In this paper, a potential biological structure for computer volumetric memory is investigated. Bacteriorhodopsin is a protein found in the purple membrane of the bacteria Halobacterium salinarum. This protein has lig...
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Digital Multiplexers and Analog multiplexers, through the use of filters and their applications implemented in frequency domain, which requires extensive hardware and is costly have seen substantial amount of research...
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Digital Multiplexers and Analog multiplexers, through the use of filters and their applications implemented in frequency domain, which requires extensive hardware and is costly have seen substantial amount of research compared to an analog multiplexer in time domain. This Paper Presents design, Simulation, Implementation and Evaluation of a four-to-one analog multiplexer. The analog multiplexer is implemented with the help of four buffers and four transmission gates using 0.35 μm CMOS Technology with optimum linear ranges that covers 60% of total input supply voltage. The analog multiplexer is developed for a biochemical preprocessing unit to multiplex time-varying analog input signals coming from real-time sensor arrays and to connect the selected input to one A/D converter for digital implementation of a bio-inspired intelligent signal detection system or to an analog implemented biochemical detection system.
A proof of correctness of a given VLSIC systemdesign is established by proving the consistency and implication of the implemented design with respect to the specified design. The set of conditions that establish such...
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Intelligent Information Processing (IIP) or the smart processing of signals in communication systems and data measurements from multi-sensor systems are needed for advanced micro autonomous applications. A balanced co...
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Intelligent Information Processing (IIP) or the smart processing of signals in communication systems and data measurements from multi-sensor systems are needed for advanced micro autonomous applications. A balanced combination of efficient algorithms, fast networks, and collaboration of the different technologies are required for smaller, faster, and more efficient system-on-a-chip applications. In this paper we present guidelines/approach for intelligent information processing using Neural Networks (NNs) and Genetic Algorithms (GAs) which are capable of learning through discovery and/or reinforcement with features optimization through chromosome mutations of GAs. Specific details about a special application for Electronic-Nose (EN) implementation to discriminate among four chemicals, using reinforcement NN implemented tiny-chip and a GA system implementation is presented with test results.
Although there are many simulated Genetic Algorithms (GAs) in applications, less research has been directed toward their practical hardware implementations. In this paper we present a GA for optimum sensors-measuremen...
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Although there are many simulated Genetic Algorithms (GAs) in applications, less research has been directed toward their practical hardware implementations. In this paper we present a GA for optimum sensors-measurement fusion and characteristics weights. A multilevel verification of the GA is performed via the Mentor Graphics design Architect (DA) and ModelSim CAD tools. In particular the design of efficient universal multipliers, dividers, and their integrated circuits is addressed. Effective mutation and crossover apprache has been implemented in the GA system operation. It requires 960 clock cycles for complete iteration of 64 chromosomes, each with 3 genes of two binary-bits. This requires only 12 μsec when implemented in the 0.25 μm CMOS technology. The GA system is developed for a preprocessing unit to select optimal weights from real-time sensors measurement, and for fused measurements as in electronic nose, integrated accelerometer systems, and for performance enhancement of recurrent dynamic neural networks in noisy environments. The proposed approach, simulation results, and possible experimental results will be presented.
An operational transconductance amplifier (OTA) using three cascaded comparator stages is presented in 180nm Fully Depleted Silicon On Insulator (FDSOI) technology processed at MIT Lincoln laboratory. Each comparator ...
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This paper introduces an enhanced Genetic Algorithm GA that is faster and more efficient than the standard one. A simple 3D convex surface is optimized using different methods, Brute-Force, standard GA, and the enhanc...
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A sixteen-channel linear phased array radar is calibrated by means of Artificial Neural Network (ANN). Limited data are used to train the various layers of the network, for Angle of Arrival (AOA) determinacy. This is ...
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Based on our work [1] we present a study for dimensions (number of FBG sections) in our novel APU-BA. C-band single-wavelength, and eye-safe dual-wavelength Triangular Spectrum Fiber Bragg Gratings (TS-FBG) of chosen ...
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Advanced microsystems that include, sensors, interface-circuits, and pattern-recognition integrated monolithically or in a hybrid module are needed for civilian, military, and space applications. These include: automo...
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Advanced microsystems that include, sensors, interface-circuits, and pattern-recognition integrated monolithically or in a hybrid module are needed for civilian, military, and space applications. These include: automotive, medical applications, environmental engineering, and manufacturing automation. ASICs with Artificial Neural Networks (ANN) are considered in this paper, with the objective of recognizing air-borne volatile organic compounds, especially alcohols, ethers, esters, halocarbons, NH/sub 3/, NO/sub 2/, and other warfare agent simulants. The ASIC inputs are connected to the outputs from array-distributed sensors which measure three-features for identifying each of four chemicals. A Specialized Reinforcement Neural Network (RNN) learning approach is chosen for the chemicals classification problem. Hardware implementation of the RNN is presented for 2 /spl mu/m CMOS process, MOSIS chip. design implementation and evaluation are also presented.
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