A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

DESEO: an active vision system for detection, tracking and recognition

[EN]In this paper, a basic conceptual architecture aimed at the design of Computer Vision System is qualitatively described. The proposed architecture addresses the design of vision systems in a modular fashion using modules with three distinct units or components: a processing network or diagnostics unit, a control unit and a communications unit. The control of the system at the modules level is designed based on a Discrete Events Model. This basic methodology has been used to design a realtime active vision system for detection, tracking and recognition of people. It is made up of three functional modules aimed at the detection, tracking, recognition of moving individuals plus a supervision module.

An active vision system integrating fast and slow processes

[EN]This paper describes an Active Vision System whose design assumes a distinction between fast or reactive and slow or background processes. Fast processes need to operate in cycles with critical timeouts that may affect system stability. While slow processes, though necessary, do not compromise system stability if its execution is delayed. Based on this simple taxonomy, a control architecture has been proposed and a prototype implemented that is able to track people in real-time with a robotic head while trying to identify the target. In this system, the tracking module is considered as the reactive part of the system while person identification is considered a background task.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

Geographical Distribution Of The Association Between El Niño South Oscillation And Dengue Fever In The Americas: A Continental Analysis Using Geographical Information System-based Techniques.

El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20(th) century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AIy) by country was computed using Pan-American Health Association data. SOI and AIy values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AIy (rSOI,AI) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6° 31' S in 1995 to 21° 12' S in 1999 and the rSOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The rSOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

Human Herpesvirus Infections Of The Central Nervous System: Laboratory Diagnosis Based On Dna Detection By Nested Pcr In Plasma And Cerebrospinal Fluid Samples.

Infections of the central nervous systems (CNS) present a diagnostic problem for which an accurate laboratory diagnosis is essential. Invasive practices, such as cerebral biopsy, have been replaced by obtaining a polymerase chain reaction (PCR) diagnosis using cerebral spinal fluid (CSF) as a reference method. Tests on DNA extracted from plasma are noninvasive, thus avoiding all of the collateral effects and patient risks associated with CSF collection. This study aimed to determine whether plasma can replace CSF in nested PCR analysis for the detection of CNS human herpesvirus (HHV) diseases by analysing the proportion of patients whose CSF nested PCR results were positive for CNS HHV who also had the same organism identified by plasma nested PCR. In this study, CSF DNA was used as the gold standard, and nested PCR was performed on both types of samples. Fifty-two patients with symptoms of nervous system infection were submitted to CSF and blood collection. For the eight HHV, one positive DNA result-in plasma and/or CSF nested PCR-was considered an active HHV infection, whereas the occurrence of two or more HHVs in the same sample was considered a coinfection. HHV infections were positively detected in 27/52 (51.9%) of the CSF and in 32/52 (61.5%) of the plasma, difference not significant, thus nested PCR can be performed on plasma instead of CSF. In conclusion, this findings suggest that plasma as a useful material for the diagnosis of cases where there is any difficulty to perform a CSF puncture.

Single reaction interface flow system for chemiluminescent monitoring of mannitol based on its hydroxyl radical scavenger activity

A single reaction interface flow analysis (SIFA) system for the monitoring of mannitol in pharmaceutical formulations and human urine is presented. The developed approach takes advantage of the mannitol scavenger aptitude to inhibit the chemiluminescent reaction between luminol and myoglobin in the absence of H(2)O(2). The SIFA system facilitated the fully automation of the developed methodology, allowing the in-line reproducible handling of chemical species with a very short lifetime as is the case of the hydroxyl radical generated in the abovementioned luminol/myoglobin reaction. The proposed methodology allowed the determination of mannitol concentrations between 25 mmol L(-1) and 1 mol L(-1), with good precision (R.S.D. < 4.7%, n = 3) and a sampling frequency of about 60 h(-1). The procedure was applied to the determination of mannitol in pharmaceuticals and in human urine samples Without any pretreatment process. The results obtained for pharmaceutical formulations were statistically comparable to those provided by the reference method (R.D. < 4.6%); recoveries values obtained in the analysis of spiked urine samples (between 94.9 and 105.3% of the added amount) were also satisfactory. (C) 2008 Elsevier B.V. All rights reserved.

Embedded DSP-Based Compact Fuzzy System and Its Application for Induction-Motor V/f Speed Control

This paper presents a compact embedded fuzzy system for three-phase induction-motor scalar speed control. The control strategy consists in keeping constant the voltage-frequency ratio of the induction-motor supply source. A fuzzy-control system is built on a digital signal processor, which uses speed error and speed-error variation to change both the fundamental voltage amplitude and frequency of a sinusoidal pulsewidth modulation inverter. An alternative optimized method for embedded fuzzy-system design is also proposed. The controller performance, in relation to reference and load-torque variations, is evaluated by experimental results. A comparative analysis with conventional proportional-integral controller is also achieved.

Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Considering the increasing popularity of network-based control systems and the huge adoption of IP networks (such as the Internet), this paper studies the influence of network quality of service (QoS) parameters over quality of control parameters. An example of a control loop is implemented using two LonWorks networks (CEA-709.1) interconnected by an emulated IP network, in which important QoS parameters such as delay and delay jitter can be completely controlled. Mathematical definitions are provided according to the literature, and the results of the network-based control loop experiment are presented and discussed.

MODELING OF AN INFORMATION SYSTEM FOR WINE TRACEABILITY BASED ON A SERVICE ORIENTED ARCHITECTURE

The purpose is to present a scientific research that led to the modeling of an information system which aimed at the maintenance of traceability data in the Brazilian wine industry, according to the principles of a service-oriented architecture (SOA). Since 2005, traceability data maintenance is an obligation for all producers that intend to export to any European Union country. Also, final customers, including the Brazilian ones, have been asking for information about food products. A solution that collectively contemplated the industry was sought in order to permit that producer consortiums of associations could share the costs and benefits of such a solution. Following an extensive bibliographic review, a series of interviews conducted with Brazilian researchers and wine producers in Bento Goncalves - RS, Brazil, elucidated many aspects associated with the wine production process. Information technology issues related to the theme were also researched. The software was modeled with the Unified Modeling Language (UML) and uses web services for data exchange. A model for the wine production process was also proposed. A functional prototype showed that the adopted model is able to fulfill the demands of wine producers. The good results obtained lead us to consider the use of this model in other domains.

A safety assessment methodology applied to CNS/ATM-based air traffic control system

In the last decades, the air traffic system has been changing to adapt itself to new social demands, mainly the safe growth of worldwide traffic capacity. Those changes are ruled by the Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) paradigm, based on digital communication technologies (mainly satellites) as a way of improving communication, surveillance, navigation and air traffic management services. However, CNS/ATM poses new challenges and needs, mainly related to the safety assessment process. In face of these new challenges, and considering the main characteristics of the CNS/ATM, a methodology is proposed at this work by combining ""absolute"" and ""relative"" safety assessment methods adopted by the International Civil Aviation Organization (ICAO) in ICAO Doc.9689 [14], using Fluid Stochastic Petri Nets (FSPN) as the modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed (in analysis) and the legacy system models. To demonstrate its usefulness, the proposed methodology was applied to the ""Automatic Dependent Surveillance-Broadcasting"" (ADS-B) based air traffic control system. As conclusions, the proposed methodology assured to assess CNS/ATM system safety properties, in which FSPN formalism provides important modeling capabilities, and discrete event simulation allowing the estimation of the desired safety metric. (C) 2011 Elsevier Ltd. All rights reserved.

Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

An efficient expert system for the power transformer condition assessment is presented in this paper. Through the application of Duval`s triangle and the method of the gas ratios a first assessment of the transformer condition is obtained in the form of a dissolved gas analysis (DGA) diagnosis according IEC 60599. As a second step, a knowledge mining procedure is performed, by conducting surveys whose results are fed into a first Type-2 Fuzzy Logic System (T2-FLS), in order to initially evaluate the condition of the equipment taking only the results of dissolved gas analysis into account. The output of this first T2-FLS is used as the input of a second T2-FLS, which additionally weighs up the condition of the paper-oil system. The output of this last T2-FLS is given in terms of words easily understandable by the maintenance personnel. The proposed assessing methodology has been validated for several cases of transformers in service. (C) 2010 Elsevier Ltd. All rights reserved.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.