Reprogrammable Microelectromechanical Membrane Resonator for Logic Computation

Precision and grade are two important quantitative indexes. The purpose of this paper is to combine precision and grade, and explore new extended rough set model. Based on logical OR operation of precision and grade, this paper proposes model of logical OR operation of variable precision lower approximation operator and grade upper approximation operator. In the new model, fundamental structure and properties are obtained by transformation formulas between variable precision approximations and graded approximations, furthermore regular algorithm and structural algorithm are proposed and analyzed. Finally a practical example is given to illustrate the model and the algorithms. The new model has extended variable precision rough set model, graded rough set model and classical rough set model, and so these models have obtained similar properties of approximation operators.

This paper is to construct new operation of approximation operators, and explore its calculation. First it proposes logical OR operation of variable precision upper approximation operator and grade lower approximation operator. Then regular algorithm and structural algorithm are proposed and analyzed, and furthermore it obtains an important conclusion that structural algorithm has advantages in space complexity. Finally the new operation and its algorithms are explained by a medical example.

Besides storage information, memristor can also be used in logic operation. Although IMPLY and CLEAR based on memristor are logic completeness, there would be more steps when representing AND and OR operation which are more commonly used. Based on this, the paper presents a feasible approach to implement OR logic, and implements OR logic in NOR memory array based on memristors. Utilizing the advantage of parallel computing, NOR memory array based on memristors can greatly improve the speed of logic operation, and offering a new method for the combination of storage and operation.

Bioinspired artificial ionic components are extensively utilized to mimic biological systems, as the vast majority of biological signaling is mediated by ions and molecules. Particular attention is given to nanoscale fluidic components where the ion transport can be regulated by the induced ion permselectivity. As a step from fundamentals toward ion-controlled devices, this study presents the use of ionic diodes made of oppositely charged polyelectrolytes, as a gate for low-abundance molecules. The use of ionic diodes that exhibited nonlinear current-voltage responses enabled realization of a basic Boolean operation of an ionic OR logic gate. Aside from the electrical response, the asymmetric ion transport through the diode was shown to affect the transport of low-abundance molecules across the diode, only allowing crossing when the diode was forward-biased. Integration of multiple diodes enabled implementation of an OR logic operation on both the voltage and the molecule transport, while obtaining electrical and optical output readouts that were associated with low and high logic levels. Similarly to electronics, implementation of logic gates opens up new functionalities of on-chip ionic computation via integrated circuits consisting of multiple basic logic gates.

A mathematical morphology-based computer-aided detection (CAD) scheme for the identification of clustered microcalcifications was developed and tested. The potential for improving either sensitivity or specificity by combining the results with those previously reported was investigated. The CAD scheme presented here is based on mathematical morphology and a series of simple rule-based criteria for the identification of clustered microcalcifications. A database of 105 digitized mammograms was used for training and rule setting of the scheme. A test set of 191 digitized mammograms was used to evaluate its performance. The same test set had been used to evaluate a multilayer, topography-based scheme. The results obtained by the two schemes were then combined using logical OR and AND operations. The morphology-based and topography-based CAD schemes performed at sensitivities of 82.9% and 89.5%, with false-positive detection rates of 1.3 and 0.4 per image, respectively. A logical OR operation resulted in 95.4% sensitivity. An AND operation achieved 76.2% sensitivity, with no false identifications on 93% of images. By combining the results of the morphology-based and the topography-based schemes, either sensitivity or specificity can be improved.

Optics has already proved its strong potentiality for conducting parallel logic, arithmetic and algebraic operations. Since last few decades so many all-optical data processors were proposed. To implement these processors different encoding / decoding techniques, such as polarization encoding, intensity encoding, phase encoding technique have been reported. Very recently ‘frequency encoding’ technique have drawn a special interest to the scientific community. The frequency is the fundamental character of a signal and it preserves its identity irrespective of reflection, refraction, transmission, absorption throughout the communication. Therefore, if two specific frequencies are encoded as 1 state and 0 states respectively, then one can ensure about the state of information throughout the communications. In this paper the authors propose a method of conducting inversion OR, NOR and NAND logic operations exploiting the wavelength conversion, which is achieved by exploiting the cross gain modulation (XGM) property of reflecting semiconductor optical amplifier (RSOA), as well as the property of change of nonlinear rotation of the state of polarization of probe beam by cross phase modulation (XPM). Both the wavelength conversion properties are controlled by the intensity of the pump beam. The major advantage of SOA-switching over any other conventional switching is that SOA-switching require low power with good on off contrast ratio. Again as the NAND and NOR are the universal logic gates, so any other logic operation can be implemented using this concept.

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

We discuss how the idea of unicast 1+1 protection can be efficiently extended to protect multicast connections in optical backbone networks. Particularly, we show how to achieve instantaneous failure recovery and cost efficiency by allowing intermediate nodes to merge their incoming flows by a simple network code, i.e., logical OR operation. Under simple network coding, the problem of minimum cost multicast 1+1 protection is formulated as a 2-connectivity problem. In order to solve this problem, an optimal ILP and three efficient heuristic algorithms are proposed. Simulation results on real-world networks show that the average cost of our best heuristic algorithm is only 2.6 % higher compared to the optimal ILP solution.

A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary preprocessing step in the computerized analysis of thoracic MR images. This task is complicated by potentially significant cardiac and pulmonary motion artifacts, partial volume effect, and morphological deformation from disease. We have developed an automated segmentation method to account for these complications. First, the thorax is segmented using a threshold obtained from analysis of the cumulative gray-level histogram constructed along a diagonal line through the center of the image. Next two separate lung-thresholded images are created. The first lung-thresholded image is created using histogram-based gray-level thresholding techniques applied to the segmented thorax. To include lung areas that may be adversely affected by artifact or disease, a second lung-thresholded image is created by applying a grayscale erosion operator to the first lung-thresholded image. After a rolling ball filter is applied to the lung contour to eliminate non-lung pixels from the thresholded lung regions, a logical OR operation is used to combine the two lung-thresholded images into the final segmented lung regions. Modifications to this approach were required to properly segment sections in the lung bases. In a preliminary evaluation, the automated method was applied to 10 MR scans, an observer evaluated the segmented lung regions using a five-point scale ("highly accurate segmentation" to "highly inaccurate segmentation"). Eighty-five percent of the segmented lung regions were rated as highly or moderately accurate.

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.

A cis-regulatory module often processes multiple regulatory inputs provided by transcription factors that bind to DNA and regulate the expression of the gene in a combinatorial logic manner. Here the authors analyse dynamic cross correlations between the regulator concentrations and the output of the gene using stochastic modelling, and show their characteristics for different logic operations. The authors find that the dynamic cross-correlation curve with respect to the correlation time near the peak close to the point of the zero correlation time is upwards convex in the case of AND logic whereas downwards convex in the case of OR logic, where intrinsic and extrinsic noise plays a distinct role. This result implies that the cross-correlation function is a useful and robust index for distinguishing super- and sub-additive gene regulatory scenarios (molecular analogues of AND and OR logic operations) that faithfully represent gene expression.

A scheme of developing frequency encoded tristate logic operations exploiting nonlinear character of PPLN waveguide and RSOA

This paper explores the design and performance evaluation of an all-optical combinational logic network. The proposed optical digital link generates AND logic operation along with exclusive OR logic operations concurrently in a single unit. Numerical simulation for the applied data inputs and consequence output has been successfully accomplished. Accordingly, desired outputs for the combinational network—the AND logic, the exclusive OR logic—have been verified at the 20 Gbps of data rate. Numerical simulations for the different key design parameters, for instance, current injection efficiency and pump current of semiconductor optical amplifier and the variation in the operating data rates up to 20 and 50 Gbps have been carried out. It has showed very good extinction ratio (>10 dB) performance. The proposed design is compact and avoids frequent optoelectronic conversions, and it is well supportive to design forthcoming all-optical complex computing networks.

Loop-recognition dual-hairpin competitive probes enabling a dual-ratiometric electrochemical platform for specific SNP discrimination and logic gate construction.