Probabilistic Energy Optimization in Wireless Sensor Networks with Variable Size Griding

Abstract-Due to limited energy supplies, reducing power consumption is an important goal in wireless sensor networks. Clustering techniques are used to reduce power consumption and prolong network lifetime in many existing research efforts, among which grid-based ones are often used due to their simplicity and scalability. However, most existing work uses average distance as a simplification in calculating distance-related power consumption, which leads to a large underestimation of the actual energy depletion rate. In this paper, we propose an energy optimization model based on probabilistic distance distributions, which captures the distance-induced power consumption with high accuracy. We further analyze the uneven traffic distribution in wireless sensor networks and propose a nonuniform griding scheme to balance the energy depletion in all grids. Through our analysis, we are able to obtain the optimal grid size ratio that minimizes the energy consumption. Analytical results are validated through simulation, which shows the promising potentials of our method and the nonuniform griding technique

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Robust compressive sensing techniques

Cataloged from PDF version of article.Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed from an underdetermined linear measurements. However, in reality there is a mismatch between the assumed and the actual dictionary due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes signifi- cant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this thesis presents two novel robust algorithm and an adaptive discretization framework that can obtain successful sparse representations. In the proposed techniques, the selected dictionary atoms are perturbed towards directions to decrease the orthogonal residual norm. The first algorithm named as Parameter Perturbed Orthogonal Matching Pursuit (PPOMP) targets the off-grid problem and the parameters of the selected dictionary atoms are perturbed. The second algorithm named as Perturbed Orthogonal Matching Pursuit (POMP) targets the unstructured basis mismatch problem and performs controlled rotation based perturbation of selected dictionary atoms. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of both parametric and unstructured basis mismatch problem can be obtained as compared to standard sparse reconstruction techniques. Different from the proposed perturbation approaches, the proposed adaptive framework discretizes the continuous parameter space depending on the estimated sparsity level. Once a provisional solution is obtained with a sparse solver, the framework recursively splits the problem into sparser sub-problems so that each sub-problem is exposed to less severe off-grid problem. In the presented recursive framework, any sparse reconstruction technique can be used. As illustrated over commonly used applications, the error in the estimated parameters of sparse signal components almost achieve the Cram´er-Rao lower bound in the proposed framework.Teke, OğuzhanM.S

NASA Tech Briefs, February 1994

Topics covered include: Test and Measurement; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences; Books and Report

Novel deep learning architectures for marine and aquaculture applications

Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices

Human Activity Recognition and Fall Detection Using Unobtrusive Technologies

As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people

XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)

En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional. El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA