Study on Desiccant and Evaporative Cooling Systems for Livestock Thermal Comfort: Theory and Experiments

The present study considers evaporative cooling and desiccant unit-based air-conditioning (AC) options for livestock AC application. In this regard, proposed systems are investigated by means of experiments and thermodynamic investigations. Air-conditioning requirements for animals are theoretically investigated and temperature-humidity index (THI) is estimated. A lab-scale heat mass exchanger based on the Maisotsenko-cycle evaporative cooling conception (MEC) is set up and its performance is evaluated at different ambient air conditions. In addition, a desiccant-based air-conditioning (DAC) unit is thermodynamically evaluated using a steady-state model available in the literature. The study focuses on the ambient conditions of Multan which is the 5th largest city of Pakistan and is assumed to be a typical hot city of southern Punjab. The study proposed three kinds of AC combination i.e., (i) stand-alone MEC, (ii) stand-alone desiccant AC, and (iii) M-cycle based desiccant AC systems. Wet bulb effectiveness of the stand-alone MEC unit resulted in being from 64% to 78% whereas the coefficient of performance for stand-alone desiccant AC and M-cycle based desiccant AC system was found to be 0.51 and 0.62, respectively. Results showed that the stand-alone MEC and M-cycle based desiccant AC systems can achieve the animals’ thermal comfort for the months of March to June and March to September, respectively, whereas, stand-alone desiccant AC is not found to be feasible in any month. In addition, the ambient situations of winter months (October to February) are already within the range of animal thermal comfort

(Review article*) Permaculture: Smart Growth Strategies and Management for Juniper Forest

Permaculture creates an integrated system by incorporating those parameters which are often viewed as separate entities such as smart growth, low-impact development, habitat protection, complete streets, and other initiatives. Its gives better planning options and give policies a line of coherence and directions which provides basis for the real concept of sustainability. Presently in Pakistan, forest management policies suffer from a number of drawbacks and especially the Juniper forests in Pakistan are under constant pressure due to natural as well as anthropogenic pressures. To conserve the Juniper Forest Ecosystem a proposed Smart Growth Strategies based on Permaculture’s principles are designed to protect the Ziarat Juniper Forest that offers an unequivocal vision and strategy to gain valid sustainability in forest management

A review of recent advances in adsorption desalination technologies

Adsorption-based desalination (AD) is an emerging concept to co-generate distilled fresh water and cooling applications. The present study is aimed to provide a comprehensive review of the adsorption desalination systems and subsequent hybridization with known conventional cycles such as the multiple-effect AD (MED), solar regenerable, integrated evaporator-condenser cascaded, and ejector integrated systems. The systems are investigated for energy consumption, productivity enhancement, and performance parameters, including production cost, daily water production, and performance coefficient. Comprehensive economic aspects, future challenges, and future progress of the technologies are discussed accordingly to pave researchers' paths for technological innovation. Traditional AD systems can produce specific daily water production of 25 kg per kg of adsorbent. The solar adsorption desalination-cooling (ADC) showed a promising specific cooling power of 112 W/kg along with a COP of 0.45. Furthermore, for a hybrid MEDAD cycle, the gain output ratio (GOR) and performance ratio (PR) is found to be 40%, along with an augmented water production rate from 60% to two folds. The AD technology could manage the high salinity feed water with the production of low salinity water with a reasonable cost of US$0.2/m3

Visuelle Terrain-Klassifizierung für mobile Outdoor-Roboter

In this thesis we present a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. For this purpose, we put a camera on a mobile robot and use it to capture images which are then analyzed to recognize the terrains present in these images. There are two sets of approaches that we use to classify terrains. The first is based on greyscale images and the second one is based on color images. For greyscale images, we use two different robot platforms for two different scenarios. The first robot platform is a wheeled outdoor robot. The second platform is a flying robot. For terrain classification, we modify and test three approaches called SURF, Daisy and Contrast Context Histogram, which are traditionally not used for texture classification. We compare these with more traditional texture classification approaches, such as Local Binary Patterns (LBP), Local Ternary Patterns (LTP) and a newer extension Local Adaptive Ternary Patterns (LATP). The image is divided into a grid and local features are calculated on the cells of this grid. These features are then used to train a classifier that can differentiate between different terrain classes. Images of different terrain types are captured using a single camera mounted on a mobile outdoor robot. We drove our robot under different weather and ground conditions and captured data of different terrain types for our experiments. We did not filter out blurred images which occur due to robot motion and other artifacts caused by rain, etc. We used a Random Forest classifier for classification and cross-validation for the verification of our results. It is shown that SURF features perform better than other descriptors for smaller cell sizes and LTP works best for larger grid cell sizes. The results show that these approaches work well for terrain classification in a fast moving mobile robot, despite image blur and other artifacts induced due to variant weather conditions. Furthermore we investigate the effectiveness of local image features for visual terrain classification for outdoor flying robots. A quadrocopter fitted with a single camera is flown over different terrains to take images of the ground below. Six different terrain types are considered in this approach. The images captured have artifacts like blur and scale variations. It is shown that SURF features also perform better here than other descriptors for smaller grid cell sizes and LTP performs better for larger cell sizes. We also test color image based terrain classification. For this purpose, we use two different types of camera mounted on our wheeled outdoor robot and capture five different terrain types traversed by the robot. We use two different image descriptors that can work on color images. The first descriptor is the co-occurrence matrix and the second descriptor is the SURF descriptor. Each of these descriptors is applied to the color channels of the image to extract a feature vector. These features are then used to train and test classifiers like Random Decision Trees and Support Vector Machines. We test these classification techniques on different color spaces for the images containing terrains. We also apply Principle Component Analysis (PCA) to reduce the dimensionality of the feature vectors. The co-occurrence matrix produced the best result in this case.In dieser Arbeit werden mehrere Ansätze zur visuellen Terrainklassifizierung für mobile Roboter vorgestellt und verglichen. Dafür wurden monochrome und farbige Bilder, die von mobilen Outdoor-Robotern aufgenommen wurden, mit Hilfe von lokalen Merkmalen analysiert, um verschiedene Terraintypen zu erkennen. Für die Analyse der monochromen Bilder wurden zwei verschiedene Roboter-Plattformen mit zwei verschiedenen Szenarien verwendet. Bei der ersten Plattform handelte es sich um einen fahrenden Outdoor-Roboter und bei der zweiten Plattform um einen fliegenden Roboter. Bei der Terrainklassifizierung wurden zum einen die klassischen Ansätze Local-Binary-Patterns, Local-Ternary-Patterns und die neue Erweiterung Local-Adaptive-Ternary-Patterns verwendet. Zum anderen wurden neuere Verfahren wie SURF, Daisy und Kontrast-Kontext-Histogramme untersucht und miteinander verglichen. Das Bild wurde dafür in ein Gitter aufgeteilt, an dessen Schnittpunkten die Deskriptoren berechnet wurden. Anhand dieser Merkmale wurden anschließend Klassifikatoren trainiert, um die verschiedenen Terraintypen zu unterscheiden. Die Bilder für die Klassifizierung wurden für verschiedene Bodentypen bei unter- schiedlichen Witterungen aufgenommen. Dabei wurden Bilder mit Bewegungsunschärfe oder anderen Artefakten, wie Regen, nicht herausgefiltert. Als Klassifikator kamen Random-Forests zum Einsatz, deren Ergebnisse über eine Kreuzvalidierung verifiziert wurden. Dabei zeigten SURF-Features bei kleinen Gittergrößen die besten Ergebnisse, wogegen bei großen Gittern die Local-Ternary-Patterns am besten abschnitten. Bei diesen Versuchen hat sich gezeigt, dass die Terrainklassifizierung durch einen sich schnell bewegenden mobilen Roboter, auch bei Bewegungsunschärfe und bei Wettereinflüssen, sehr gute Ergebnisse erreicht. Außerdem wird in dieser Arbeit die visuelle Terrainklassifizierung auf Basis von lokalen Bildmerkmalen bei fliegenden Robotern untersucht. Ein Quadrocopter mit einer einzelnen Kamera wurde dafür über verschiedene Terraintypen geflogen und nahm dabei Bilder des Bodens auf. Die so entstandenen Bilder haben Artefakte wie Bewegungsunschärfe und durch die Flughöhe eine unterschiedliche Skalierung. Auch bei diesen Versuchen schnitten die SURF-Deskriptoren bei kleinen Gittern und die Local-Ternary-Patterns bei größeren Gittern am besten ab. Zuletzt wurde die Terrain-Klassifizierung mit Hilfe von Farbbildern untersucht. Dafür wurden eine Farb- und eine Monochromkamera auf einen fahrenden Outdoor-Roboter montiert und fünf verschiedene Terraintypen aufgenommen. Es kamen zwei verschiedene Deskriptoren für Farbbilder zum Einsatz. Der erste Deskriptor ist die Co-Occurrence-Matrix und der zweite der SURF-Deskriptor. Jeder der Deskriptoren wurde auf die Farbkanäle der Bilder angewendet, um einen Merkmalsvektor zu generieren. Diese Merkmale wurden dann mit Klassifikatoren wie Random-Decission-Trees und Support-Vector-Machines für unterschiedliche Farbräume trainiert und getestet. Mit Hilfe der Principle-Component-Analysis wurde außerdem die Dimension der Merkmalsvektoren verkleinert. Bei diesen Versuchen zeigte die Co-Occurrence-Matrix die besten Ergebnisse

Terrain Classification With Conditional Random Fields on Fused 3D LIDAR and Camera Data

Abstract — For a mobile robot to navigate safely and efficiently in an outdoor environment, it has to recognize its surrounding terrain. Our robot is equipped with a low– resolution 3D LIDAR and a color camera. The data from both sensors are fused to classify the terrain in front of the robot. Therefore, the ground plane is divided into a grid and each cell is classified as either asphalt, cobblestones, grass or gravel. We use height and intensity features for the LIDAR data and Local ternary patterns for the image data. By additionally taking into account the context–sensitive nature of the terrain, the results can be improved significantly. We present a method based on Conditional Random Fields and compare it with a Markov Random Field based approach. We show that the Conditional Random Field model is better suited for our task. We achieve an average true positive rate of 94.2 % for classifying the grid cells into the four terrain classes. I

Visual Terrain Classification by Flying Robots

Abstract — In this paper we investigate the effectiveness of SURF features for visual terrain classification for outdoor flying robots. A quadrocopter fitted with a single camera is flown over different terrains to take images of the ground below. Each image is divided into a grid and SURF features are calculated at grid intersections. A classifier is then used to learn to differentiate between different terrain types. Classification results of the SURF descriptor are compared with results from other texture descriptors like Local Binary Patterns and Local Ternary Patterns. Six different terrain types are considered in this approach. Random forests are used for classification on each descriptor. It is shown that SURF features perform better than other descriptors at higher resolutions