0000000000336396
AUTHOR
R. Gadea
Conceptual design of the TRACE detector readout using a compact, dead time-less analog memory ASIC
[EN] The new TRacking Array for light Charged particle Ejectiles (TRACE) detector system requires monitorization and sampling of all pulses in a large number of channels with very strict space and power consumption restrictions for the front-end electronics and cabling. Its readout system is to be based on analog memory ASICs with 64 channels each that sample a View the MathML source window of the waveform of any valid pulses at 200 MHz while discarding any other signals and are read out at 50 MHz with external ADC digitization. For this purpose, a new, compact analog memory architecture is described that allows pulse capture with zero dead time in any channel while vastly reducing the tota…
Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks
Aims: To study the ability of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape-based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (a(w)) and sub-inhibitory doses of the fungicide carbendazim. Methods and Results: A strain of A. carbonarius was cultured in a red grape juice-based medium. The input variables to the network were temperature (20-28 degrees C), a(w) (0 center dot 94-0 center dot 98), carbendazim level (0-450 ng ml(-1)) and time (3-15 days after the lag phase). The output of the ANNs was OTA level determined by liqui…
Implementing a Margolus Neighborhood Cellular Automata on a FPGA
Margolus neighborhood is the easiest form of designing Cellular Automata Rules with features such as invertibility or particle conserving. In this paper we introduce a notation to describe completely a rule based on this neighborhood and implement it in two ways: The first corresponds to a classical RAM-based implementation, while the second, based on concurrent cells, is useful for smaller systems in which time is a critical parameter. This implementation has the feature that the evolution of all the cells in the design is performed in the same clock cycle.
Position sensitive scintillator based detector improvements by means of an integrated front-end
PESIC is an integrated front-end for multianode photomultiplier based nuclear imaging devices. Its architecture has been designed to improve position sensitive detectors behavior by equalizing its response over its whole area. Its preamplying stage introduces two main benefits: digitally programmable gain adjustment for every photomultiplier output, and isolation from other front-end electronics by means of current buffers. This last feature allows to use different types of photomultipliers and optimizes front-end deadtime, reducing impact position dependent output delay. PESIC also includes an indirect measurement of the depth of interaction of the gamma ray inside the scintillator crystal…
Multiprocessor SoC Implementation of Neural Network Training on FPGA
Software implementations of artificial neural networks (ANNs) and their training on a sequential processor are inefficient because they do not take advantage of parallelism. ASIC and FPGA implementations employ specific hardware structures to exploit parallelism in order to improve processing speed; however, optimizing resource usage requires the use of fixed-point arithmetic, thereby losing precision, and the final system is restricted to a particular network topology. This paper presents a mixed approach based on a multiprocessor system-on-chip (SoC) on a FPGA. The use of software-driven embedded microprocessors with custom floating-point extensions for ANN related functions allows for gr…
DOI measurement with monolithic scintillation crystals: A primary performance evaluation
We report a first assessment of image quality enhancement achieved by the implementation of depth of interaction detection with monolithic crystals. The method of interaction depth measurement is based on analogue computation of the standard deviation with an enhanced charge divider readout. This technique of depth of interaction detection was developed in order to provide fast and determination of this parameter at a reasonable increase of detector cost. The detector consists of an large-sized monolithic scintillator coupled to a position sensitive photomultiplier tube. A special design feature is the flat-topped pyramidal shape of the crystal. This reduces image compression near the edges…
SoC-Based Implementation of the Backpropagation Algorithm for MLP
The backpropagation algorithm used for the training of multilayer perceptrons (MLPs) has a high degree of parallelism and is therefore well-suited for hardware implementation on an ASIC or FPGA. However, most implementations are lacking in generality of application, either by limiting the range of trainable network topologies or by resorting to fixed-point arithmetic to increase processing speed. We propose a parallel backpropagation implementation on a multiprocessor system-on-chip (SoC) with a large number of independent floating-point processing units, controlled by software running on embedded processors in order to allow flexibility in the selection of the network topology to be traine…
Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contaminated with Fusarium culmorum
The capacity of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict deoxynivalenol (DON) accumulation in barley seeds contaminated with Fusarium culmorum under different conditions has been assessed. Temperature (20-28 °C), water activity (0.94-0.98), inoculum size (7-15 mm diameter), and time were the inputs while DON concentration was the output. The dataset was used to train, validate and test many ANNs. Minimizing the mean-square error (MSE) was used to choose the optimal network. Single-layer perceptrons with low number of hidden nodes proved better than double-layer perceptrons, but the performance depended on the training …
Maximum likelihood positioning for gamma-ray imaging detectors with depth of interaction measurement
Abstract The center of gravity algorithm leads to strong artifacts for gamma-ray imaging detectors that are based on monolithic scintillation crystals and position sensitive photo-detectors. This is a consequence of using the centroids as position estimates. The fact that charge division circuits can also be used to compute the standard deviation of the scintillation light distribution opens a way out of this drawback. We studied the feasibility of maximum likelihood estimation for computing the true gamma-ray photo-conversion position from the centroids and the standard deviation of the light distribution. The method was evaluated on a test detector that consists of the position sensitive …
Neural network models for prediction of trichothecene content in wheat
Fusarium graminearum is a mould that causes serious diseases in cereals worldwide and that synthesises mycotoxins such as deoxynivalenol (DON), which can seriously affect human and animal health. Predicting the level of mycotoxin accumulation in food is very difficult, because of the complexity of the influencing parameters. In this work, we have studied the possibility of using artificial neural networks (NN) to predict DON level attained in F. graminearum wheat cultures taking as inputs the fungal contamination level of the cereal, the water activity as a measure of the available water for fungal growth in the cereal, the temperature and time. DON analysis was performed by gas chromatogr…
A Tool for Implementing and Exploring SBM Models: Universal 1D Invertible Cellular Automata
The easiest form of designing Cellular Automata rules with features such as invertibility or particle conserving is to rely on a partitioning scheme, the most important of which is the 2D Margolus neighborhood. In this paper we introduce a 1D Margolus-like neighborhood that gives support to a complete set of Cellular Automata models. We present a set of models called Sliding Ball Models based on this neighborhood and capable of universal computation. We show the way of designing logic gates with these models, propose a digital structure to implement them and finally we present SBMTool, a software development system capable of working with the new models.
PESIC: An Integrated Front-End for PET Applications
An ASIC front-end has been developed for multi-anode photomultiplier based nuclear imaging devices. Its architecture has been designed to improve resolution and decrease pile-up probability in Positron Emission Tomography systems which employ continuous scintillator crystals. Analog computation elements are isolated from the photomultiplier by means of a current sensitive preamplifier stage. This allows digitally programmable adjustment of every anode gain, also providing better resolution in gamma event position calculation and a shorter front-end deadtime. The preamplifier stage also offers the possibility of using other types of photomultiplier devices such as SiPM. The ASIC architecture…