{"title":"Use of a neural net computer system for analysis of flow cytometric data of phytoplankton populations","authors":"D. Frankel, R. Olson, S. Frankel, S. Chisholm","doi":"10.1109/IJCNN.1989.118316","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118316","url":null,"abstract":"Summary form only given. A description is given of the application of neural net computer technology to the analysis of flow cytometry data. Although the data used in this study are from oceanographic research, the results are general and should be directly applicable to flow cytometry data or any sort. The neural network described offers the advantages of adaptability to changing conditions and potential real-time analysis. High accuracy and processing speed, near that required for real-time classification, have been achieved in a software simulation of the neural network on a Macintosh SE personal computer.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neural networks for extraction of weak targets in high clutter environments","authors":"M. W. Roth","doi":"10.1109/21.44038","DOIUrl":"https://doi.org/10.1109/21.44038","url":null,"abstract":"Because of the statistical nature of many types of clutter, a detection device must set a high threshold in order to maintain a reasonable false-alarm rate. However, by selecting this threshold setting, detections of small and medium size targets can be missed. An old but previously impractical technique for improving performance was to use all contacts from several scans and employ a very large bank of matched filters. This could achieve a detection on one or more of all possible target trajectories formed from stored contacts for each input detection. Neural network hardware offers new opportunities to implement such techniques. It is shown that feedforward and graded-response Hopfield neural networks can implement the optimum postdetection target track receiver. For the Hopfield net, the spurious states correspond to the important case of multiple track detection. Finally, the author presents simulations that show that substantial signal-to-noise gain can be achieved.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116827750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Roitblat, P. Moore, P. E. Nachtigall, R. Penner, W. Au
{"title":"Dolphin echolocation: identification of returning echoes using a counterpropagation network","authors":"H. Roitblat, P. Moore, P. E. Nachtigall, R. Penner, W. Au","doi":"10.1109/IJCNN.1989.118594","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118594","url":null,"abstract":"The authors report on the result of experiments on the recognition of targets by an echo-locating dolphin and by a counterpropagation neural network. The first experiment describes the success of a counterpropagation network with 20 input bands in classifying four different targets on the basis of the spectral distribution returned in the echo from the objects. Echoes for this experiment were collected in a quiet test pool using a simulated dolphin click as the source. These patterns were classified with 100% accuracy. These data compared well with those obtained from a real dolphin recognizing these same targets in a noisy natural environment (94.5% correct). The same network architecture was then used to classify echoes from three of these targets, collected while the dolphin echo-located in the noisy environment while performing the item recognition task. Under these conditions, the network was 96.7% correct. These results suggest that neural networks of various sorts may be promising computational devices for automated sonar target recognition and for the modeling of cognitive and perceptual processes in dolphins.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Ohta, M. Takahashi, Y. Nitta, S. Tai, K. Mitsunaga, K. Kyuma
{"title":"A new approach to a GaAs/AlGaAs optical neurochip with three layered structure","authors":"J. Ohta, M. Takahashi, Y. Nitta, S. Tai, K. Mitsunaga, K. Kyuma","doi":"10.1109/IJCNN.1989.118285","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118285","url":null,"abstract":"A GaAs/AlGaAs optical synaptic interconnection device for neural networks is reported. It consists of a light-emitting-diode array, an interconnection matrix, and a photodiode array, which are integrated in a hybrid-layered structure on a GaAs substrate. The device structure and characteristics are reported. The fabricated device can simulate a 32-neuron system. The experimental results for a Hopfield associative memory with three stored vectors are described.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127060782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Outline of a theory of massively parallel analog computation","authors":"B. MacLennan","doi":"10.1109/IJCNN.1989.118390","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118390","url":null,"abstract":"Summary form only given. The author proposes the following definition of massive parallelism. A computational system is massively parallel if the number of processing elements is so large that it may conveniently be considered a continuous quantity. The author proposes this definition of massive parallelism for a number of reasons. First, skillful behavior seems to require significant neural mass. Second, he is interested in computers, such as optical computers and molecular computers, for which the number of processing elements is effectively continuous. Third, continuous mathematics is generally easier than discrete mathematics. The author develops a theoretical framework for understanding massively parallel analog computers.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128835137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Biological model of vision for an artificial system that learns to perceive its environment","authors":"M. Blackburn, H. G. Nguyen","doi":"10.1109/IJCNN.1989.118702","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118702","url":null,"abstract":"A computer algorithm is described which implements models of the biological visual mechanisms of the retina, thalamic lateral geniculate and perigeniculate nuclei, and primary visual cortex. Motion and pattern analyses are performed in parallel and interact in the cortex to construct perceptions. The authors hypothesize that motion reflexes serve as unconditioned pathways for the learning and recall of pattern information. The algorithm demonstrates this conditioning through a learning function approximating heterosynaptic facilitation.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115311870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A neural network for invariant object recognition in a robotic environment","authors":"S.-C. Lyon, Luoting Fu","doi":"10.1109/IJCNN.1989.118465","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118465","url":null,"abstract":"Summary form only given, as follows. Object recognition, which may be subject to occlusion or to various combinations of scaling, translational, and rotational transformations from prestored object models, is under investigation. Such an environment is very typical in the applications of robotics. A 'pure' neural network approach is adopted here, i.e. without including any mathematical transforms, such as polar or Fourier transforms, as a preprocessor. Detailed discussions on the neocognitron by Fukushima are given which show that the network model is able to solve the problems of invariant recognition and of occlusion resolving by adjusting the parameters of both static structures and dynamic learning rules.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementation of an adaptive neural controller for sensory-motor coordination","authors":"M. Kuperstein, Jorge Rubinstein","doi":"10.1109/37.24808","DOIUrl":"https://doi.org/10.1109/37.24808","url":null,"abstract":"A theory and the prototype of a neural controller called INFANT that learns sensory-motor coordination from its own experience are presented. INFANT adapts unforeseen changes in the geometry of the physical motor system and to the location, orientation, shape, and size of objects. It can learn to accurately grasp an elongated object without any information about the geometry of the physical sensory-motor system. This new neural controller relies on the self-consistency between sensory and motor signals to achieve unsupervised learning. It is designed to be generalized for coordinating any number of sensory inputs with limbs of any number of joints. INFANT is implemented with an image processor, stereo cameras, and a 5 degrees-of-freedom robot arm. Its average grasping accuracy after learning is 3% of the arm's length in position and 6 degrees in orientation.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"7 27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129608360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On training of artificial neural networks","authors":"J. Wang, B. Malakooti","doi":"10.1109/IJCNN.1989.118727","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118727","url":null,"abstract":"A theory and methodology are presented for training artificial neural networks in a general setting. Starting with defining general concepts, and analyzing associated properties of artificial neural networks, the authors formalize, categorize, and characterize artificial neural networks from a system point of view. They focus on the analysis aspect of artificial neural nets to address and investigate trainability and representability; on the synthesis aspect of artificial neural nets to provide design principles to the systems; and on the algorithmic aspect of the artificial neural nets to develop an effective and efficient learning paradigm.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1989-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115161551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Target detection using a neural network based passive sonar system","authors":"A. Khotanzad, J. H. Lu, M. Srinath","doi":"10.1109/IJCNN.1989.118605","DOIUrl":"https://doi.org/10.1109/IJCNN.1989.118605","url":null,"abstract":"A neural-network (NN)-based system for the passive detection of targetlike signals in underwater acoustic fields is being developed. The input to the NN is an intensity modulated signal, which is a measure of the power of the received signal plus noise at different frequencies as time varies. Thus, a two-dimensional array (image) is to be examined to reach a decision. It is assumed that the target emits a sinusoidal signal at a fixed frequency f/sub 0/. If the target moves with a constant speed with respect to the receiver, the received signal frequency will be (1+ delta ) f/sub 0/, where delta is the Doppler shift. The received two-dimensional image is first thresholded to obtain a binary (0 or 1) image. The first stage of the proposed system consists of an autoassociative memory (ASM) whose function is to eliminate the noise and reconstruct the received signal. The output of the ASM is input to the second stage of the system, which consists of a multilayer perceptron (MLP) classifier trained using the backpropagation algorithm. The MLP outputs a decision regarding the presence or absence of the targets. Results of an initial experimental study are reported. A promising classification accuracy of 97% for targets and 100% for no-targets has been obtained.<<ETX>>","PeriodicalId":199877,"journal":{"name":"International 1989 Joint Conference on Neural Networks","volume":"941 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115388795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}