In this paper, a tool based on free software to perform low level optimization on analog designs is presented. Nowadays, the use of design automation tools for microelectronic circuits design is extending from digital to analog circuits, due in part to the fact that although the analog part of a mixed signal ASIC takes only the 10% of the silicon area, it represents almost 90% of the whole design time. For analog circuits, design process can be divided in two major tasks: topology selection and device sizing. The tool here presented consists on a simulation based optmizh is used to perform automatic low level analog circuit sizing. The tool is composed of three modules: a layout generator, which includes a parasitic extractor, an alaog circuit simulator and a circuit optimizer. The two first modules are respectively Magic and Spice from Berkeley, while the third one, the optimizer, has been developed to evaluate dc, ac, and transient sensitivity simulations performed by Spice and make corrections on the layout sizing. Optimization process starts with a certain topology and standard sized devices, which is then extracted by Magic and simulated by Spice. Performance is evaluated and a sizing correction is proposed. These simulation and corrections are done on an iterative loop until circuit performance reaches design parameters. The tool is demonstrated with an example of a simple analog subcircuit optimization, where parameters like silicon area or power dissipation are optimized, while the circuit keeps on design parameters.
Recently, neuromodeling methods of microwave devices have been developed. These methods are suitable for the model generation of novel devices. They allow fast and accurate simulations and optimizations. However, the development of libraries makes these methods to be a formidable task, since they require massive input-output data provided by an electromagnetic simulator or measurements and repeated artificial neural network (ANN) training. This paper presents a strategy reducing the cost of library development with the advantages of the neuromodeling methods: high accuracy, large range of geometrical and material parameters and reduced CPU time. The library models are developed from a set of base prior knowledge input (PKI) models, which take into account the characteristics common to all the models in the library, and high-level ANNs which give the library model outputs from base PKI models. This technique is illustrated for a microwave multiconductor tunable phase shifter using anisotropic substrates. Closed-form relationships have been developed and are presented in this paper. The results show good agreement with the expected ones.
KEYWORDS: Heart, Signal detection, Signal processing, Algorithm development, Detection and tracking algorithms, Signal analyzers, Linear filtering, Pathology, Data acquisition, Human-machine interfaces
The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.
KEYWORDS: Digital signal processing, Bismuth, Signal processing, Algorithm development, Image segmentation, Image processing, Detection and tracking algorithms, Evolutionary algorithms, Parallel computing, Surgery
In first place, in this paper, the basic process of parallel code
implementation is discussed for a VLIW architecture. Parallel code modules allow the implementation of a contour active (snake) for segmentation and tracking of endocardium in echocardiographic images. In second place, this work discusses the performance obtained through this design model. In this case, it is necessary to
check performances in order to obtain a qualitative and quantitative
measurement of our implementation. We have chosen one example which permits to understand the methodology used in order to obtain the maximum performance of hardware features of VLIW processor: the distance between points of active contour, very used in different modules composing the active contour algorithm.
The detection of buried landmines is an important problem in
regions where an army conflict has occurred. In particular, antipersonnel plastic mines cannot be detected with classical techniques, such as metal detectors. So a very promising detection technique based on a thermal model of the soil is applied to detect this kind of mines, in which infrared (IR) images of the soil are used. The core of this technique is the solution of the heat transfer process in the soil and at the soil-air interface, which is a very time consuming process. To overcome this problem we propose an analog circuit which can solve the equations that model the system reducing time cost by taking advantage of the inherent massive parallelism of the circuit. The description of the equations is made with VHDL--AMS and then an automatic synthesis tool generates a circuit which solves the equations.
This paper presents an analog CMOS implementation of a neural
network based on a spinal cord model. The network is comprised by three pairs of cells, Alpha motorneurons, Interneurons and Renshaw cells, which form the basic control motor system for a single limb movement. Behaviour of each neuron is described by a differential equation, which provides it with a dynamic performance. This network is useful to control limb movements based in an antagonist pair of
actuators, i.e. muscles for a human limb or electric motors or SMA fibers for machine applications. This antagonist structure has the main advantage that allows independent control of limb position and stiffness, which makes it suitable for applications where inertial load compensation is a critical factor. For the implementation of the neurons we have developed individual analog operators, like multipliers and integrators, which have been then joined to obtain the cell. The whole circuit works in current mode, and exhibits good
performance in power disipation and bandwidth. The implementation of the network has been done in a 0.35um process from AMS. The layout size is 870 × 480 μm and the power dissipation is 14 mW, using a reference voltage of 3.3 volts. The applications in which this network canbe used fall in two broad cathegories. Firstly, in the development of human-machine interfaces capable to be used both in industry and in handicaped people and secondly in the development o neural controller for industrial robots, providing them with a compliance performance.
This paper describes implementation of neural network processing layers using basic current-mode operating modules. The research work has been focused on the implementation of neural networks based on the Adaptive Resonance Theory, developed by S. Grossberg and G.A. Carpenter. The ART-based neural network whose operating modules have been choosen for development is the one called MART, proposed by
F. Delgado, because of its complex architecture, auto--adaptive self-learning process, able to discard unmeaningful cathegories. Our presentation starts introducing the behaviour of MART with an analysis of its structure. The development described by this research work is focused on the monochannel block included in the main signal
processing part of the MART neural network. The description of the computing algorithm of the layers inside a monochannel block are also provided in order to show what operational current-mode modules are
needed (multiplier, divider, square-rooter, adder, substractor, absolute value, maximum and minimum evaluator...). Descriptions at schematic and layout levels of all the processing layers are given. All of them have been designed using AMS 0.35 micron technology with a supply voltage of 3.3 volts. The modules are designed to deal with input currents in the range of 20 to 50 microamps, showing a lineal behaviour and an output error of less than 10%, which is good enough for neural signal processing systems. The maximum frecuency of operation is around 200 kHz. Simulation results are included to show that the operation performed by the hardware designed matches the behaviour described by the MART neural network. For testing purposes we show the design of a monochannel block hardware implementation restricted to five inputs and three cathegories.
In this paper a high-speed procedure for motion tracking of endocardium is presented. If a contour is available in the first frame of a sequence, the contours on the subsequent frames are segmented. Deformable active contours is a technique that combine geometry, physics and approximation theory to solve problems with fundamental important into medical image analysis like segmentation, representation and matching of shapes, and tracking of objects in movement. The procedure was implemented on a DSP processor (TMS320C6701) using its hardware characteristics massively. The results are illustrated on a sequence of four-chambers apical images.
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