An investigation has been done on the parameters of a hysteretic bistable optical Schmitt trigger device. From a design point of view, it is important to know the regions where this bistability occurs and is fully functional with respect to its subsystem parameters. Otherwise experimentally reaching such behavior will be very time-consuming and frustrating, especially with multiple devices employed in a single photonic circuit. A photonic Schmitt trigger consisting of two feedbacked inverting amplifiers, each characterized by −m (slope), A (y-intercept), and B (constant base) parameters is considered. This system is investigated dynamically with a varying input to find its stable and unstable states both mathematically and with simulation. In addition to a complete mathematical analysis of the system, we also describe how m, A, and B can be properly chosen in order to satisfy certain system conditions that result in bistability. More restrictions are also imposed to these absolute conditions by the system conditions as will be discussed. Finally, all results are verified in a more realistic photonic simulation.
We report on two types of wavelength conversion techniques that are based on gain saturation effect in semiconductor
optical amplifier (SOA) and erbium doped fiber amplifier (EDFA). In these amplifiers the gain saturation occurs when
the optical density at the gain medium is high enough to result in depletion of the population inversion by stimulated
emission. In each case, the fiber ring laser is assembled using a variable fiber coupler, a narrowband optical filter and the
gain medium. For external input power values higher than the determined threshold value of the ring resonator, the gain
will be saturated. Because the wavelength of the external laser is different from the oscillating wavelength of the ring
resonator, the optical power at the output of the resonator is drastically decreased (low-state). On the other hand, when
the input of the external laser is below the threshold value the output power of the resonator increases (high-state). In our
experiment the operating wavelengths of the ring resonators are 1314 nm and 1553 nm for the SOA and EDFA
respectively. The input signal is modulated around the threshold value for frequencies of 20 MHz and 1 MHz and
resonator lengths of around 8 m and 16 m for the SOA and EDFA cases respectively. Both systems exhibit high contrast
modulation of 41 dB and 33 dB at the output port for the low/high states of the SOA and EDFA ring lasers respectively.
This paper describes a novel A/D converter called "Binary Delta-Sigma Modulator" (BDSM) which operates only with nonnegative signal with positive feedback and binary threshold. This important modification to the conventional delta-sigma modulator makes the high-speed (>100GHz) all-optical implementation possible. It has also the capability to modify its own sampling frequency as well as its input dynamic range. This adaptive feature helps designers to optimize the system performance under highly noisy environment and also manage the power consumption of the A/D converters.
This paper presents multimode fiber optic sensors suitable for large smart structures where distributed sensing needed. By many observations of speckle patterns from different multimode fibers, it seems that each speckle pattern has its own signature and uniquely defines its fiber. This prompted us to superimpose (multiplexed) the speckle patterns generated by two different fibers, and detect the resultant pattern. A two-stage feature extraction algorithm is then applied on the image, which reduces the dimension of the pattern vector. Next, a backpropagation neural network with single hidden layer is trained in mapping the feature vectors to the stress applied on each fiber. Series of experiments are conducted on two fiber sensors glued on a square shape plate under different point loads. The sensors successfully estimated that which fiber was under stress with light or heavy weight.
In this paper, we consider a peristrophic multiplexing for reflection holograms. This type of multiplexing the rotation of either the material or the reference beam causes the grating vector to be off the plane of the reference and image beams. In the case of reflection hologram, we developed a relationship for the angular selectivity which is verified experimentally.
In this paper, the design and implementation of smart actuators for active vibration control of mechanical systems are considered. The proposed smart actuator is composed of one or several layers of piezoelectric materials that works both as a sensor and an actuator, in vibration control applications. An adaptive technique is developed for estimating the unknown equivalent capacitance of the piezoelectric material, which would be used for separating the effect of actuation from the measured (sensed) signal due to the strain in the material. This algorithm can be implemented in real time on a digital signal processor (DSP), allowing for the development of a DSP-based adaptive self-sensing actuator. This self-sensing actuator is then used in the vibration control of flexible structures. The vibration control system includes a power electronic amplifier, a data acquisition system, and a DSP for digital control implementation. A simple PID control strategy is employed for vibration reduction and motion control of cantilever beams using the proposed self-sensing actuators. Simulations and preliminary experiments show good results.
This paper presents a multimode fiber optic sensor system suitable for large smart structures. By many observations of speckle patterns from different multimode fibers, it seems that each speckle pattern has its own signature and uniquely defining its fiber. This prompted us to superimpose (multiplexed) the speckle patterns generated by two different fibers, and detect the resultant pattern. A two-stage feature extraction algorithm is then applied on the image, which reduces the dimension of the pattern vector. Next, a backpropagation neural network with single hidden layer is trained in mapping the feature vectors to the stress applied on each fibers. Series of experiments are conducted on two fiber sensors glued on a square shape plate under different point loads. The sensors successfully estimated that which fiber was under stress with light or heavy weight.
This paper describes the development of an approach to estimate the applied stress sensed from a set of multimode fiber optic sensors which are laid on the surface of a smart structure. The estimation of the applied stress is based upon the discrimination between speckle patterns produced by different strain signals. Three approaches have been formulated to estimate/classify the applied stress from the speckle patterns: (a) neural network estimation, (b) Markov random field model classification, and (c) signature-based classification. In order to develop the neural network estimator which is trained to output an estimate of the applied strain signal vector, the dimension of the original input speckle vector is first reduced by estimating the entropy of each pixel and selecting the set of pixels which carry the most information in the training set. A statistical based clustering approach is formulated to reduce the dimension further by combining highly correlated pixels in the selected set. In the Markov random field model based approach, a Markovian model for texture is assumed to fit the speckle patterns. The model parameters, as estimated using maximum likelihood techniques, are used in conjunction with a nearest neighbor rule to classify the speckle images. The signature-based classification approach is a method which incorporates both dimensionality reduction and classification directly for the case when the reference speckle images from highly representative strain vectors are available.
This paper introduces a relatively simple multimode fiber optic sensor built for on-line use and a package that multiplexes two fiber sensors. The multiplexing is achieved by the random nature of the multimode fiber output intensity variation (so called speckle pattern). The demultiplexing is performed by a neural network. The dynamic range of each sensor is 0.8- 120 micrometers over an effective length of 11 m. The sensors operate in 68-94 degrees F.
Today neural networks are being adopted as an alternate method of solving complex pattern recognition/classification problems. Information regarding performance measure is critical in evaluating the capacity of this system in performing recognition/classification tasks. Currently this information is obtained using unstandardized empirical techniques. This study will attempt to devise a methodical procedure to qualitatively predict the performance measure of all neural network recongition classification systems governed by a set of ordinary differential equations. The determination of this characteristic will be made through the use of specific analytic methods in mathematics. Dynamical systems can therefore be qualitatively analyzed, and issues regarding existence of parasitic limit points can be more effectively addressed.
In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.
In this paper the competition between two modes in a uni-directional ring resonator having photorefractive gain, is investigated. Each mode is defined in terms of different frequency and angle of incidence. The interacting beams are assumed to be plane waves with an amplitude varying along the direction of propagation only.
A class of learning techniques for neural networks can be considered as optimization problems. The connection strengths are modified such that the difference between the network response and a desired response Is minimized. In this paper the learning techniques based on the gradient momentum Newton and quasi-Newton methods are considered. A learning algorithm is also developed based on the conjugate gradient technique. These learning techniques are applied to the Exclusive-OR problem for comparison of their performance. For this problem the algorithm based on the conjugate gradient technique converges faster than the other algorithms. 2.
Lithium niobate crystal has been used for storing information that can be accessed through an associative recall. This fact demonstrates the application of the crystal to the optical memory. specifically In an optical ring resonator. One important factor that determines the recall ability is the diffraction efficiency. Particularly this quantity Is also a function of the polarization of the readout beam. The diffraction efficiency of the crystal Is significant If the sufficient build-up of power in the real-time programmable ring resonator is required. To this end this paper is concentrated on the study of the dependency of diffraction efficiency on the polarization of recall beam. Experimental results are also given to indicate the significance of orientation of c-axis of the crystal in the experimental configuration. 2.
The paper proposes a neural network model for two layer channel routing. We hope this research will lead to a better understanding of the capability and limitations of neural networks as a general design methodology and in particular when it is applied to the routing problem in the design of VLSI chips. In our model the neurons form a two dimensional array where the value of element N represents the " chances" of net i is positioned at track j. The strength of connections between neurons are defined such that to prevent horizontal conflict between nets and also optimize a number of important routing metrics such as: minimum routing area and minimum wire length. We ran several examples for most of the small size examples ( less than 1 5 nets ) we were able to obtain good solutions. However for larger size examples apart from the known problems of long simulation times the router was not able to find a solution in many cases. 1.
This paper discusses an application of artificial neural networks in smart structures with fiber optic sensors. Emphasis is on using a novel neural network approach to characterize the Impact signals from the embedded sensors. A recurrent network is proposed for control of stress in the composite material. Preliminary results on a composite with embedded multimode fiber indicates that the speckle pattern of the optical fiber can be characterized for stress measurements. This new sensing technique will simplify the measurement components as compared to the conventional methods. 2.
Although neural networks are very effective pattern classifiers a major limitation is that they are not suitable for classifying patterns with Inherent time-variations. This paper describes an approach to incorporate a temporal structure in a neural network system which wifi accomodate the time variations in local feature sets encountered in problems such as partial shape classification. 1.
Proc. SPIE. 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90
KEYWORDS: Data modeling, Manufacturing, Data processing, Process control, Neural networks, Optical engineering, Electrical engineering, Network architectures, Systems modeling, Design for manufacturability
This paper discusses a dynamical network for mapping of interciass members without performing a learning process. This allows a member of class A to be mapped to a member of class B. Given sample members of each class a backpropagation network is trained to form the corresponding class boundaries. Upon completion of the training process the weights obtained are used in a recurrent network which performs the interclass member mapping without any further training. This mapping is achieved as the recurrent network evolves In time. The Initial state of the network is mapped to its equilibrium state. The interclass member mapping network (IMMN) has many applications in selfcorrecting systems. In this paper the IMMN is developed to represent two classes namely class B (for instance a class for representing members with desirable and correct features) and class A (members with incorrect features). An example is given in which two categories are used namely poorly and well-designed manufacturing parts. Given a poorly-designed part the network wifi suggest corrections resulting in a well-designed part. This example has nonlinear decision regions and shows the generalization capability of the network.
Proc. SPIE. 1396, Applications of Optical Engineering: Proceedings of OE/Midwest '90
KEYWORDS: Artificial neural networks, Data processing, Process control, Neural networks, Optical engineering, Dynamical systems, Systems modeling, Content addressable memory, New and emerging technologies, Brain
Artificial neural networks are Information processing structures based upon models of brain functions. They are highly parallel and distributed dynamical systems that can carry out information processing by means of their state response to input stimuli. These networks promises advantages in speed adaptability and fault tolerance resulting from their highly parallel architectures. In this paper feedforward and recurrent networks are reviewed. These networks consist of collections of simple processing elements and dense interconnections among the processors. The optical techniques are good candidates for proving the neural network Implementation needs specifically the heavy interconnections among the processors. Finally some Important optical neural networks are reviewed.