Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning

Distributed representations were often criticized as inappropriate for encoding of data with a complex structure. However Plate's Holographic Reduced Representations and Kanerva's Binary Spatter Codes are recent schemes that allow on-the-fly encoding of nested compositional structures by real-valued or dense binary vectors of fixed dimensionality. In this paper we consider procedures of the Context-Dependent Thinning which were developed for representation of complex hierarchical items in the architecture of Associative-Projective Neural Networks. These procedures provide binding of items represented by sparse binary codevectors (with low probability of 1s). Such an encoding is biologically plausible and allows a high storage capacity of distributed associative memory where the codevectors may be stored. In contrast to known binding procedures, Context-Dependent Thinning preserves the same low density (or sparseness) of the bound codevector for varied number of component codevectors. Besides, a bound codevector is not only similar to another one with similar component codevectors (as in other schemes), but it is also similar to the component codevectors themselves. This allows the similarity of structures to be estimated just by the overlap of their codevectors, without retrieval of the component codevectors. This also allows an easy retrieval of the component codevectors. Examples of algorithmic and neural-network implementations of the thinning procedures are considered. We also present representation examples for various types of nested structured data (propositions using role-filler and predicate-arguments representation schemes, trees, directed acyclic graphs) using sparse codevectors of fixed dimension. Such representations may provide a fruitful alternative to the symbolic representations of traditional AI, as well as to the localist and microfeature-based connectionist representations

Development and analysis of computer vision system for micromechanics

Summary: In micromechanics the best technologies are MicroElectroMechanical Systems (MEMS) and MicroEquipment Technology (MET). The MEMS used the electronic technology to produce mechanical components. Due to the advantages of the MET such as the development of low-cost micro devices, the possibility of using various manufacturing materials, the possibility of producing three-dimensional microcomponents it will be very useful to automatize all processes of mechanics production and develop different technological innovations. The automation and robotics are two closely related technologies since automation can be defined as a technology that is related to the use of mechanical-electrical systems based on computers for the operation and control of production. The field of micromechanics has been involved in different applications that cover almost all areas of science and technology, an example of this is the management of microdevices for the autofocus of digital cameras whose objective is image processing (recognizing and locate objects). The use of computer vision systems can help to automate the work of MEMS and MET systems, so the study of image processing using a computer is very important. The objective was to design a computer vision system that allows the movement of the lens to focus the work area, for the monitoring of the micromachine tool in manufacturing processes and assembly of microcomponents in real time using previously developed image recognition algorithms. The developed algorithms use the criterion of improving the contrast of the input image. We describe our approach and obtained results. This approach can be used not only in micromechanics but in nanomechanics to

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

Image Recognition Systems with Permutative Coding

A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%

Permutation Coding Technique for Image Recognition Systems

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1

Micromechanics as a testbed for artificial intelligence methods evaluation

Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Solar Concentrators Manufacture and Automation

Solar energy is one of the most promising types of renewable energy. Flat facet solar concentrators were proposed to decrease the cost of materials needed for production. They used small flat mirrors for approximation of parabolic dish surface. The first prototype of flat facet solar concentrators was made in Australia in 1982. Later various prototypes of flat facet solar concentrators were proposed. It was shown that the cost of materials for these prototypes is much lower than the material cost of conventional parabolic dish solar concentrators. To obtain the overall low cost of flat facet concentrators it is necessary to develop fully automated technology of manufacturing and assembling processes. Unfortunately, the design of known flat facet concentrators is too complex for automation process. At present we develop the automatic manufacturing and assembling system for flat facet solar concentrators. For this purpose, we propose the design of flat facet solar concentrator that is convenient for automatization. We describe this design in the paper. At present, almost all solar-energy plants in the world occupy specific areas that are not used for agricultural production. This leads to a competition between the solar-energy plants and agriculture production systems. To avoid this competition, it is possible to co-locate solar-energy devices in agricultural fields. The energy obtained via such co-location can be used for agricultural needs (e.g., water extraction for irrigation) and other purposes (e.g., sent to an electrical grid). In this study, we also describe the results of an investigation on co-location methods for the minimal loss of agricultural harvest too

Travelling Energy Collectors

Abstract: Almost all modern solar and wind energy plants can be used only as auxiliary energy sources because of their intermittent character. On the other hand, geothermal systems can produce energy continuously. However, geothermal power plants need expensive wells, and the well will not always give high temperature underground water. It is possible to improve the performance of the plant by combining the different features of these mentioned systems. It is possible to obtain hot water not from drills but by using solar and wind energy installations placed on mobile platforms (travelling energy collectors) that will transport hot water to the power plant, where it will be stored in special tanks. A similar procedure is possible for cold water. To transform thermal energy, stored in the hot water and cold water tanks to electric energy it is possible to use conventional equipment of geothermal power plants. In this paper we give estimations of some parameters of the proposed power generation system based on travelling energy collectors. The estimations show that the power plant based on travelling energy collectors can be considered as a base load source of electric energy

Micromechanics as a testbed for artificial intelligence methods evaluation

Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI