A video-based spatial optical communication method for a wearable information terminal using a video camera with an infrared LED ring is proposed. By the video image of the wearable terminal that is captured by the camera, the physical location of the terminal and user's messages that are carried by it can be detected simultaneously. Therefore, data communication can be implemented without the private data of users such as an address or ID number; thus the security and privacy of users will be maintained. The effectiveness of this method is demonstrated in a multilanguage communication terminal for a wearable information environment.
The National Institute of Advanced Industrial Science and Technology (AIST) in Japan has been developing Aimulet, which is a compact low-power consuming information terminal for a personal information services. Conventional Aimulet, which is called Aimulet ver. 1 or CoBIT, has features of location and direction sensitive information service device without batteries. On the other hand, the Aimulet ver. 1 has two subjects, one is multiplex and demultiplex of some contents, and another is operation under sunshine. In Former subject is of solved by the wavelength multiplex technique using LED emitter with different wavelength and dielectric optical filters. Latter subject is solved by new micro spherical solar cells with a
visible-light-eliminating optical filter and a new design of light irradiation. These techniques are applied to the EXPO 2005, Aichi Japan and introduced in public. The former technique is applied on Aimulet GH, which is used in Orange Hall of the Global House, scientific museum with a fossil of a frozen mammoth. The latter technique is applied on Aimulet LA, which is used in the Laurie Anderson's WALK project in the Japanese Garden.
As an implementation of ubiquitous information service environments, we have been researching location-based information service systems at indoor and short distance area. The system should provide adequate information services, which fit the user's attributes, such as language, knowledge level and the volume of information, what is so-called "Right now, Here, and for Me" information services. Keeping privacy and security of the user is an important issue. Spatial optical communication technique is used for the system because the technique is easy to implement a location- and direction-based communication system. Information broadcasting in an area can be realized by an omnidirectional modulated light emission. However, the omnidirectional beam causes spill out of secure information to others, and has lower energy conservation than a focused beam communication. In this paper, a new spatial optical information broadcasting system, which can focus modulated beams only to intended users. CGH (Computer Generated Hologram) technique on a SLM (Spatial Light Modulator) is proposed and demonstrated. The system is composed of a PAL-SLM (Parallel Aligned Nematic Liquid Crystal Spatial Light Modulator), an eye-safe semiconductor laser or a semiconductor laser pumped YAG laser for the beam emitter, and an infrared video camera with an infrared LED illuminator for user locator. Experimental results of beam deflecting characteristics are described on beam uniformity, deflecting angle and the enhancement, communication characteristics, and real time tracking of user with a corner-reflecting sheet.
The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.
We constructed a learning optical neural network with variable learning coefficient by fuzzy controlling. The system performs learning with 2D optical means for handling images without scanning and pixeling. By the fuzzy controlling theory, the learning coefficient in back- propagation algorithm is adjusted based on the training error and training time. The effectiveness of the system confirmed by the learning experiments of the recognition of three human faces.
Optical associative memory for pattern recognition using the Hopfield model has the advantage of simplicity for its network structure. But further investigation reveals that the basins of the attractors for stored patterns are small in the Hopfield model and the recalling ability is not so great. Moreover, the memory capacity is low because there are many spurious states and oscillations in the network. In order to obviate the spurious states and low storage capacity in the Hopfield model, a type of attractor called a terminal attractor (TA), which represents singular solutions of a neural dynamic system, was introduced. These terminal attractors are characterized by having finite relaxation times, no spurious states, and infinite stability.
The memory capacity of terminal attractor (TA) model associative memory is investigated based on the consistency between the stored pattern x$_i)<SUP>(m</SUP>) and the obtained equilibrium state x<SUB>i</SUB> in statistical thermodynamics. By the computer simulations, we give intuitive estimates of the memory capacity of the TA model associative memory. FOr the feasibility of the optical implementation of the TA associative memory, we impose some approximations to original TA associative memory without loosing the essence of the TA model. The memory capacity of such a modified TA model associative memory is also given by the numerical simulation. In this simulation, a 10 X 10 neuron network model is used and Hamming distances among inputs and the stored patterns are chosen to be equal to 5 or more both in the original and modified TA models. The results indicate that the absolute memory capacity of the TA model is greater than 0.35N, which contrasts with the relative capacity of 0.15N or the theoretical absolute capacity of N/(41nN) for the conventional associative memory.