Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

LiDARPheno: A Low-Cost LiDAR-based 3D Scanning System for Plant Morphological Trait Characterization

The ever-growing world population brings the challenge for food security in the current world. The gene modification tools have opened a new era for fast-paced research on new crop identification and development. However, the bottleneck in the plant phenotyping technology restricts the alignment in Geno-pheno development as phenotyping is the key for the identification of potential crop for improved yield and resistance to the changing environment. Various attempts to making the plant phenotyping a “high-throughput” have been made while utilizing the existing sensors and technology. However, the demand for ‘good’ phenotypic information for linkage to the genome in understanding the gene-environment interactions is still a bottleneck in the plant phenotyping technologies. Moreover, the available technologies and instruments are inaccessible, expensive and sometimes bulky. This thesis work attempts to address some of the critical problems, such as exploration and development of a low-cost LiDAR-based platform for phenotyping the plants in-lab and in-field. A low-cost LiDAR-based system design, LiDARPheno, is introduced in this thesis work to assess the feasibility of the inexpensive LiDAR sensor in the leaf trait (length, width, and area) extraction. A detailed design of the LiDARPheno, based on low-cost and off-the-shelf components and modules, is presented. Moreover, the design of the firmware to control the hardware setup of the system and the user-level python-based script for data acquisition is proposed. The software part of the system utilizes the publicly available libraries and Application Programming Interfaces (APIs), making it easy to implement the system by a non-technical user. The LiDAR data analysis methods are presented, and algorithms for processing the data and extracting the leaf traits are developed. The processing includes conversion, cleaning/filtering, segmentation and trait extraction from the LiDAR data. Experiments on indoor plants and canola plants were performed for the development and validation of the methods for estimation of the leaf traits. The results of the LiDARPheno based trait extraction are compared with the SICK LMS400 (a commercial 2D LiDAR) to assess the performance of the developed system. Experimental results show a fair agreement between the developed system and a commercial LiDAR system. Moreover, the results are compared with the acquired ground truth as well as the commercial LiDAR system. The LiDARPheno can provide access to the inexpensive LiDAR-based scanning and open the opportunities for future exploration

Smartphone-based Thermal Imaging System for Diabetic Foot Ulcer Assessment

International audienceThis research work is part of the STANDUP project http://www.standupproject.eu/ dedicated to improve diabetic foot ulcer prevention and treatment of the plantar foot surface using smartphone-embedded thermal imaging system. The aim of this preliminary work is to build an ulcer assessment tool based on a smartphone and an IR thermal camera. The proposed system represents a practical tool for accurate DFU healing assessment, combining color and thermal information in a single user-friendly system. To ensure robust tissue identification, an annotation software was developed based on SLIC superpixel segmentation algorithm. The tool thus developed allows clinicians to achieve objective and accurate tissue identification and annotation. The proposed system could serve as an intelligent telemedicine system to be deployed by clinicians at hospitals and healthcare centers for more accurate diagnosis of diabetic foot ulcers

Advances in navigation and intraoperative imaging for intraoperative electron radiotherapy

Mención Internacional en el título de doctorEsta tesis se enmarca dentro del campo de la radioterapia y trata específicamente sobre la radioterapia intraoperatoria (RIO) con electrones. Esta técnica combina la resección quirúrgica de un tumor y la radiación terapéutica directamente aplicada sobre el lecho tumoral post-resección o sobre el tumor no resecado. El haz de electrones de alta energía es colimado y conducido por un aplicador específico acoplado a un acelerador lineal. La planificación de la RIO con electrones es compleja debido a las modificaciones geométricas y anatómicas producidas por la retracción de estructuras y la eliminación de tejidos cancerosos durante la cirugía. Actualmente, no se dispone del escenario real en este tipo de tratamientos (por ejemplo, la posición/orientación del aplicador respecto a la anatomía del paciente o las irregularidades en la superficie irradiada), sólo de una estimación grosso modo del tratamiento real administrado al paciente. Las imágenes intraoperatorias del escenario real durante el tratamiento (concretamente imágenes de tomografía axial computarizada [TAC]) serían útiles no sólo para la planificación intraoperatoria, sino también para registrar y evaluar el tratamiento administrado al paciente. Esta información es esencial en estudios prospectivos. En esta tesis se evaluó en primer lugar la viabilidad de un sistema de seguimiento óptico de varias cámaras para obtener la posición/orientación del aplicador en los escenarios de RIO con electrones. Los resultados mostraron un error de posición del aplicador inferior a 2 mm (error medio del centro del bisel) y un error de orientación menor de 2º (error medio del eje del bisel y del eje longitudinal del aplicador). Estos valores están dentro del rango propuesto por el Grupo de Trabajo 147 (encargo del Comité de Terapia y del Subcomité para la Mejora de la Garantía de Calidad y Resultados de la Asociación Americana de Físicos en Medicina [AAPM] para estudiar en radioterapia externa la exactitud de la localización con métodos no radiográficos, como los sistemas infrarrojos). Una limitación importante de la solución propuesta es que el aplicador se superpone a la imagen preoperatoria del paciente. Una imagen intraoperatoria proporcionaría información anatómica actualizada y permitiría estimar la distribución tridimensional de la dosis. El segundo estudio específico de esta tesis evaluó la viabilidad de adquirir con un TAC simulador imágenes TAC intraoperatorias de escenarios reales de RIO con electrones. No hubo complicaciones en la fase de transporte del paciente utilizando la camilla y su acople para el transporte, o con la adquisición de imágenes TAC intraoperatorias en la sala del TAC simulador. Los estudios intraoperatorios adquiridos se utilizaron para evaluar la mejora obtenida en la estimación de la distribución de dosis en comparación con la obtenida a partir de imágenes TAC preoperatorias, identificando el factor dominante en esas estimaciones (la región de aire y las irregularidades en la superficie, no las heterogeneidades de los tejidos). Por último, el tercer estudio específico se centró en la evaluación de varias tecnologías TAC de kilovoltaje, aparte del TAC simulador, para adquirir imágenes intraoperatorias con las que estimar la distribución de la dosis en RIO con electrones. Estos dispositivos serían necesarios en el caso de disponer de aceleradores lineales portátiles en el quirófano ya que no se aprobaría mover al paciente a la sala del TAC simulador. Los resultados con un maniquí abdominal mostraron que un TAC portátil (BodyTom) e incluso un acelerador lineal con un TAC de haz de cónico (TrueBeam) serían adecuados para este propósito.This thesis is framed within the field of radiotherapy, specifically intraoperative electron radiotherapy (IOERT). This technique combines surgical resection of a tumour and therapeutic radiation directly applied to a post-resection tumour bed or to an unresected tumour. The high-energy electron beam is collimated and conducted by a specific applicator docked to a linear accelerator (LINAC). Dosimetry planning for IOERT is challenging owing to the geometrical and anatomical modifications produced by the retraction of structures and removal of cancerous tissues during the surgery. No data of the actual IOERT 3D scenario is available (for example, the applicator pose in relation to the patient’s anatomy or the irregularities in the irradiated surface) and consequently only a rough approximation of the actual IOERT treatment administered to the patient can be estimated. Intraoperative computed tomography (CT) images of the actual scenario during the treatment would be useful not only for intraoperative planning but also for registering and evaluating the treatment administered to the patient. This information is essential for prospective trials. In this thesis, the feasibility of using a multi-camera optical tracking system to obtain the applicator pose in IOERT scenarios was firstly assessed. Results showed that the accuracy of the applicator pose was below 2 mm in position (mean error of the bevel centre) and 2º in orientation (mean error of the bevel axis and the longitudinal axis), which are within the acceptable range proposed in the recommendation of Task Group 147 (commissioned by the Therapy Committee and the Quality Assurance and Outcomes Improvement Subcommittee of the American Association of Physicists in Medicine [AAPM] to study the localization accuracy with non-radiographic methods such as infrared systems in external beam radiation therapy). An important limitation of this solution is that the actual pose of applicator is superimposed on a patient’s preoperative image. An intraoperative image would provide updated anatomical information and would allow estimating the 3D dose distribution. The second specific study of this thesis evaluated the feasibility of acquiring intraoperative CT images with a CT simulator in real IOERT scenarios. There were no complications in the whole procedure related to the transport step using the subtable and its stretcher or the acquisition of intraoperative CT images in the CT simulator room. The acquired intraoperative studies were used to evaluate the improvement achieved in the dose distribution estimation when compared to that obtained from preoperative CT images, identifying the dominant factor in those estimations (air gap and the surface irregularities, not tissue heterogeneities). Finally, the last specific study focused on assessing several kilovoltage (kV) CT technologies other than CT simulators to acquire intraoperative images for estimating IOERT dose distribution. That would be necessary when a mobile electron LINAC was available in the operating room as transferring the patient to the CT simulator room could not be approved. Our results with an abdominal phantom revealed that a portable CT (BodyTom) and even a LINAC with on-board kV cone-beam CT (TrueBeam) would be suitable for this purpose.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín López Herráiz.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Óscar Acosta Tamay

Development of a Large Field-of-View PIV System for Rotorcraft Testing in the 14- x 22-Foot Subsonic Tunnel

A Large Field-of-View Particle Image Velocimetry (LFPIV) system has been developed for rotor wake diagnostics in the 14-by 22-Foot Subsonic Tunnel. The system has been used to measure three components of velocity in a plane as large as 1.524 meters by 0.914 meters in both forward flight and hover tests. Overall, the system performance has exceeded design expectations in terms of accuracy and efficiency. Measurements synchronized with the rotor position during forward flight and hover tests have shown that the system is able to capture the complex interaction of the body and rotor wakes as well as basic details of the blade tip vortex at several wake ages. Measurements obtained with traditional techniques such as multi-hole pressure probes, Laser Doppler Velocimetry (LDV), and 2D Particle Image Velocimetry (PIV) show good agreement with LFPIV measurements

Measuring the dynamic photosynthome

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal “mismatch” between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved

Image Quality Improvement of Medical Images using Deep Learning for Computer-aided Diagnosis

Retina image analysis is an important screening tool for early detection of multiple dis eases such as diabetic retinopathy which greatly impairs visual function. Image analy sis and pathology detection can be accomplished both by ophthalmologists and by the use of computer-aided diagnosis systems. Advancements in hardware technology led to more portable and less expensive imaging devices for medical image acquisition. This promotes large scale remote diagnosis by clinicians as well as the implementation of computer-aided diagnosis systems for local routine disease screening. However, lower cost equipment generally results in inferior quality images. This may jeopardize the reliability of the acquired images and thus hinder the overall performance of the diagnos tic tool. To solve this open challenge, we carried out an in-depth study on using different deep learning-based frameworks for improving retina image quality while maintaining the underlying morphological information for the diagnosis. Our results demonstrate that using a Cycle Generative Adversarial Network for unpaired image-to-image trans lation leads to successful transformations of retina images from a low- to a high-quality domain. The visual evidence of this improvement was quantitatively affirmed by the two proposed validation methods. The first used a retina image quality classifier to confirm a significant prediction label shift towards a quality enhance. On average, a 50% increase of images being classified as high-quality was verified. The second analysed the perfor mance modifications of a diabetic retinopathy detection algorithm upon being trained with the quality-improved images. The latter led to strong evidence that the proposed solution satisfies the requirement of maintaining the images’ original information for diagnosis, and that it assures a pathology-assessment more sensitive to the presence of pathological signs. These experimental results confirm the potential effectiveness of our solution in improving retina image quality for diagnosis. Along with the addressed con tributions, we analysed how the construction of the data sets representing the low-quality domain impacts the quality translation efficiency. Our findings suggest that by tackling the problem more selectively, that is, constructing data sets more homogeneous in terms of their image defects, we can obtain more accentuated quality transformations