A Method for the Associative Storage of Analog Vectors

A method for storing analog vectors in Hopfield's continuous feedback model is proposed. By analog vectors we mean vectors whose components are real-valued. The vectors to be stored are set as equilibria of the network. The network model consists of one layer of visible neurons and one layer of hidden neurons. We propose a learning algorithm, which results in adjusting the positions of the equilibria, as well as guaranteeing their stability. Simulation results confirm the effectiveness of the method

Nondeterministic data base for computerized visual perception

A description is given of the knowledge representation data base in the perception subsystem of the Mars robot vehicle prototype. Two types of information are stored. The first is generic information that represents general rules that are conformed to by structures in the expected environments. The second kind of information is a specific description of a structure, i.e., the properties and relations of objects in the specific case being analyzed. The generic knowledge is represented so that it can be applied to extract and infer the description of specific structures. The generic model of the rules is substantially a Bayesian representation of the statistics of the environment, which means it is geared to representation of nondeterministic rules relating properties of, and relations between, objects. The description of a specific structure is also nondeterministic in the sense that all properties and relations may take a range of values with an associated probability distribution

An analog feedback associative memory

A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is developed for the Hopfield continuous-time network. An important requirement is that each memory vector has to be an asymptotically stable (i.e. attractive) equilibrium of the network. Some of the limitations imposed by the continuous Hopfield model on the set of vectors that can be stored are pointed out. These limitations can be relieved by choosing a network containing visible as well as hidden units. An architecture consisting of several hidden layers and a visible layer, connected in a circular fashion, is considered. It is proved that the two-layer case is guaranteed to store any number of given analog vectors provided their number does not exceed 1 + the number of neurons in the hidden layer. A learning algorithm that correctly adjusts the locations of the equilibria and guarantees their asymptotic stability is developed. Simulation results confirm the effectiveness of the approach

DABI: A data base for image analysis with nondeterministic inference capability

A description is given of the data base used in the perception subsystem of the Mars robot vehicle prototype being implemented at the Jet Propulsion Laboratory. This data base contains two types of information. The first is generic (uninstantiated, abstract) information that specifies the general rules of perception of objects in the expected environments. The second kind of information is a specific (instantiated) description of a structure, i.e., the properties and relations of objects in the specific case being analyzed. The generic knowledge can be used by the approximate reasoning subsystem to obtain information on the specific structures which is not directly measurable by the sensory instruments. Raw measurements are input either from the sensory instruments or a human operator using a CRT or a TTY

Application Of Malaria Detection Of Drawing Blood Cells Using Microscopic Opencv

The goal of the research is to produce an application, which can detect malaria on patient through microscopic digital image of blood sample. The research methods are data collection, design analysis, testing and evaluation. The used application methods are image pre-processing, morphology and image segmentation using OpenCV. The expected result is a creation of application, which can be able to detect malaria on a microscopic digital image of patient blood sample. The conclusion is that the application can detect malaria from young trophozoites stadium and gametesocytes from the picture.Keywords: Detection; Malaria; Computer Vision; OpenCVINTRODUCTIONSystem technology of computer-based with artificial intelligence already can be used in medicine field, for example, to resolve the problems: detecting specific disease and its symptoms, analyzing the content of a sample, monitoring the condition of an organ, and others. Nevertheless, the medical field is very wide, so for detecting diseases problems, not yet much disease that detection can be done with a computer-based system. One example of the issues is well-known disease detection, which is malaria. Malaria is classified as a serious disease because it can cause death if it is not treated properly. Malaria has various types and can affect anyone anywhere. The symptoms of malaria is really common as it may appear in daily life, but cannot always indicate that a person infected with malaria. Indications, which can show that a person infected with malaria, are the clinical examination and blood tests.With the blood test, the treatment of malaria can be implemented correctly and precisely. It needs technology that can detect malaria correctly and precisely. The solution is the method of support vector machine that can detect malaria in humans by viewing image of appearance blood cells.METHODThe methods used in this research are data collection, analysis and design. The data collection includes literature studies about computer division with OpenCV and data collection of microscopic of blood sample. Analysis method includes process, detection procedure and malaria diagnosis. While design method includes steps of detection implementation and diagnosis to the application program, coding and continued with evaluation.MalariaMalaria parasites in human have a life cycle that requires a human host and mosquito host. In the anopheles mosquito, plasmodium does sexual reproduction. In humans, these parasites asexual reproduction, starting in the liver cells (hepatocytes), then repeatedly in the red blood cells (erythrocytes).While an infected female anopheles mosquito is sucking human\u27s blood, at the same time the mosquito inserts its saliva that is to keep the capillary vessels, which is inhaled not forming a blood clots factor that causes the blood flow stops. At this time the parasite creates sporozoites to enter the blood flow and infect hepatocytes. For one until two weeks (depends on plasmodium species), each sporozoites creates schizont; a structure that contains thousands of merozoites. When schizont is mature, hepatocytes will rupture and release merozoites to blood flow.In plasmodium vivax and plasmodium ovale, sporozoites develops into hipnozoit; a form of plasmodium that in dorman phase during several months to years. When hipnozoit re-activate, they will evolve into schizont that will cause recurrent symptoms to the infected person.Next is the merozoites, which is released to the blood flow, will invade erythrocyte then they will grow and consume hemoglobin. In erythrocyte, half of merozoites will grow to another phase of asexual, which creates schizont filled with merozoites. When schizont is mature, the cell will rupture and merozoites will be released and invade erythrocyte