This paper proposes a distributed model predictive control (DMPC) strategy with known delays in the communication network. The algorithm is suitable for a vehicle platooning application in the cooperative adaptive cru...
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ISBN:
(数字)9781728157429
ISBN:
(纸本)9781728157436
This paper proposes a distributed model predictive control (DMPC) strategy with known delays in the communication network. The algorithm is suitable for a vehicle platooning application in the cooperative adaptive cruise control (CACC) framework. All the platoon vehicles are connected in a uni-directional wireless communication network. The simulation results for a five-vehicle platoon show that the DMPC optimization problem can successfully accommodate for the communication delays, while a velocity-dependent inter-vehicle spacing-policy for the follower vehicles is used.
Edge detection is an important problem in image processing. This was extensively studied in recent years. In this paper artificial feedforward neural networks are used for identifying edges in gray-scale images. Super...
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Edge detection is an important problem in image processing. This was extensively studied in recent years. In this paper artificial feedforward neural networks are used for identifying edges in gray-scale images. Supervised learning based on the gradient descent algorithm is used. A new method is proposed for neural network training patterns using fuzzy concepts. Fuzzy membership functions are used for improving the generalization capability of neural network. Edge detection in noisy images without applying a noise removal technique poses a difficult problem. The proposed method obtained successful experimental results for digital images. The advantage of our approach is that we use the trained neural network as a filter on both noisy and noise free images.
The authors propose a new optimal method for the implementation of an integer division sequential algorithm using multifunctional registers (MFR) with decoded control inputs, based on transfer matrix method. The imple...
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The authors propose a new optimal method for the implementation of an integer division sequential algorithm using multifunctional registers (MFR) with decoded control inputs, based on transfer matrix method. The implementation cost is calculated emphasizing the most economical solutions. Low cost means less power consumed - green architectures, the CPU FPU logic core is much faster and the responses timing are short. The modern design tools handle digital systems with many outputs and represent them by cubes, for efficiency reasons. Talking as optimal, the implementation of the digital automaton can be reduced to a combinatorial one: synthesis using logic gates primitives and using floor planning design. The digital logic network that generates the control signals of the Finite State Machine (FSM) can be synthesized using the transfer matrix.
Networked embedded systems using event triggered communication are widely used in industry. While the bus load increases, the transmission of the lower priority messages may experience variable delays (jitter) or even...
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ISBN:
(纸本)9781509027217
Networked embedded systems using event triggered communication are widely used in industry. While the bus load increases, the transmission of the lower priority messages may experience variable delays (jitter) or even may be unable to be transmitted. Even at maximum load, the transmission of such messages has to be guaranteed and the delivery time has to be determined with high precision. CANopen is an industrial event-driven network based on controller Area Network (CAN) protocol at the physical and data link communication levels. A time-triggered software extension of CANopen based on Time-Triggered CAN is described in this paper, as well as its implementation on an embedded networked system based on microcontrollers.
Byzantine music represents a vast and complex musical tradition which precedes contemporary Western music. Nowadays, it is used mostly within Eastern Christian churches. Few computer software applications deal with By...
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Byzantine music represents a vast and complex musical tradition which precedes contemporary Western music. Nowadays, it is used mostly within Eastern Christian churches. Few computer software applications deal with Byzantine music and the imperfect command of Byzantine music theory among singers often requires parallel classical notation staves. A software application written in C++ has been written, which enables the user to write simple musical scores using standard notation, as well as Byzantine music compositions using a subset of the specific Byzantine notation. The application can be used to produce transcriptions of Byzantine-notation compositions into their classical notation counterparts. Extensive data structures and algorithms for containing and manipulating the data, as well as interacting with the computer user, have been developed. Additionally, the application offers some serialization capabilities, as well as preliminary support for pitch tracking. By offering the functionality described above, the application is an example of modern technology being used to preserve cultural heritage.
This paper describes a novel image steganography algorithm, with the central idea being the use of two hidden thresholds. By implementing the described method, various types of information (sub-images, numbers, symbol...
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ISBN:
(数字)9781728198095
ISBN:
(纸本)9781728198101
This paper describes a novel image steganography algorithm, with the central idea being the use of two hidden thresholds. By implementing the described method, various types of information (sub-images, numbers, symbols) can be hidden inside a usual image. The article presents how to apply this procedure for a gray image: after recovering the two threshold values, hidden inside the least significant bit of each of the first 16 pixels on the edges, an image can be processed by comparing the gray level to the threshold levels (making sure the gray level is between the thresholds) and thus reveal the secret message. The algorithm can be extended to different types of data and image representations and could also suggest another method of embedding metadata inside an image, such as the time of download, the original website from where it was downloaded or information on its author, etc.
Enterprise control applications are becoming more and more complex, exploiting distributed object technology in multi-tier architectures. For educational purposes, the development of a manufacturing control laboratory...
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Enterprise control applications are becoming more and more complex, exploiting distributed object technology in multi-tier architectures. For educational purposes, the development of a manufacturing control laboratory that may emphasize the new control techniques and the overall integration of the system activities is a complex and challenging task. In the paper, an enterprise application solution that integrates the ERP concept in three- tier architecture with a flexible manufacturing system is presented. It contains modularized, distributed subsystems in configurable and maintainable software. Starting from the customer order, to planned order dispatch, the ERP main components and the information flow within the system is presented. The software architecture contains the ERP components that are implemented using enterprise Java beans, deployed in a J2EE application server. It enables transactions with the business environment, shop-floor system and the database. The application is conceptually designed and analyzed using a visual model developed in Unified Modeling Language.
The paper presents a symmetrical T-network utilizing fractional order elements. Fractional calculus was used for its modeling. A comparative analysis of a classic T-network and fractional order T-network step response...
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The paper presents a symmetrical T-network utilizing fractional order elements. Fractional calculus was used for its modeling. A comparative analysis of a classic T-network and fractional order T-network step responses was carried out for both no-load condition and short-circuit condition.
Recent advancements in artificial intelligence have led to significant results in various domains, including image classification, natural language processing, and mastering complex games. However, current deep neural...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
Recent advancements in artificial intelligence have led to significant results in various domains, including image classification, natural language processing, and mastering complex games. However, current deep neural networks seem to process information differently from humans. Neuro-symbolic methods may offer a promising solution to address this concern. This paper proposes a preliminary cognitive architecture focused on neural cell assemblies, which can combine the adaptability of neural approaches with the explicit reasoning capabilities of symbolic systems. It presents a case study on learning to count, and highlights mechanisms for learning, generalization, and adaptation based on predictive errors.
Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast canc...
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ISBN:
(数字)9798331532147
ISBN:
(纸本)9798331532154
Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.
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