A new solid-state Wavelength-Selective Switch (WSS) based on a Programmable Micro-Diffractive Grating (PMDG) fabricated over a Lithium-Niobate (LiNbO3) substrate is presented, and its operation described. The device consists of a periodic arrangement of ridge waveguides whose optical phase delay can be individually or collectively tuned through the electro-optics (Pockels) effect. Each waveguide is patterned with a metal electrode and connected to an external microprocessor-based driving unit. By appropriately programming the phase shift induced on each waveguide, the far-field diffraction pattern of the grating can be shaped in order to implement complex all-optical signal processing functions, such as 1-to-M demultiplexing, beam splitting, beam steering or wavelength-selective filtering. Once integrated within a telecommunications infrastructure, this component can clearly enhance the level of flexibility and robustness of the network optical physical layer.
This work presents and discusses the features of a monolithic Programmable Micro Diffractive Grating (PMDG) fabricated over a lithium niobate substrate, which can be used to synthetize the visible and near-infrared spectra of important analytes, including dangerous materials (chemically aggressive, toxic or explosive gases). The functional core of the device consists of a periodic arrangement (array) of ridge waveguides whose optical delay (phase shift) is controlled electrically via the linear electro-optical (Pockels) effect. By distinctly polarizing every waveguide composing the comb with a suitable voltage, the collective transparency of the grating can be tailored so that the output far-field, at a predetermined diffraction angle, may reproduce a spectral distribution of interest. Therefore, this device can serve as universal reference cell in a correlation spectroscopy set-up, with particular interest for safety and security applications, as it could avoid the direct manipulation of dangerous or explosive materials. Moreover, by using a dual colour InGaAs detector, the sensing system can process optical spectra covering an extremely wide wavelength band, from the near UV, (~380 nm), to the MIR (~2.5 μm). In the present article, a schematic description of the sensing system, together with a detailed description of the PMDG device and its programming, will be provided and compared with some experimental data and the corresponding generated synthetic spectra. Examples of simulation of synthetic spectra generation in the case of some gases of interest for safety and security, together with the modelling of the device performances, as a function of the design parameters will also be discussed.
In this paper, we present an evolution of the classical Barrow-Wheeler Transform (BWT) algorithm applied to the sorting procedure of interferometric data, in view of their lossless compression: the Dynamic Segmentation and Sorting (DSS) algorithm. This algorithm is based on the application of the Dynamic Perceptron (DP) neural algorithm. It allows a fast and computationally efficient dynamic segmentation into homogeneous blocks of the coefficients of the Fourier transform. In this way, a limited order sorting procedure of such coefficients can be allowed, optimizing the BWT sorting procedure, characterized as such by an unlimited order. This new method has been specifically studied in order to its hardware implementation for integrating, as a fast compression module, the Micro-Electro-Optical-Mach Zender-Sensor (MEOMS) -- a integrated optical micro-sensor of the MEOS class, based on an array of Mach-Zender type interferometers. This device has been recently designed and constructed in Italy, at the IMM and ISAC Institutes of the Italian National Research Council (CNR) in Bologna. Because of its characteristics, the complete system is suitable for being installed onboard on satellites, for the continuous monitoring of the earth atmosphere. Some encouraging previous results of our lossless compression module of the interferograms outputted by the MEOMS are presented at the end of this paper.
In this paper we present an encryption module included in the Subsidiary Communication Channel (SCC) System we are developing for video-on-FM radio broadcasting. This module is aimed to encrypt by symmetric key the video image archive and real-time database of the broadcaster, and by asymmetric key the video broadcasting to final users. The module includes our proprietary Techniteia Encryption Library (TEL), that is already successfully running and securing several e-commerce portals in Europe. TEL is written in C-ANSI language for its easy exportation onto all main platforms and it is optimized for real-time applications. It is based on the blowfish encryption algorithm and it is characterized by a physically separated sub-module for the automatic generation/recovering of the variable sub-keys of the blowfish algorithm. In this way, different parts of the database are encrypted by different keys, both in space and in time, for granting an optimal security.
KEYWORDS: Video, FM band, Fermium, Frequency modulation, Video compression, Image compression, Discrete wavelet transforms, Modulation, Data communications, Image processing
In this paper we have sketched some technical details of an FM sub-carrier technology called Multi Purpose Radio Communication Channel (MPRC). This technology delivers actually data at maximum data rate of around 40 kbs using a proprietary codec algorithm: Subsidiary Communication Channel (SCC). A core device of this codec algorithm is a DWT compressor with a proprietary pre-processing, constituted by a neural self-adapting filter, the Dynamic Perceptron Algorithm (DPA), able to detect edges and to extract objects from the moving images flow, so to optimize the overall compression rate and the image quality. As a result it is possible to obtain video transmission in QCIF format at roughly 8/12 fps using 35 kHz of the 100 kHz available for a commercial FM radio station in Europe. This allows transmitting video on FM radio together with the usual radio broadcasting. On the contrary, if we use all the available 100 kHz., we obtain, after the charge related to the error protocol, a channel for compressed video transmission of about 113 kbit, allowing high quality 640x480 (zoomed or not zoomed) video images.
We propose a novel method for simultaneous speckle reduction and data compression based on wavelets. The main feature of the method is that of preserving the geometrical shapes of the figures present in the noisy images. A fast algorithm, the dynamic perceptron, is applied to detect the regular shapes present in the noisy image. Another fast algorithm is then used to find the best wavelet basis in the rate- distortion sense. Subsequently, a soft thresholding is applied in the wavelet domain to significantly suppress the speckles of the synthetic aperture radar images, while maintaining bright reflections for subsequent detection.
In this paper we present an innovative lossy plus lossless residual encoding scheme consisting of the following steps: (A) Dynamic pre-processing applied either to the original image in order to separate homogeneous parts of it; or to the histogram of the pixel values in order to generate three images each with the same size of the original one that superposed reconstruct exactly the source image. (B) Use of an efficient lossy compression scheme to pre-processed data in order to generate low bit rate images. (C) Definition of residuals by computing the differences between the lossy reconstructions and the pre-processed images. (D) Encode the residuals using an appropriate lossless technique. We applied this double scheme, with the two different pre-processing techniques, to some HST FITS images, obtaining from 1:4 to 1:6.4 lossless compression ratios.
Aim of this work is to demonstrate theoretically and experimentally how straightforwardly simple neural structures can obtain satisfying results in financial forecasting that can be easily used by market operators. The simplicity of the structures can allow indeed very flexible and user friendly implementations also for real-time forecasting. Such structure simplicity however has to be rightly understood. In fact, it is the result of a wide experimental research and a consequent theoretical demonstration devoted to outline a mathematical theorem for the definition of the optimal minimal neural structure for particular and very diffused typologies of financial data. The discussion of these theoretical and experimental results will be developed in this paper according to the following scheme: Deep theoretical discussion of the precedent points in terms of the 'generalization-learning theorem' for classical neural architectures. Recalling of the main principles underlying our 'dynamic perceptron' architecture presented and discussed elsewhere, also in precedent Orlando's SPIE Conferences. Partial neural implementation of these ideas by modification in a 'dynamic' sense of a classical back-propagation architecture. Application of the theoretical results discussed above to the time series of monetary cross-rates.
In this paper we discuss a first parallel implementation of our new compression algorithm for lossy and lossless image compression. The compression algorithm is based on a new method of `dynamic quantization' of the coefficients of the wavelet transform applied on the data. The parallel implementation of the algorithm is on the Italian QUADRICS parallel supercomputer of the Alenia-Space Co. This implementation follows the principles of our `dynamic perceptron' neural algorithm we presented elsewhere. The results of the comparison between the performances of our compression prototype and the usual algorithms for image compression will also be discussed. The tests were made on standard SAR images of the European Space Agency. The results suggest this parallel implementation is able to perform a real time efficient compression of SAR image (15 Mb/sec).
In this paper, starting from a general discussion on neural network dynamics from the standpoint of statistical mechanics, we discuss three different strategies to deal with the problem of pattern recognition in neural nets. Particularly we emphasized the role of matching the intrinsic correlations within the input patterns, to solve the problem of the optimal pattern recognition. In this context, the first two strategies, we applied to different problems and we discuss in this paper, consist essentially in adding either white noise or colored noise (deterministic chaos) on the input pattern pre-processing, to make easier for a classical backpropagation algorithm the class separation, respectively because the input patterns are too correlated among themselves or, on the contrary, are too noisy. The third more radical strategy, we applied to very hard pattern recognition problems in HEP experiments, consists in an automatic (dynamic) redefinition of the same net topology on the inner correlations of the inputs.
With respect to Rosenblatt linear perceptron, two classical limitation theorems demonstrated by M. Minsky and S. Papert are discussed. These two theorems, `(Psi) One-in-a-box' and `(Psi) Parity,' ultimately concern the intrinsic limitations of parallel calculations in pattern recognition problems. We demonstrate a possible solution of these limitation problems by substituting the static definition of characteristic functions and of their domains in the `geometrical' perceptron, with their dynamic definition. This dynamic consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input.
In this paper we start from a critical analysis of the fundamental problems of the parallel calculus in linear structures and of their extension to the partial solutions obtained with non- linear architectures. Then, we briefly present a new dynamic architecture able to solve the limitations of the previous architectures through an automatic redefinition of the topology. This architecture is applied to real time recognition of particle tracks in high energy accelerators and in astrophysics experiments.
Many papers have been published recently about the characterization of time-dependent processes through techniques using wavelet approach. Our work takes into account a particular class of time-dependent processes in nonlinear realm. We want to characterize chaotic dynamics from the standpoint of its unstable periodicities. For this aim we introduce a new technique able to stabilize such unstable orbits. We illustrate this technique both from the theoretical and the experimental standpoint. As a further step, we want to deal with the problem of detecting and removing noise from chaotic dynamics. In this paper, firstly, we show how our technique is able to distinguish with very high sensitivity between a purely chaotic dynamics and a chaotic dynamics with noise even though the noise percentage is very low (of the order of 1 percent only Secondly, we apply our technique to remove noise from this dynamics. Finally, we compare both from the theoretical and experimental standpoint our technique with the well known wavelet technique. This work is a part of 'Skynnet' international project supported by the Italian National Institute for Nuclear Physics (INFN) and partially devoted to the application of new chaotic techniques instantiated in neural architectures for compressing, storing and transmitting information to earth from satellites.
We want to present a further development of our technique of backpropagation with stochastic preprocessing to recognize particle tracks in a silicon calorimeter on a satellite to detect cosmic ray composition. In the first release we applied our technique to distinguish between two classes of discrete patterns. In the present release we developed the stochastic preprocessing to deal with continuous patterns such as the energy deposited by a cosmic particle. From the theoretical standpoint we demonstrate that by such a preprocessing technique the neural net is able to represent the complexity of learning set in a polynomial and not exponential time. This work is a part of `Skynnet' international project supported by INFN (National Institute for Nuclear Physics) and partially devoted to the application of neural techniques for recognition of high energy particle tracks in spatial environment.
In the context of M. Minsky's and S. Papert's theorems on the impossibility of evaluating simple linear predicates by parallel architectures we want to show how these limitations can be avoided by introducing a generalized input-dependent preprocessing technique that does not suppose any a-priori knowledge of input like in classical input filtering procedures. This technique can be formalized in a very general way and can be also deduced by meta- mathematical arguments. A further development of the same technique can be applied at level of learning procedure to introduce in such a way the complete notion of `dynamic perception'. From the experimental standpoint, we show two applications of the dynamic perceptron in particle track recognition in high-energy accelerators. Firstly, we show the amazing improvement of performances that can be obtained in a perceptron architecture with classical learning by adding our dynamic preprocessing technique, already introduced last year in another paper presented at this Conference. Secondly, we show the first results of this technique extended also at the level of learning procedure always applied to the problem of particle track recognition.
In order to find a very efficient technique to compress, store, and transmit to earth information from a satellite we developed a scheme of chaotic neural net using a new technique of extraction of unstable orbits within a chaotic attractor without applying classical embedding dimensions. We illustrate this technique both from the theoretical and the experimental standpoint. From the theoretical standpoint we show that by this extraction technique it is possible to perform a series expansion of a chaotic dynamics directly through all its composing cycles. Finally, we show how to apply these new possibilities deriving from our new technique of chaos detection, characterization, and stabilization to design a chaotic neural net. Because it is possible to profit by all the skeleton of unstable periodic orbits (i.e., all the inner frequencies) characterizing a chaotic attractor to store information, this net can in principle display an exponential increasing of memory capacity with respect to classical attractor nets.
With respect to three different paradigms of neural networks generally studied: (1) the convergent one; (2) the oscillatory one; (3) the chaotic one; we propose a fourth one. In some general sense, it makes the precedent ones three particular cases of itself. The core of the approach is based on a mutual redefinition of short term memory (STM) and long term memory (LTM) able to overcome computability problems typical of pattern recognition. An application is shown about real time particle discrimination in high energy physics at ADONE e+e- storage ring in Frascati (Italy). The computational effectiveness of the proposed solution has made us able to have real-time particle discrimination in software.
A new method of extraction of unstable periodic orbits from chaotic dynamics is presented. This method is founded on a theorem of mutual redefinition between numbers (space) and processes (dynamics) to solve diagonalization problems. The computational relevance of the method is discussed in view of grating a characterization of chaotic dynamics in linear (not exponential) time. A possible informational use of this method is sketched.
With respect to Rosenblatt linear perceptron, a classical limitation theorem demonstrated by M. Minsky and S. Papert is discussed. This theorem, '$PSIOne-in-a-box', ultimately concern the intrinsic limitations of parallel calculations in pattern calculations in pattern recognition problems. We demonstrate a possible solution of this limitation problem by substituting the static definition of characteristic functions and of their domains in the 'geometrical' perceptron, with their dynamic definition. This dynamics consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input. We show an application of this 'dynamic' perceptron scheme in particle tracks recognition in high energy physics. Actually, this algorithm is being used for real time automatic triggering of ADONE e+e- storage ring (Frascati, Rome) to evaluate the neutron time-like electromagnetic form factor in the context of 'Fenice' collaboration by Italian Institute of Nuclear Physics (INFN).
Usually, to discriminate among particle tracks in high energy physics a set of discriminating parameters is used. To cope with the different particle behaviors these parameters are connected by the human observer with boolean operators. We tested successfully an automatic method for particle recognition using a stochastic method to pre-process the input to a back propagation algorithm. The test was made using raw experimental data of electrons and negative pions taken at CERN laboratories (Geneva). From the theoretical standpoint, the stochastic pre-processing of a back propagation algorithm can be interpreted as finding the optimal fuzzy membership function notwithstanding high fluctuating (noisy) input data.
After a short discussion on the problems related to the higher order correlations treatment in Hopfield neural net, we propose a modified architecture able to rearrange dynamically its topology in function of the input representation. The relations of this problem with computability problems are briefly considered, particularly in view of avoiding exponential time in computation. Some experimental results are shown for the recognition of particle traces in high energy accelerators and in speaker independent speed recognition.
After a discussion of some theoretical limitations and their experimental demonstration of multilayer architectures in contextual pattern recognition, we propose an implementation of a spin-glass like neural net designed to deal efficiently in real time with time-dependent inputs (pattern translations, rotations, scaling, deformations) in noisy environments. The basic idea is a double dynamic on activations and weights on the same time scale. The two dynamics are correlated through an STM locking function on the object. This locking is the means by which the LTM module of the net can perform an invariant recognition of the object under transformations. This is possible owing to the invariant extraction of global features. The net is non-stationary and asymmetrical, because it is able to choose the right correlation order regarding the memorized prototypes for a successful recognition. Nevertheless, the same non- stationary condition, depending on the locking on an object under transformations, implies that the net displays a non-relaxing stabilization. It is presented as an application of the model to the classical recognition problem of rotation `T' and `C' pattern sequences in different noisy contexts.
With respect to the three different paradigms of neural networks generally studied (convergent, oscillatory, chaotic), a fourth is proposed. In some general sense, it makes the precedent ones three particular cases of itself. It is defined as a nonstationary model of a spin- glass like neural net. It has both a dynamics on the spins and on the weights in view of granting to the net a continuous redefinition of its phase space on a purely dynamic basis. So the system displays different behaviors (noisy, chaotic, stable) in function of its finite temporal order parameter, i.e., in function of a finite correlation among the spins acting on the weight dynamics. A first analysis of this model, capable of making nonstationary the probability distribution function on the spins, is developed in comparison with several paradigms of relaxation neural nets, developed in the classical framework of statistical mechanics. The nonstationary, analytically unpredictable, but deterministic and hence computable behavior of such a model is useful to make a neural net able to reckon with recognition tasks of nonsteady inputs and semantical problems.
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