Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

Optimization of a multielectrode array (MEA)-based approach to study the impact of Aβ on the SH-SY5Y cell line

The human brain stores, integrates, and transmits information recurring to millions of neurons, interconnected by countless synapses. Though neurons communicate through chemical signaling, information is coded and conducted in the form of electrical signals. Neuroelectrophysiology focus on the study of this type of signaling. Both intra and extracellular approaches are used in research, but none holds as much potential in high-throughput screening and drug discovery, as extracellular recordings using multielectrode arrays (MEAs). MEAs measure neuronal activity, both in vitro and in vivo. Their key advantage is the capability to record electrical activity at multiple sites simultaneously. Alzheimer’s disease (AD) is the most common neurodegenerative disease and one of the leading causes of death worldwide. It is characterized by neurofibrillar tangles and aggregates of amyloid-β (Aβ) peptides, which lead to the loss of synapses and ultimately neuronal death. Currently, there is no cure and the drugs available can only delay its progression. In vitro MEA assays enable rapid screening of neuroprotective and neuroharming compounds. Therefore, MEA recordings are of great use in both AD basic and clinical research. The main aim of this thesis was to optimize the formation of SH-SY5Y neuronal networks on MEAs. These can be extremely useful for facilities that do not have access to primary neuronal cultures, but can also save resources and facilitate obtaining faster high-throughput results to those that do. Adhesion-mediating compounds proved to impact cell morphology, viability and exhibition of spontaneous electrical activity. Moreover, SH-SY5Y cells were successfully differentiated and demonstrated acute effects on neuronal function after Aβ addition. This effect on electrical signaling was dependent on Aβ oligomers concentration. The results here presented allow us to conclude that the SH-SY5Y cell line can be successfully differentiated in properly coated MEAs and be used for assessing acute Aβ effects on neuronal signaling.

Microfluidic platforms for the characterization of in meso membrane protein crystallization

Membrane proteins, which reside in the membranes of cells, play a critical role in many important biological processes including cellular signaling, immune response, and material and energy transduction. Because of their key role in maintaining the environment within cells and facilitating intercellular interactions, understanding the function of these proteins is of tremendous medical and biochemical significance. Indeed, the malfunction of membrane proteins has been linked to numerous diseases including diabetes, cirrhosis of the liver, cystic fibrosis, cancer, Alzheimer's disease, hypertension, epilepsy, cataracts, tubulopathy, leukodystrophy, Leigh syndrome, anemia, sensorineural deafness, and hypertrophic cardiomyopathy.1-3 However, the structure of many of these proteins and the changes in their structure that lead to disease-related malfunctions are not well understood. Additionally, at least 60% of the pharmaceuticals currently available are thought to target membrane proteins, despite the fact that their exact mode of operation is not known.4-6 Developing a detailed understanding of the function of a protein is achieved by coupling biochemical experiments with knowledge of the structure of the protein. Currently the most common method for obtaining three-dimensional structure information is X-ray crystallography. However, no a priori methods are currently available to predict crystallization conditions for a given protein.7-14 This limitation is currently overcome by screening a large number of possible combinations of precipitants, buffer, salt, and pH conditions to identify conditions that are conducive to crystal nucleation and growth.7,9,11,15-24 Unfortunately, these screening efforts are often limited by difficulties associated with quantity and purity of available protein samples. While the two most significant bottlenecks for protein structure determination in general are the (i) obtaining sufficient quantities of high quality protein samples and (ii) growing high quality protein crystals that are suitable for X-ray structure determination,7,20,21,23,25-47 membrane proteins present additional challenges. For crystallization it is necessary to extract the membrane proteins from the cellular membrane. However, this process often leads to denaturation. In fact, membrane proteins have proven to be so difficult to crystallize that of the more than 66,000 structures deposited in the Protein Data Bank,48 less than 1% are for membrane proteins, with even fewer present at high resolution (< 2Å)4,6,49 and only a handful are human membrane proteins.49 A variety of strategies including detergent solubilization50-53 and the use of artificial membrane-like environments have been developed to circumvent this challenge.43,53-55 In recent years, the use of a lipidic mesophase as a medium for crystallizing membrane proteins has been demonstrated to increase success for a wide range of membrane proteins, including human receptor proteins.54,56-62 This in meso method for membrane protein crystallization, however, is still by no means routine due to challenges related to sample preparation at sub-microliter volumes and to crystal harvesting and X-ray data collection. This dissertation presents various aspects of the development of a microfluidic platform to enable high throughput in meso membrane protein crystallization at a level beyond the capabilities of current technologies. Microfluidic platforms for protein crystallization and other lab-on-a-chip applications have been well demonstrated.9,63-66 These integrated chips provide fine control over transport phenomena and the ability to perform high throughput analyses via highly integrated fluid networks. However, the development of microfluidic platforms for in meso protein crystallization required the development of strategies to cope with extremely viscous and non-Newtonian fluids. A theoretical treatment of highly viscous fluids in microfluidic devices is presented in Chapter 3, followed by the application of these strategies for the development of a microfluidic mixer capable of preparing a mesophase sample for in meso crystallization at a scale of less than 20 nL in Chapter 4. This approach was validated with the successful on chip in meso crystallization of the membrane protein bacteriorhodopsin. In summary, this is the first report of a microfluidic platform capable of performing in meso crystallization on-chip, representing a 1000x reduction in the scale at which mesophase trials can be prepared. Once protein crystals have formed, they are typically harvested from the droplet they were grown in and mounted for crystallographic analysis. Despite the high throughput automation present in nearly all other aspects of protein structure determination, the harvesting and mounting of crystals is still largely a manual process. Furthermore, during mounting the fragile protein crystals can potentially be damaged, both from physical and environmental shock. To circumvent these challenges an X-ray transparent microfluidic device architecture was developed to couple the benefits of scale, integration, and precise fluid control with the ability to perform in situ X-ray analysis (Chapter 5). This approach was validated successfully by crystallization and subsequent on-chip analysis of the soluble proteins lysozyme, thaumatin, and ribonuclease A and will be extended to microfluidic platforms for in meso membrane protein crystallization. The ability to perform in situ X-ray analysis was shown to provide extremely high quality diffraction data, in part as a result of not being affected by damage due to physical handling of the crystals. As part of the work described in this thesis, a variety of data collection strategies for in situ data analysis were also tested, including merging of small slices of data from a large number of crystals grown on a single chip, to allow for diffraction analysis at biologically relevant temperatures. While such strategies have been applied previously,57,59,61,67 they are potentially challenging when applied via traditional methods due to the need to grow and then mount a large number of crystals with minimal crystal-to-crystal variability. The integrated nature of microfluidic platforms easily enables the generation of a large number of reproducible crystallization trials. This, coupled with in situ analysis capabilities has the potential of being able to acquire high resolution structural data of proteins at biologically relevant conditions for which only small crystals, or crystals which are adversely affected by standard cryocooling techniques, could be obtained (Chapters 5 and 6). While the main focus of protein crystallography is to obtain three-dimensional protein structures, the results of typical experiments provide only a static picture of the protein. The use of polychromatic or Laue X-ray diffraction methods enables the collection of time resolved structural information. These experiments are very sensitive to crystal quality, however, and often suffer from severe radiation damage due to the intense polychromatic X-ray beams. Here, as before, the ability to perform in situ X-ray analysis on many small protein crystals within a microfluidic crystallization platform has the potential to overcome these challenges. An automated method for collecting a "single-shot" of data from a large number of crystals was developed in collaboration with the BioCARS team at the Advanced Photon Source at Argonne National Laboratory (Chapter 6). The work described in this thesis shows that, even more so than for traditional structure determination efforts, the ability to grow and analyze a large number of high quality crystals is critical to enable time resolved structural studies of novel proteins. In addition to enabling X-ray crystallography experiments, the development of X-ray transparent microfluidic platforms also has tremendous potential to answer other scientific questions, such as unraveling the mechanism of in meso crystallization. For instance, the lipidic mesophases utilized during in meso membrane protein crystallization can be characterized by small angle X-ray diffraction analysis. Coupling in situ analysis with microfluidic platforms capable of preparing these difficult mesophase samples at very small volumes has tremendous potential to enable the high throughput analysis of these systems on a scale that is not reasonably achievable using conventional sample preparation strategies (Chapter 7). In collaboration with the LS-CAT team at the Advanced Photon Source, an experimental station for small angle X-ray analysis coupled with the high quality visualization capabilities needed to target specific microfluidic samples on a highly integrated chip is under development. Characterizing the phase behavior of these mesophase systems and the effects of various additives present in crystallization trials is key for developing an understanding of how in meso crystallization occurs. A long term goal of these studies is to enable the rational design of in meso crystallization experiments so as to avoid or limit the need for high throughput screening efforts. In summary, this thesis describes the development of microfluidic platforms for protein crystallization with in situ analysis capabilities. Coupling the ability to perform in situ analysis with the small scale, fine control, and the high throughput nature of microfluidic platforms has tremendous potential to enable a new generation of crystallographic studies and facilitate the structure determination of important biological targets. The development of platforms for in meso membrane protein crystallization is particularly significant because they enable the preparation of highly viscous mixtures at a previously unachievable scale. Work in these areas is ongoing and has tremendous potential to improve not only current the methods of protein crystallization and crystallography, but also to enhance our knowledge of the structure and function of proteins which could have a significant scientific and medical impact on society as a whole. The microfluidic technology described in this thesis has the potential to significantly advance our understanding of the structure and function of membrane proteins, thereby aiding the elucidation of human biology, the development of pharmaceuticals with fewer side effects for a wide range of diseases. References (1) Quick, M.; Javitch, J. A. P Natl Acad Sci USA 2007, 104, 3603. (2) Trubetskoy, V. S.; Burke, T. J. Am Lab 2005, 37, 19. (3) Pecina, P.; Houstkova, H.; Hansikova, H.; Zeman, J.; Houstek, J. Physiol Res 2004, 53, S213. (4) Arinaminpathy, Y.; Khurana, E.; Engelman, D. M.; Gerstein, M. B. Drug Discovery Today 2009, 14, 1130. (5) Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. Nat Rev Drug Discov 2006, 5, 993. (6) Dauter, Z.; Lamzin, V. S.; Wilson, K. S. Current Opinion in Structural Biology 1997, 7, 681. (7) Hansen, C.; Quake, S. R. Current Opinion in Structural Biology 2003, 13, 538. (8) Govada, L.; Carpenter, L.; da Fonseca, P. C. A.; Helliwell, J. R.; Rizkallah, P.; Flashman, E.; Chayen, N. E.; Redwood, C.; Squire, J. M. J Mol Biol 2008, 378, 387. (9) Hansen, C. L.; Skordalakes, E.; Berger, J. M.; Quake, S. R. P Natl Acad Sci USA 2002, 99, 16531. (10) Leng, J.; Salmon, J.-B. Lab Chip 2009, 9, 24. (11) Zheng, B.; Gerdts, C. J.; Ismagilov, R. F. Current Opinion in Structural Biology 2005, 15, 548. (12) Lorber, B.; Delucas, L. J.; Bishop, J. B. J Cryst Growth 1991, 110, 103. (13) Talreja, S.; Perry, S. L.; Guha, S.; Bhamidi, V.; Zukoski, C. F.; Kenis, P. J. A. The Journal of Physical Chemistry B 2010, 114, 4432. (14) Chayen, N. E. Current Opinion in Structural Biology 2004, 14, 577. (15) He, G. W.; Bhamidi, V.; Tan, R. B. H.; Kenis, P. J. A.; Zukoski, C. F. Cryst Growth Des 2006, 6, 1175. (16) Zheng, B.; Tice, J. D.; Roach, L. S.; Ismagilov, R. F. Angew Chem Int Edit 2004, 43, 2508. (17) Li, L.; Mustafi, D.; Fu, Q.; Tereshko, V.; Chen, D. L. L.; Tice, J. D.; Ismagilov, R. F. P Natl Acad Sci USA 2006, 103, 19243. (18) Song, H.; Chen, D. L.; Ismagilov, R. F. Angew Chem Int Edit 2006, 45, 7336. (19) van der Woerd, M.; Ferree, D.; Pusey, M. Journal of Structural Biology 2003, 142, 180. (20) Ng, J. D.; Gavira, J. A.; Garcia-Ruiz, J. M. Journal of Structural Biology 2003, 142, 218. (21) Talreja, S.; Kenis, P. J. A.; Zukoski, C. F. Langmuir 2007, 23, 4516. (22) Hansen, C. L.; Quake, S. R.; Berger, J. M. US, 2007. (23) Newman, J.; Fazio, V. J.; Lawson, B.; Peat, T. S. Cryst Growth Des 2010, 10, 2785. (24) Newman, J.; Xu, J.; Willis, M. C. Acta Crystallographica Section D 2007, 63, 826. (25) Collingsworth, P. D.; Bray, T. L.; Christopher, G. K. J Cryst Growth 2000, 219, 283. (26) Durbin, S. D.; Feher, G. Annu Rev Phys Chem 1996, 47, 171. (27) Talreja, S.; Kim, D. Y.; Mirarefi, A. Y.; Zukoski, C. F.; Kenis, P. J. A. J Appl Crystallogr 2005, 38, 988. (28) Yoshizaki, I.; Nakamura, H.; Sato, T.; Igarashi, N.; Komatsu, H.; Yoda, S. J Cryst Growth 2002, 237, 295. (29) Anderson, M. J.; Hansen, C. L.; Quake, S. R. P Natl Acad Sci USA 2006, 103, 16746. (30) Hansen, C. L.; Sommer, M. O. A.; Quake, S. R. P Natl Acad Sci USA 2004, 101, 14431. (31) Lounaci, M.; Rigolet, P.; Abraham, C.; Le Berre, M.; Chen, Y. Microelectron Eng 2007, 84, 1758. (32) Zheng, B.; Roach, L. S.; Ismagilov, R. F. J Am Chem Soc 2003, 125, 11170. (33) Zhou, X.; Lau, L.; Lam, W. W. L.; Au, S. W. N.; Zheng, B. Anal. Chem. 2007. (34) Cherezov, V.; Caffrey, M. J Appl Crystallogr 2003, 36, 1372. (35) Qutub, Y.; Reviakine, I.; Maxwell, C.; Navarro, J.; Landau, E. M.; Vekilov, P. G. J Mol Biol 2004, 343, 1243. (36) Rummel, G.; Hardmeyer, A.; Widmer, C.; Chiu, M. L.; Nollert, P.; Locher, K. P.; Pedruzzi, I.; Landau, E. M.; Rosenbusch, J. P. Journal of Structural Biology 1998, 121, 82. (37) Gavira, J. A.; Toh, D.; Lopez-Jaramillo, J.; Garcia-Ruiz, J. M.; Ng, J. D. Acta Crystallogr D 2002, 58, 1147. (38) Stevens, R. C. Current Opinion in Structural Biology 2000, 10, 558. (39) Baker, M. Nat Methods 2010, 7, 429. (40) McPherson, A. In Current Topics in Membranes, Volume 63; Volume 63 ed.; DeLucas, L., Ed.; Academic Press: 2009, p 5. (41) Gabrielsen, M.; Gardiner, A. T.; Fromme, P.; Cogdell, R. J. In Current Topics in Membranes, Volume 63; Volume 63 ed.; DeLucas, L., Ed.; Academic Press: 2009, p 127. (42) Page, R. In Methods in Molecular Biology: Structural Proteomics - High Throughput Methods; Kobe, B., Guss, M., Huber, T., Eds.; Humana Press: Totowa, NJ, 2008; Vol. 426, p 345. (43) Caffrey, M. Ann Rev Biophys 2009, 38, 29. (44) Doerr, A. Nat Methods 2006, 3, 244. (45) Brostromer, E.; Nan, J.; Li, L.-F.; Su, X.-D. Biochemical and Biophysical Research Communications 2009, 386, 634. (46) Li, G.; Chen, Q.; Li, J.; Hu, X.; Zhao, J. Anal Chem 2010, 82, 4362. (47) Jia, Y.; Liu, X.-Y. The Journal of Physical Chemistry B 2006, 110, 6949. (48) RCSB Protein Data Bank. http://www.rcsb.org/ (July 11, 2010). (49) Membrane Proteins of Known 3D Structure. http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html (July 11, 2010). (50) Michel, H. Trends Biochem Sci 1983, 8, 56. (51) Rosenbusch, J. P. Journal of Structural Biology 1990, 104, 134. (52) Garavito, R. M.; Picot, D. Methods 1990, 1, 57. (53) Kulkarni, C. V. 2010; Vol. 12, p 237. (54) Landau, E. M.; Rosenbusch, J. P. P Natl Acad Sci USA 1996, 93, 14532. (55) Pebay-Peyroula, E.; Rummel, G.; Rosenbusch, J. P.; Landau, E. M. Science 1997, 277, 1676. (56) Cherezov, V.; Liu, W.; Derrick, J. P.; Luan, B.; Aksimentiev, A.; Katritch, V.; Caffrey, M. Proteins: Structure, Function, and Bioinformatics 2008, 71, 24. (57) Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen, S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Kuhn, P.; Weis, W. I.; Kobilka, B. K.; Stevens, R. C. Science 2007, 318, 1258. (58) Cherezov, V.; Yamashita, E.; Liu, W.; Zhalnina, M.; Cramer, W. A.; Caffrey, M. J Mol Biol 2006, 364, 716. (59) Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.; Chien, E. Y. T.; Lane, J. R.; IJzerman, A. P.; Stevens, R. C. Science 2008, 322, 1211. (60) Rosenbaum, D. M.; Cherezov, V.; Hanson, M. A.; Rasmussen, S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Yao, X. J.; Weis, W. I.; Stevens, R. C.; Kobilka, B. K. Science 2007, 318, 1266. (61) Wacker, D.; Fenalti, G.; Brown, M. A.; Katritch, V.; Abagyan, R.; Cherezov, V.; Stevens, R. C. J Am Chem Soc 2010, 132, 11443. (62) Höfer, N.; Aragão, D.; Caffrey, M. Biophys J 2010, 99, L23. (63) Li, L.; Ismagilov, R. F. Ann Rev Biophys 2010. (64) Pal, R.; Yang, M.; Lin, R.; Johnson, B. N.; Srivastava, N.; Razzacki, S. Z.; Chomistek, K. J.; Heldsinger, D. C.; Haque, R. M.; Ugaz, V. M.; Thwar, P. K.; Chen, Z.; Alfano, K.; Yim, M. B.; Krishnan, M.; Fuller, A. O.; Larson, R. G.; Burke, D. T.; Burns, M. A. Lab Chip 2005, 5, 1024. (65) Jayashree, R. S.; Gancs, L.; Choban, E. R.; Primak, A.; Natarajan, D.; Markoski, L. J.; Kenis, P. J. A. J Am Chem Soc 2005, 127, 16758. (66) Wootton, R. C. R.; deMello, A. J. Chem Commun 2004, 266. (67) McPherson, A. J Appl Crystallogr 2000, 33, 397.

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition.

Embedding Video Object Detection Capabilities on Low-Power Nano-Drones

Our objective for this thesis work was the deployment of a Neural Network based approach for video object detection on board a nano-drone. Furthermore, we have studied some possible extensions to exploit the temporal nature of videos to improve the detection capabilities of our algorithm. For our project, we have utilized the Mobilenetv2/v3SSDLite due to their limited computational and memory requirements. We have trained our networks on the IMAGENET VID 2015 dataset and to deploy it onto the nano-drone we have used the NNtool and Autotiler tools by GreenWaves. To exploit the temporal nature of video data we have tried different approaches: the introduction of an LSTM based convolutional layer in our architecture, the introduction of a Kalman filter based tracker as a postprocessing step to augment the results of our base architecture. We have obtain a total improvement in our performances of about 2.5 mAP with the Kalman filter based method(BYTE). Our detector run on a microcontroller class processor on board the nano-drone at 1.63 fps.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

Do buzz ao marketing viral : um estudo

Dissertação de Mestrado em Gestão de Empresas/MBA.

Multiprocessor platform using LEON3 processor

The recent advances in embedded systems world, lead us to more complex systems with application specific blocks (IP cores), the System on Chip (SoC) devices. A good example of these complex devices can be encountered in the cell phones that can have image processing cores, communication cores, memory card cores, and others. The need of augmenting systems’ processing performance with lowest power, leads to a concept of Multiprocessor System on Chip (MSoC) in which the execution of multiple tasks can be distributed along various processors. This thesis intends to address the creation of a synthesizable multiprocessing system to be placed in a FPGA device, providing a good flexibility to tailor the system to a specific application. To deliver a multiprocessing system, will be used the synthesisable 32-bit SPARC V8 compliant, LEON3 processor.

Desenvolvimento de uma metodologia baseada em redes neuronais para o cálculo da energia produzida por aerogeradores

O uso da energia eólica para a produção de eletricidade apresenta na última década um crescimento apreciável. Monitorizar o desempenho dos aerogeradores torna-se um processo incontornável, quer por motivos financeiros, quer por questões operacionais. Os investimentos despendidos na construção de parques eólicos são muito consideráveis, pelo que é essencial a análise constante dos aspetos preponderantes no retorno do investimento. A maximização da energia produzida por cada aerogerador é o objetivo principal da monitorização dos parques eólicos. Os sistemas Supervisory Control and Data Acquisition (SCADAs) instalados nos parques eólicos permitem uma supervisão em tempo real relativamente ao estado e funcionamento dos aerogeradores, adquirindo uma elevada importância na avaliação dos rendimentos energéticos e anomalias de funcionamento, garantido desta forma melhorias de produtividade. O objetivo deste trabalho é estimar a energia produzida pelos aerogeradores quando ocorrem falhas de comunicação com o seu contador interno ou avaria do mesmo. A ocorrência destas situações não permite a monitorização da energia produzida durante esse período. Foram analisados dados operacionais dos aerogeradores relativos a um parque eólico localizado na zona Norte de Portugal, sendo usados os dados recolhidos pelo sistema SCADA sobre a forma de médias de 10 min referentes ao período de janeiro de 2011 a agosto 2011. O desempenho da rede neuronal depende da qualidade e quantidade do conjunto de dados usados para o treino da rede. Os dados usados devem representar de forma fiel o estado que se pretende para o equipamento. Para a obtenção do objetivo proposto foi fundamental a identificação das grandezas disponíveis a utilizar no método de cálculo da energia produzida. Os resultados obtidos com aplicação das redes neuronais no método de cálculo da energia produzida por aerogeradores demonstram que independentemente do período de indisponibilidade da informação referente à energia produzida é possível estimar o valor da mesma.

User-interface technologies for the industrial environment: towards the cyber-factory

In the past few years the so-called gadgets like cellular phones, personal data assistants and digital cameras are more widespread even with less technological aware users. However, for several reasons, the factory-floor itself seems to be hermetic to this changes ... After the fieldbus revolution, the factory-floor has seen an increased use of more and more powerful programmable logic controllers and user interfaces but the way they are used remains almost the same. We believe that new user-computer interaction techniques including multimedia and augmented rcaliry combined with now affordable technologies like wearable computers and wireless networks can change the way the factory personal works together with the roachines and the information system on the factory-floor. This new age is already starting with innovative uses of communication networks on the factory-floor either using "standard" networks or enhancing industrial networks with multimedia and wireless capabilities.

An embedded 1149.4 extension to support mixed-signal debugging

Debugging electronic circuits is traditionally done with bench equipment directly connected to the circuit under debug. In the digital domain, the difficulties associated with the direct physical access to circuit nodes led to the inclusion of resources providing support to that activity, first at the printed circuit level, and then at the integrated circuit level. The experience acquired with those solutions led to the emergence of dedicated infrastructures for debugging cores at the system-on-chip level. However, all these developments had a small impact in the analog and mixed-signal domain, where debugging still depends, to a large extent, on direct physical access to circuit nodes. As a consequence, when analog and mixed-signal circuits are integrated as cores inside a system-on-chip, the difficulties associated with debugging increase, which cause the time-to-market and the prototype verification costs to also increase. The present work considers the IEEE1149.4 infrastructure as a means to support the debugging of mixed-signal circuits, namely to access the circuit nodes and also an embedded debug mechanism named mixed-signal condition detector, necessary for watch-/breakpoints and real-time analysis operations. One of the main advantages associated with the proposed solution is the seamless migration to the system-on-chip level, as the access is done through electronic means, thus easing debugging operations at different hierarchical levels.

Three-dimensial patchy lattice model: Ring formation and phase separation

We investigate the structural and thermodynamic properties of a model of particles with 2 patches of type A and 10 patches of type B. Particles are placed on the sites of a face centered cubic lattice with the patches oriented along the nearest neighbor directions. The competition between the self- assembly of chains, rings, and networks on the phase diagram is investigated by carrying out a systematic investigation of this class of models, using an extension ofWertheim's theory for associating fluids and Monte Carlo numerical simulations. We varied the ratio r epsilon(AB)/epsilon(AA) of the interaction between patches A and B, epsilon(AB), and between A patches, epsilon(AA) (epsilon(BB) is set to theta) as well as the relative position of the A patches, i.e., the angle. between the (lattice) directions of the A patches. We found that both r and theta (60 degrees, 90 degrees, or 120 degrees) have a profound effect on the phase diagram. In the empty fluid regime (r < 1/2) the phase diagram is reentrant with a closed miscibility loop. The region around the lower critical point exhibits unusual structural and thermodynamic behavior determined by the presence of relatively short rings. The agreement between the results of theory and simulation is excellent for theta = 120 degrees but deteriorates as. decreases, revealing the need for new theoretical approaches to describe the structure and thermodynamics of systems dominated by small rings. (C) 2014 AIP Publishing LLC.

Real time fault injection using a modified debugging infrastructure

Dependability is a critical factor in computer systems, requiring high quality validation & verification procedures in the development stage. At the same time, digital devices are getting smaller and access to their internal signals and registers is increasingly complex, requiring innovative debugging methodologies. To address this issue, most recent microprocessors include an on-chip debug (OCD) infrastructure to facilitate common debugging operations. This paper proposes an enhanced OCD infrastructure with the objective of supporting the verification of fault-tolerant mechanisms through fault injection campaigns. This upgraded on-chip debug and fault injection (OCD-FI) infrastructure provides an efficient fault injection mechanism with improved capabilities and dynamic behavior. Preliminary results show that this solution provides flexibility in terms of fault triggering and allows high speed real-time fault injection in memory elements

Real time fault injection using enhanced OCD -- a performance analysis

Fault injection is frequently used for the verification and validation of dependable systems. When targeting real time microprocessor based systems the process becomes significantly more complex. This paper proposes two complementary solutions to improve real time fault injection campaign execution, both in terms of performance and capabilities. The methodology is based on the use of the on-chip debug mechanisms present in modern electronic devices. The main objective is the injection of faults in microprocessor memory elements with minimum delay and intrusiveness. Different configurations were implemented and compared in terms of performance gain and logic overhead.

Physical layer aware network architecture for the future internet

In this article, physical layer awareness in access, core, and metro networks is addressed, and a Physical Layer Aware Network Architecture Framework for the Future Internet is presented and discussed, as proposed within the framework of the European ICT Project 4WARD. Current limitations and shortcomings of the Internet architecture are driving research trends at a global scale toward a novel, secure, and flexible architecture. This Future Internet architecture must allow for the co-existence and cooperation of multiple networks on common platforms, through the virtualization of network resources. Possible solutions embrace a full range of technologies, from fiber backbones to wireless access networks. The virtualization of physical networking resources will enhance the possibility of handling different profiles, while providing the impression of mutual isolation. This abstraction strategy implies the use of well elaborated mechanisms in order to deal with channel impairments and requirements, in both wireless (access) and optical (core) environments.