1D Cellular Automata for Pulse Width Modulated Compressive Sampling CMOS Image Sensors

Compressive sensing (CS) is an alternative to the Shannon limit when the signal to be acquired is known to be sparse or compressible in some domain. Since compressed samples are non-hierarchical packages of information, this acquisition technique can be employed to overcome channel losses and restricted data rates. The quality of the compressed samples that a sensor can deliver is affected by the measurement matrix used to collect them. Measurement matrices usually employed in CS image sensors are recursive random-like binary matrices obtained using pseudo-random number generators (PRNG). In this paper we analyse the performance of these PRNGs in order to understand how their non-idealities affect the quality of the compressed samples. We present the architecture of a CMOS image sensor that uses class-III elementary cellular automata (ECA) and pixel pulse width modulation (PWM) to generate onchip a measurement matrix and high the quality compressed samples.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research N000141410355CONACYT (Mexico) MZO-2017-29106

Compressive image sensor architecture with on-chip measurement matrix generation

A CMOS image sensor architecture that uses a cellular automaton for the pseudo-random compressive sampling matrix generation is presented. The image sensor employs in-pixel pulse-frequency modulation and column wise pulse counters to produce compressed samples. A common problem of compressive sampling applied to image sensors is that the size of a full-frame compressive strategy is too large to be stored in an on-chip memory. Since this matrix has to be transmitted to or from the reconstruction system its size would also prevent practical applications. A full-frame compressive strategy generated using a 1-D cellular automaton showing a class III behavior neither needs a storage memory nor needs to be continuously transmitted. In-pixel pulse frequency modulation and up-down counters allow the generation of differential compressed samples directly in the digital domain where it is easier to improve the required dynamic range. These solutions combined together improve the accuracy of the compressed samples thus improving the performance of any generic reconstruction algorithm.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N00014141035

Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N000141410355European Union H2020 76586

Non-recursive method for motion detection from a compressive-sampled video stream

This paper introduces a non-recursive algorithm for motion detection directly from the analysis of compressed samples. The objective of this research is to create an algorithm able to detect, in real-time, the presence of moving objects over a fixed background from a compressive-sampled greyscale video stream. Many difficulties arise using this type of algorithm because it violates the fundamental principles of compressive sensing reconstruction that lie beneath traditional recursive methods. Recursive reconstruction methods even if accurate need large amounts of time and resources because they aim to retrieve all of the information contained within a scene. Our method is based on two key considerations. The first is that the targeted information of a moving element compared to a fixed background is really small. The second is an appropriate choice of a sub-Gaussian compressive sampling strategy. Our aim is to reduce the focus of general reconstruction in order to retrieve only objects of interest. This algorithm can be used to process compressed samples derived from a video stream with a speed of 100fps. This makes possible to detect the presence of moving objects directly from compressed samples with limited resources.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-R, IPT-2011-1625-430000, IPC-20111009 CDTIJunta de Andalucía TIC 2338–2013Office of Naval Research (USA) N00014141035

On the design of a sparsifying dictionary for compressive image feature extraction

Compressive sensing is an alternative to Nyquist-rate sampling when the signal to be acquired is known to be sparse or compressible. A sparse signal has a small number of nonzero components compared to its total length. This property can either exist either in the sampling domain, i. e. time or space, or with respect to a transform basis. There is a parallel between representing a signal in a compressed domain and feature extraction. In both cases, there is an effort to reduce the amount of resources required to describe a large set of data. A given feature is often represented by a set of parameters, which only acquire a relevant value in a few points in the image plane. Although there are some works reported on feature extraction from compressed samples, none of them considers the implementation of the feature extractor as a part of the sensor itself. Our approach is to introduce a sparsifying dictionary, feasibly implementable at the focal plane, which describes the image in terms of features. This allows a standard reconstruction algorithm to directly recover the interesting image features, discarding the irrelevant information. In order to validate the approach, we have integrated a Harris-Stephens corner detector into the compressive sampling process. We have evaluated the accuracy of the reconstructed corners compared to applying the detector to a reconstructed image.Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011-1625-430000, IPC-20111009Junta de Andalucía TIC 2338-2013Office of Naval Research (USA) N00014141035

Neural paraphrasing by automatically crawled and aligned sentence pairs

Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphrasing to make the conversation more natural, e.g., talking about a certain topic using different paraphrases in different time instants. Recently, the task of automatically generating paraphrases has been approached in the context of Natural Language Generation (NLG). While many existing systems simply consist in rule-based models, the recent success of the Deep Neural Networks in several NLG tasks naturally suggests the possibility of exploiting such networks for generating paraphrases. However, the main obstacle toward neural-network-based paraphrasing is the lack of large datasets with aligned pairs of sentences and paraphrases, that are needed to efficiently train the neural models. In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles. We propose a similarity search procedure with linguistic constraints that, given a reference sentence, is able to locate the most similar candidate paraphrases out from millions of indexed sentences. The data generation process is evaluated in the case of the Italian language, performing experiments using pointer-based deep neural architectures.Comment: The 6th International Conference on Social Networks Analysis, Management and Security (SNAMS 2019

Administration of Aloe arborescens homogenate to cattle: interaction with rumen fermentation and gut absorption of aloin

Aloe has long been used as a traditional medicine for its numerous beneficial properties, which are mainly ascribed to β-polysaccharides and phenolic compounds including anthraquinones, anthrones and chromones. However, few studies on large animals are currently available. The effect of whole leaf Aloe arborescens homogenate on the in vitro rumen fermentative processes was tested using alfalfa hay and barley meal as substrates. The Aloe homogeneate was added at three different concentrations (0.4, 2.0 and 10.0 g L−1 of fermentation liquid). The same homogenate was dosed (200 g) orally and through the rumen cannula to three rumen cannulated heifers and orally to six lactating dairy cows to measure the rumen degradation of aloin and the transfer of aloin from the gut into the blood, respectively. The Aloe homogenate did not affect in vitro rumen fermentations and feed digestibility. The administration of Aloe homogenate did not negatively affect animal feed intake and health neither on the cannulated heifers nor on the dairy cows. Aloin underwent a rapid degradation in the rumen milieu, and became undetectable 2 h after oral dosage. However, when Aloe homogenate was administered to dairy cows, aloin appeared in blood as early as 2 h after administration, reached a maximum after 4 h (6.2 ± 5.8 μg L−1) and progressively decreased thereafter. These results suggest that Aloe compounds can be absorbed into the blood and encourage the study of Aloe as a potential nutraceutical in ruminants. Further studies should determine the most effective in vivo dosage

A novel integrated industrial approach with cobots in the age of industry 4.0 through conversational interaction and computer vision

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future

A novel integrated industrial approach with cobots in the age of industry 4.0 through conversational interaction and computer vision

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future

Radiological Protection in Industries Involving NORM: A (Graded) Methodological Approach to Characterize the Exposure Situations

The interest in radiation protection in industrial sectors involving Naturally Occurring Radioactive Materials (NORM) is increasingly growing. This is due also to the recent implementation of the European Council Directive 59/2013/Euratom which in Italy and in the other European Union Member States extends the field of application to industrial sectors never involved before. This paper reports main results of a research project on radiation protection in industries involving NORM carried out in Italy aimed to provide useful tools for stakeholders to comply new legal obligations. The project activities were mainly focused on different aspects relevant to the NORM involving industries, accounting for the positive list reported in the Italian law. Firstly, the inventory of the industries currently operating in Italy in order to identify the industrial sectors with an important radiological impact on population and workers was updated. Based on this information, a general methodology was elaborated taking into account a graded approach. The first phase consists in the identification and characterization of the most critical exposure scenarios and of the radiological content of NORMs involved in the different phases of the industrial processes. In the second phase calculation methods were developed for dose estimation for workers and members of public. These tools require the use of existing and well tested calculation codes, and the development of a dedicated user-friendly software