereas Bedouin villages were not, is a setback in the struggle to make the Bedouin citizens with equal rights in Israeli society. As the Court held, ‘Beyond their protective function, the Migunit [Protective Structures] may make a moral and psychological contribution that should not be undervalued’. This contribution is one that the Bedouin did not receive in the Abu Afash verdict. The basic thesis is that the Court’s verdict analyzed above clearly demonstrates that the reliance on classical liberal instruments (e.g., equality) cannot secure full appreciation of all aspects of Bedouin life, and hence it can in fact prejudice them. Therefore, elements of the recognition theory should be added, in order to find the channel for cognitive dignity, thereby advancing the Bedouins’ ability to perceive themselves as equal human beings in the Israeli society.

Keywords: bedouin dispersion, cognitive respect, recognition theory, sustainable development

Procedia PDF Downloads 355

Authors: Emilienne Lardy, Eric Ballot, Mariam Lafkihi

Abstract:

Efficient urban logistics requires a comprehensive understanding of traffic dynamics, particularly as it pertains to kinetic parameters influencing energy consumption and trip duration estimations. While real-time traffic information is increasingly accessible, current high-precision forecasting services embedded in route planning often function as opaque 'black boxes' for users. These services, typically relying on AI-processed counting data, fall short in accommodating open design parameters essential for management studies, notably within Supply Chain Management. This work revisits the modelling of traffic conditions in the context of city logistics, emphasizing its significance from the user’s point of view, with two focuses. Firstly, the focus is not on the vehicle flow but on the vehicles themselves and the impact of the traffic conditions on their driving behaviour. This means opening the range of studied indicators beyond vehicle speed, to describe extensively the kinetic and dynamic aspects of the driving behaviour. To achieve this, we leverage the Art. Kinema parameters are designed to characterize driving cycles. Secondly, this study examines how the driving context (i.e., exogenous factors to the traffic flow) determines the mentioned driving behaviour. Specifically, we explore how accurately the kinetic behaviour of a vehicle can be predicted based on a limited set of exogenous factors, such as time, day, road type, orientation, slope, and weather conditions. To answer this question, statistical analysis was conducted on real-world driving data, which includes high-frequency measurements of vehicle speed. A Factor Analysis and a Generalized Linear Model have been established to link kinetic parameters with independent categorical contextual variables. The results include an assessment of the adjustment quality and the robustness of the models, as well as an overview of the model’s outputs.

Keywords: factor analysis, generalised linear model, real world driving data, traffic congestion, urban logistics, vehicle kinematics

Procedia PDF Downloads 72

Authors: Wlodzimierz Ogryczak, Tomasz Sliwinski, Jaroslaw Hurkala, Mariusz Kaleta, Bartosz Kozlowski, Piotr Palka

Abstract:

Implementation concepts of a decision support system for planning and management of mobile personnel tasks (sales representatives and others) are discussed. Large-scale periodic time-dependent vehicle routing and scheduling problems with complex constraints are solved for this purpose. Complex nonuniform constraints with respect to frequency, time windows, working time, etc. are taken into account with additional fast adaptive procedures for operational rescheduling of plans in the presence of various disturbances. Five individual solution quality indicators with respect to a single personnel person are considered. This paper deals with modeling issues corresponding to the problem and general solution concepts. The research was supported by the European Union through the European Regional Development Fund under the Operational Programme ‘Innovative Economy’ for the years 2007-2013; Priority 1 Research and development of modern technologies under the project POIG.01.03.01-14-076/12: 'Decision Support System for Large-Scale Periodic Vehicle Routing and Scheduling Problems with Complex Constraints.'

Keywords: mobile personnel management, multiple criteria, time dependent, time windows, vehicle routing and scheduling

Procedia PDF Downloads 326

Authors: Rameez Ahmad, Aqib Mehmood, Imran Khan

Abstract:

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

Procedia PDF Downloads 376

Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

Abstract:

The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

Procedia PDF Downloads 49

Authors: T. Thein, S. Kalyar Myo

Abstract:

Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

Procedia PDF Downloads 286

Authors: Igor Astrov, Natalya Berezovski

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMA-L2 neurocontroller for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: NARMA-L2 neurocontroller, situational awareness, vectored thrust aerial vehicle, aviation

Procedia PDF Downloads 425

Authors: Barrere Sarah

Abstract:

The area of liberty, security, and justice within the European Union is still a work in progress. No one can deny that the EU struggles between a monistic and a dualist approach. The aim of our essay is to first review how the European law is perceived by the rest of the international scene. It will then discuss two main mechanisms at play: the interpretation of larger international treaties and the penal mechanisms of European law. Finally, it will help us understand the role of a penal Europe on the international scene with concrete examples. Special attention will be paid to cases that deal with fundamental rights as they represent an interesting case study in Europe and in the rest of the World. It could illustrate the aforementioned duality currently present in the Union’s interpretation of international public law. On the other hand, it will explore some specific European penal mechanism through mutual recognition and the European arrest warrant in the transnational criminality frame. Concerning the interpretation of the treaties, it will first, underline the ambiguity and the general nature of some treaties that leave the EU exposed to tension and misunderstanding then it will review the validity of an EU act (whether or not it is compatible with the rules of International law). Finally, it will focus on the most complete manifestation of liberty, security and justice through the principle of mutual recognition. Used initially in commercial matters, it has become “the cornerstone” of European construction. It will see how it is applied in judicial decisions (its main event and achieving success is via the European arrest warrant) and how European member states have managed to develop this cooperation.

Keywords: European penal law, international scene, liberty security and justice area, mutual recognition

Procedia PDF Downloads 411

Authors: Çağrı Bekir Baysal, Göktuğ Burak Çalık

Abstract:

Armored vehicles may have different traction layouts as a result of terrain capabilities and mobility needs. Two main categories of layouts can be separated as wheeled and tracked. Tracked vehicles have superior off-road capabilities but what they gain on terrain performance they lose on mobility front. Wheeled vehicles on the other hand do not have as good terrain capabilities as tracked vehicles but they have superior mobility capabilities such as top speed, range and agility with respect to tracked vehicles. Conventional armored vehicles employ a diesel ICE as main power source. In these vehicles ICE is mechanically connected to the powertrain. This determines the ICE rpm as a result of speed and torque requested by the driver. ICE efficiency changes drastically with torque and speed required and conventional vehicles suffer in terms of fuel consumption because of this. Hybrid electric vehicles employ at least one electric motor in order to improve fuel efficiency. There are different types of hybrid vehicles but main types are Series Hybrid, Parallel Hybrid and Series-Parallel Hybrid. These vehicles introduce an electric motor for traction and also can have a generator electric motor for range extending purposes. Having an electric motor as the traction power source brings the flexibility of either using the ICE as an alternative traction source while it is in efficient range or completely separating the ICE from traction and using it solely considering efficiency. Hybrid configurations have additional advantages for armored vehicles in addition to fuel efficiency. Heat signature, silent operation and prolonged stationary missions can be possible with the help of the high-power battery pack that will be present in the vehicle for hybrid drivetrain. Because of the reasons explained, hybrid armored vehicles are becoming a target area for military and also for vehicle suppliers. In order to have a better idea and starting point when starting a hybrid armored vehicle design, hybrid drivetrain configuration has to be selected after performing a trade-off study. This study has to include vehicle mobility simulations, integration level, vehicle level and performance level criteria. In this study different hybrid traction configurations possible for an 8x8 vehicle is compared using above mentioned criteria set. In order to compare hybrid traction configurations ease of application, cost, weight advantage, reliability, maintainability, redundancy and performance criteria have been used. Performance criteria points have been defined with the help of vehicle simulations and tests. Results of these simulations and tests also help determining required tractive power for an armored vehicle including conditions like trench and obstacle crossing, gradient climb. With the method explained in this study, each configuration is assigned a point for each criterion. This way, correct configuration can be selected objectively for every application. Also, key aspects of armored vehicles, mine protection and ballistic protection will be considered for hybrid configurations. Results are expected to vary for different types of vehicles but it is observed that having longitudinal differential locking capability improves mobility and having high motor count increases complexity in general.

Keywords: armored vehicles, electric drivetrain, electric mobility, hybrid vehicles

Procedia PDF Downloads 88

Authors: Ons Sassi, Wahiba Ramdane Cherif-Khettaf, Ammar Oulamara

Abstract:

In this paper, we consider a new real-life Heterogenous Electric Vehicle Routing Problem with Time Dependant Charging Costs and a Mixed Fleet (HEVRP-TDMF), in which a set of geographically scattered customers have to be served by a mixed fleet of vehicles composed of a heterogenous fleet of Electric Vehicles (EVs), having different battery capacities and operating costs, and Conventional Vehicles (CVs). We include the possibility of charging EVs in the available charging stations during the routes in order to serve all customers. Each charging station offers charging service with a known technology of chargers and time-dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are not necessarily compatible with all available charging technologies and a partial charging is allowed. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and then minimize the total travel and charging costs. In this study, we present a Mixed Integer Programming Model and develop a Charging Routing Heuristic and a Local Search Heuristic based on the Inject-Eject routine with three different insertion strategies. All heuristics are tested on real data instances.

Keywords: charging problem, electric vehicle, heuristics, local search, optimization, routing problem

Procedia PDF Downloads 465

Authors: S. Ali, R. Yunus, A. Arif, Y. Ayaz, M. Baber Sial, R. Asif, N. Naseer, M. Jawad Khan

Abstract:

In this study, we have proposed a gesture to emotion recognition method using flex sensors mounted on metacarpophalangeal joints. The flex sensors are fixed in a wearable glove. The data from the glove are sent to PC using Wi-Fi. Four gestures: finger pointing, thumbs up, fist open and fist close are performed by five subjects. Each gesture is categorized into sad, happy, and excited class based on the velocity and acceleration of the hand gesture. Seventeen inspectors observed the emotions and hand gestures of the five subjects. The emotional state based on the investigators assessment and acquired movement speed data is compared. Overall, we achieved 77% accurate results. Therefore, the proposed design can be used for emotional state detection applications.

Keywords: emotion identification, emotion models, gesture recognition, user perception

Procedia PDF Downloads 289

Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

Abstract:

In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

Procedia PDF Downloads 353

Authors: Amanuel Berhanu Kesamo

Abstract:

This paper presents both circuit simulation and hardware implementation of a robot vehicle that can be either controlled manually via Bluetooth with video streaming or navigate autonomously to a target point by avoiding obstacles. In manual mode, the user controls the mobile robot using C# windows form interfaced via Bluetooth. The camera mounted on the robot is used to capture and send the real time video to the user. In autonomous mode, the robot plans the shortest path to the target point while avoiding obstacles along the way. Ultrasonic sensor is used for sensing the obstacle in its environment. An efficient path planning algorithm is implemented to navigate the robot along optimal route.

Keywords: Arduino Uno, autonomous, Bluetooth module, path planning, remote controlled robot, ultra sonic sensor

Procedia PDF Downloads 148

Authors: Ivana Cestar, Ivica Stančerić, Saša Ahac, Vesna Dragčević, Tamara Džambas

Abstract:

On special paved areas, such as road intersections, vehicles are usually moving through horizontal curves with smaller radii and occupy considerably greater area compared to open road segments. Planning procedure of these areas is mainly an iterative process that consists of designing project elements, assembling those elements to a design project, and analyzing swept paths for the design vehicle. If applied elements do not fulfill the swept path requirements for the design vehicle, the process must be carried out again. Application of specialized computer software for swept path analysis significantly facilitates planning procedure of special paved areas. There are various software of this kind available on the global market, and each of them has different specifications. In this paper, comparison of two software commonly used in Croatia (Auto TURN and Vehicle Tracking) is presented, their advantages and disadvantages are described, and their applicability on a particular paved area is discussed. In order to reveal which one of the analyszed software is more favorable in terms of swept paths widths, which one includes input parameters that are more relevant for this kind of analysis, and which one is more suitable for the application on a certain special paved area, the analysis shown in this paper was conducted on a number of different intersection types.

Keywords: software comparison, special paved areas, swept path analysis, swept path input parameters

Procedia PDF Downloads 324

Authors: Orhun Vural, Oguz Bayat, Rustu Akay, Osman N. Ucan

Abstract:

This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms.

Keywords: parallel k-means algorithm, parallel clustering, clustering algorithms, clustering on flowing data

Procedia PDF Downloads 227

Authors: Norbert Szulc, Jakub Wilk, Franciszek Górski

Abstract:

This paper presents an approach for simulating and testing robotic systems based on PX4, using a local Kubernetes cluster. The approach leverages modern cloud-native tools and runs on single-board computers. Additionally, this solution enables the creation of datasets for computer vision and the evaluation of control system algorithms in an end-to-end manner. This paper compares this approach to method commonly used Docker based approach. This approach was used to develop simulation environment for an unmanned surface vehicle (USV) for RoboBoat 2023 by running a containerized configuration of the PX4 Open-source Autopilot connected to ROS and the Gazebo simulation environment.

Keywords: cloud computing, Kubernetes, single board computers, simulation, ROS

Procedia PDF Downloads 82

Authors: Pedro F. Albuquerque, Pedro V. Gamboa, Miguel A. Silvestre

Abstract:

The present work describes the implementation of the Enhanced Collaborative Optimization (ECO) multilevel architecture with a gradient-based optimization algorithm with the aim of performing a multidisciplinary design optimization of a generic unmanned aerial vehicle with morphing technologies. The concepts of weighting coefficient and a dynamic compatibility parameter are presented for the ECO architecture. A routine that calculates the aircraft performance for the user defined mission profile and vehicle’s performance requirements has been implemented using low fidelity models for the aerodynamics, stability, propulsion, weight, balance and flight performance. A benchmarking case study for evaluating the advantage of using a variable span wing within the optimization methodology developed is presented.

Keywords: multidisciplinary, multilevel, morphing, enhanced collaborative optimization

Procedia PDF Downloads 934

Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

Abstract:

Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

Procedia PDF Downloads 578