Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis

Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements..

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Photovoltaic Process Development and innovative Techniques

Photovoltaic processing is one of the processes that have significance in semiconductor process line. It is complicated due to the no. of elements involved that directly or indirectly affect the processing and final yield. So mathematically or empirically we can’t say assertively about the results specially related with diffusion, antireflective coating and impurity poisoning. Here I have experimented and collected data on the mono-crystal silicon wafers with varying properties and outputs. Then by using neural network with available experimental data output required can be estimated which is further tested by the test data for authenticity. One can say that it’s a kind of process simulation with varying input of raw wafers to get desired yield of photovoltaic mono-crystal cells.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Monitoring of Transmission of Tuberculosis between Wild Boars and Cattle: Genotypical Analysis of Strains by Molecular Epidemiology Techniques

An epidemiological survey for the monitoring of bovine tuberculosis transmission was carried out in western Liguria, a region in northern Italy. Fifteen Mycobacterium bovis strains were isolated from 63 wild boar samples (62 from mandibular lymph nodes and 1 from a liver specimen). Sixteen mediastinal lymph nodes of 16 head of cattle were collected, and 15 Mycobacterium bovis strains were subsequently cultured. All M. bovisstrains isolated from cattle and wild boars were genotyped by spoligotyping and by restriction fragment length polymorphism (RFLP) analysis with the IS6110 and IS1081 probes. All M. bovis strains showed the typical spoligotype characterized by the absence of the 39 to 43 spacers in comparison with the number in M. tuberculosis. A total of nine different clusters were identified by spoligotyping. The largest cluster included 9 strains isolated from wild boars and 11 strains isolated from cattle, thus confirming the possibility of transmission between the two animal species. Fingerprinting by RFLP analysis with the IS6110 probe showed an identical single-band pattern for 29 of 30 strains analyzed, and only 1 strain presented a five-band pattern. The use of IS1081 as a second probe was useful for differentiation of M. bovis from M. bovis BCG but not for differentiation among M. bovis strains, which presented the same undifferentiated genomic profile. In relation to the epidemiological investigation, we hypothesized that the feeding in pastures contaminated by cattle discharges could represent the most probable route of transmission of M. bovis between the two animal species. In conclusion, our results confirmed the higher discriminatory power of spoligotyping in relation to that of RFLP analysis for the differentiation of M. bovis genomic profiles. Our data showed the presence of a common M. bovis genotype in both cattle and wild boars, confirming the possible interspecies transmission of M. bovis.

Monitoring Ice Velocity Field in Victoria Land (Antarctica) Using Cross-Correlation Techniques on Satellite Images

Reconstructive Microsurgery and Tissue Engineering in Musculo-Skeletal Oncology - Innovative Techniques

Tumors involving bone and soft tissues are extremely challenging situations. With the recent advances of multi-modal treatment, not only the type of surgery has moved from amputation to limb-sparing procedures, but also the survivorship has improved considerably and reconstructive techniques have the goal to allow a considerably higher quality of life. In bone reconstruction, tissue engineering strategies are the main area of research. Re-vascularization and re-vitalisation of a massive allograft would considerably improve the outcome of biological reconstructions. Using a rabbit animal model, in this study we showed that, by implanting a vascular pedicle inside a weight bearing massive cortical allograft, the bone regeneration inside the allograft was higher compared to the non-vascularized implants, given the patency of the vascular pedicle. Improvement in the animal model and the addition of Stem Cells and Growth factors will allow a further improvement in the results. In soft tissue tumors, free and pedicled flaps have been proven to be of great help as reconstruction strategies. In this study we analyzed the functional and overall outcome of 14 patients who received a re-innervated vascularized flap. We have demonstrated that the use of the innovative technique of motor re-innervated muscular flaps is effective when the resection involves important functional compartments of the upper or lower limb, with no increase of post-operative complications. Although there was no direct comparison between this type of reconstruction and the standard non-innervated reconstruction, we underlined the remarkable high overall functional scores and patient satisfaction following this procedure.

Innovative techniques for sport turf managing: Agronomic and phyisiological implications

The growing substrate of the putting greens is considered a key factor for a healthy turf ecosystem. Actually detailed study on the effects of growth promoting bacteria and biostimulants on a professional sport turf are very limited. This thesis aimed to study the effectiveness of different microorganisms and biostimulants in order to improve the knowledge relative to the relationship between the beneficial microflora and root apparatus of sport turfs. The research project was divided in three principal steps: Initially, commercial products based on biostimulants and microorganisms were tested on a Lolium perenne L. essence grown in a controlled-environment. The principal evaluations were the study of the habitus of plants, biomass production and length of leaves and roots. Were studied the capacity of colonization of microorganisms within root tissues and rhizosphere. In the second step were developed two different biostimulant solutions based on effective microorganisms, mycorrhizae and humic acids. This test was conducted both on an Agrostis stolonifera putting green (Modena Golf & Country Club) in a semi-field condition and within a growth chamber on a Lolium perenne L. essence. Fungicide and chemicals applications were suspended in order to assess the effectiveness of the inoculants for nutrition and control of pests. In the last step, different microorganism mixes and biostimulants were tested on an experimental putting green in the Turf Research Center (TRC) (Virginia Tech, United States) in a real managing situation. The effects of different treatments were studied maintaining all chemicals and mechanicals managements scheduled during a sport season. Both growth-chamber and field results confirmed the capacity of microorganisms based biostimulants to promote the physiologic conditions of the plants, improve the growth of the roots and enhance the aesthetic performance of the turf. Molecular analysis confirmed the capacity of microorganisms to colonize the root tissues.

Ultra-Low Power and Non-intrusive Wireless Monitoring for Smart Buildings

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.

Monitoring Stormwater Redistribution Into Low Resistivity Soils Using Noninvasive Geophysical Techniques

Water held in the unsaturated zone is important for agriculture and construction and is replenished by infiltrating rainwater. Monitoring the soil water content of clay soils using ground-penetrating radar (GPR) has not been researched, as clay soils cause attenuation of GPR signal. In this study, GPR common-midpoint soundings (CMPs) are used in the clayey soils of the Miller Run floodplain to monitor changes in the soil water content (SWC) before and after rainfall events. GPR accomplishes this task because increases in water content will increase the dielectric constant of the subsurface material, and decrease the velocity of the GPR wave. Using an empirical relationship between dielectric constant and SWC, the Topp relation, we are able to calculate a SWC from these velocity measurements. Non-invasive electromagnetics, resistivity, and seismic were performed, and from these surveys, the layering at the field site was delineated. EM characterized the horizontal variation of the soil, allowing us to target the most clay rich area. At the CMP location, resistivity indicates the vertical structure of the subsurface consists of a 40 cm thick layer with a resistivity of 100 ohm*m. Between 40 cm and 1.5 m is a layer with a resistivity of 40 ohm*m. The thickness estimates were confirmed with invasive auger and trenching methods away from the CMP location. GPR CMPs were collected relative to a July 2013 and September 2013 storm. The velocity observations from the CMPs had a precision of +/- 0.001 m/ns as assessed by repeat analysis. In the case of both storms, the GPR data showed the expected relationship between the rainstorms and calculated SWC, with the SWC increasing sharply after the rainstorm and decreasing as time passed. We compared these data to auger core samples collected at the same time as the CMPs were taken, and the volumetric analysis of the cores confirmed the trend seen in the GPR, with SWC values between 3 and 5 percent lower than the GPR estimates. Our data shows that we can, with good precision, monitor changes in the SWC of conductive soils in response to rainfall events, despite the attenuation induced by the clay.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

On the monitoring of surface displacement in connection with volcano reactivation in Tenerife, Canary Islands, using space techniques

Geodetic volcano monitoring in Tenerife has mainly focused on the Las Cañadas Caldera, where a geodetic micronetwork and a levelling profile are located. A sensitivity test of this geodetic network showed that it should be extended to cover the whole island for volcano monitoring purposes. Furthermore, InSAR allowed detecting two unexpected movements that were beyond the scope of the traditional geodetic network. These two facts prompted us to design and observe a GPS network covering the whole of Tenerife that was monitored in August 2000. The results obtained were accurate to one centimetre, and confirm one of the deformations, although they were not definitive enough to confirm the second one. Furthermore, new cases of possible subsidence have been detected in areas where InSAR could not be used to measure deformation due to low coherence. A first modelling attempt has been made using a very simple model and its results seem to indicate that the deformation observed and the groundwater level variation in the island may be related. Future observations will be necessary for further validation and to study the time evolution of the displacements, carry out interpretation work using different types of data (gravity, gases, etc) and develop models that represent the island more closely. The results obtained are important because they might affect the geodetic volcano monitoring on the island, which will only be really useful if it is capable of distinguishing between displacements that might be linked to volcanic activity and those produced by other causes. One important result in this work is that a new geodetic monitoring system based on two complementary techniques, InSAR and GPS, has been set up on Tenerife island. This the first time that the whole surface of any of the volcanic Canary Islands has been covered with a single network for this purpose. This research has displayed the need for further similar studies in the Canary Islands, at least on the islands which pose a greater risk of volcanic reactivation, such as Lanzarote and La Palma, where InSAR techniques have been used already.

Monitoring leafy vegetables through packaging films with

Fresh-cut or minimally processed fruit and vegetables have been physically modified from its original form (by peeling, trimming, washing and cutting) to obtain a 100% edible product that is subsequently packaged (usually under modified atmosphere packaging –MAP) and kept in refrigerated storage. In fresh-cut products, physiological activity and microbiological spoilage, determine their deterioration and shelf-life. The major preservation techniques applied to delay spoilage are chilling storage and MAP, combined with chemical treatments antimicrobial solutions antibrowning, acidulants, antioxidants, etc.). The industry looks for safer alternatives. Consequently, the sector is asking for innovative, fast, cheap and objective techniques to evaluate the overall quality and safety of fresh-cut products in order to obtain decision tools for implementing new packaging materials and procedures. In recent years, hyperspectral imaging technique has been regarded as a tool for analyses conducted for quality evaluation of food products in research, control and industries. The hyperspectral imaging system allows integrating spectroscopic and imaging techniques to enable direct identification of different components or quality characteristics and their spatial distribution in the tested sample. The objective of this work is to develop hyperspectral image processing methods for the supervision through plastic films of changes related to quality deterioration in packed readyto-use leafy vegetables during shelf life. The evolutions of ready-to-use spinach and watercress samples covered with three different common transparent plastic films were studied. Samples were stored at 4 ºC during the monitoring period (until 21 days). More than 60 hyperspectral images (from 400 to 1000 nm) per species were analyzed using ad hoc routines and commercial toolboxes of MatLab®. Besides common spectral treatments for removing additive and multiplicative effects, additional correction, previously to any other correction, was performed in the images of leaves in order to avoid the modification in their spectra due to the presence of the plastic transparent film. Findings from this study suggest that the developed images analysis system is able to deal with the effects caused in the images by the presence of plastic films in the supervision of shelf-life in leafy vegetables, in which different stages of quality has been identified.

Monitoring the communication competence in an innovative context of project management learning

This document presents an innovative, formal educational initiative that is aimed at enhancing the development of engineering students' specific competences. The subject of project management is the common theoretical and practical framework that articulates an experience that is carried out by multidisciplinary groups. Full utilization of Web 2.0 platforms and Project Based Learning constitutes the applied methodology. More specifically, this study focuses on monitoring communication competence when working in virtual environments, providing an ad-hoc rubric as a final result.