Material-integrated Intelligent Systems: A Review on State of the Art, Challenges and Trends

2015 ◽  
Author(s):  
Dirk Lehmhus ◽  
Stefan Bosse
Keyword(s):  
Intelligent Systems ◽  
State Of The Art ◽  
Strong Material ◽  
Integrated Intelligent Systems

More on Brain and Semiosis: Can We Find a Point in Neuronets?

2021 ◽  
pp. 5-13
Author(s):  
Tatiana V. Chernigovskaya ◽  
Keyword(s):  
Language Processing ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Similar Data ◽  
Direct Testing ◽  
The Subject ◽  
Neurophysiological Basis ◽  
Person Experience ◽  
Cognitive Dimensions ◽  
Human Specific

The paper discusses semiotic aspects of higher human functions and a possibility and relevance of traditional search for their neurophysiological basis. The state of the art on the subject is reviewed and the lack of data on anthropological specificity for reasoning, thinking, language and its AI modeling is highlighted. Experimental neuroscience presumes that if we know the characteristics of neu­rons and their connections, we automatically understand what mind and con­sciousness are. However, it is evident that such a paradigm does not allow us to get relevant answers to the main questions. I argue that the problem should be dealt with not only within the field of neurophysiology proper. Rather, such re­search should involve exploring the 'archeology' of mental processes as they are revealed in arts as well as in other symbolic spaces. The paper discusses the ade­quacy of physiological methodology when it is employed to demonstrate brain mechanisms of higher functions. Besides, I explore the relevance of juxta­posing similar data from other biological and artificial intelligent systems. I view language processing, mind and reasoning and 1st person experience (qualia) as human specific features, and questions the possibility of direct testing these phenomena. The paper links genetic, anthropological and neurophysio­logical data to semiotic activity and semiosphere formation as the basis for com­munication. The paper discusses the place of humans in the changing world in the context of new cognitive dimensions.

Modeling and simulation of integrated intelligent systems

2008 ◽  
Author(s):  
Yongchang Li ◽  
Michael Balchanos ◽  
Bassem Nairouz ◽  
Neil Weston ◽  
Dimitri Mavris
Keyword(s):  
Modeling And Simulation ◽  
Intelligent Systems ◽  
Integrated Intelligent Systems

scholarly journals Developing a Powerful and Resilient Smart Body Sensor Network through Hypercube Interconnection

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ahmad S. Almogren
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Intelligent Systems ◽  
Intelligent Design ◽  
State Of The Art ◽  
The Body ◽  
Sensor Nodes ◽  
Body Sensor Networks ◽  
Embedded Computing ◽  
Body Sensor Network

With recent advances in wireless sensor networks and embedded computing technologies, body sensor networks (BSNs) have become practically feasible. BSNs consist of a number of sensor nodes located and deployed over the human body. These sensors continuously gather vital sign data of the body area to be used in various intelligent systems in smart environments. This paper presents an intelligent design of the body sensor network based on virtual hypercube structure backbone termed as Smart BodyNet. The main purpose of the Smart BodyNet is to provide resilience for the BSN operation and reduce power consumption. Various experiments were carried out to show the performance of the Smart BodyNet design as compared to the state-of-the-art approaches.

scholarly journals Special Issue on Intelligent Systems in Sensor Networks and Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3182
Author(s):  
Chang Choi ◽  
Gianni D’Angelo ◽  
Francesco Palmieri
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Special Issue ◽  
Research Experiences ◽  
Original State ◽  
Art Research ◽  
Open Issues

This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.

scholarly journals DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 81-85 ◽  
Author(s):  
D. A. Gavrilov ◽  
N. N. Shchelkunov ◽  
A. V. Melerzanov
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Skin Lesions ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Diagnosis ◽  
Malignant Skin ◽  
Diagnosis Accuracy ◽  
Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

scholarly journals Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform

2020 ◽  
Vol 10 (19) ◽  
pp. 7003 ◽  
Author(s):  
Pedro Narváez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Intelligent Systems ◽  
Heart Sound ◽  
State Of The Art ◽  
Computational Cost ◽  
Heart Sounds ◽  
Generative Adversarial Networks ◽  
Sound Classification ◽  
Adversarial Networks ◽  
Empirical Wavelet Transform

Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.

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