Sense of place in everyday spaces:: lessons for urban design

Many philosophers and thinkers have lamented the loss of place in terms of the loss of contact of the body with the environment in post-industrialized societies. With the diminishing cache of places it becomes even more important to study spaces in the city that posses this character and genius loci. This paper investigates the qualities of a seemingly ordinary everyday space that imbue it with character and elevate it to become a place. The paper explores the significance of this space in everyday life and how it is transformed from an ordinary space of consumption to a meaningful place for meeting, interaction and human-human contacts, and a place for haptic experiences and bodyobject contacts in the community. An extensive study of a neighborhood commercial street in Cambridge, Massachusetts revealed a handful of spaces that were extensively used for social interactions. Observations and interviews suggested that one of these spaces supported the majority of social interactions and was a concrete human space with a unique sense of place. The patterns of interactions at this location were documented and analyzed using sketches, drawings, notes, interviews, photographs and videos and by actively participating in the phenomena of this space. Observations show that this location on the street has a distinct hereness, a sense of being in it and enclosure, a sense of ease and safety that is at the core of the experience of place. By analyzing the various phenomena of this place this study suggests essential qualities for the design of public spaces to become places that will retain a sense of nearness, a connection between people and places, and will strengthen our sense of tactile reality

Design Parameters and Shape Analysis of Microstrip Patch Antenna (A Review)

In This work, the different Antenna designs are highlighted. Nowadays, the most commonly used Microstrip Patch Antenna having different design parameters which can be suitably employed for various applications is elaborated. The work focused on various methodologies and antenna operations for the future Trends

PERFORMANCE OF PAN-TILT TRACKER BASED ON THE PIN-HOLE LENS MODEL

In the modern day, recognition and tracking of face or the iris is potentially one of the most powerful ways of differentiating between an authentic person and an imposter. Our method uses stereo vision to track the 3-Dimensional coordinates of a target equivalent to a person’s eyes and using a pan-tilt unit we target these areas for additional processing such as iris or facial imaging. One of the most important parts involved in tracking is the way the pan-tilt unit is calibrated. There have been techniques in the past where PTZ (Pan-tilt-zoom) digital camera has been used and calibrated using self calibration techniques involving a checker board calibration grid but the tracking error was found to be large in these techniques. We introduce a more accurate form of calibration of the pantilt unit using photogrammetric calibration technique and view the pan-tilt unit as an emulation of a Pinhole Lens Model to detect and track the target. The system is demonstrated on ideal targets

DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Acute kidney injury as the presenting feature of sarcoidosis

Acute kidney injury is rarely the presenting feature of sarcoidosis. We report the case of a patient whose diagnosis was brought to light by the investigation of impaired kidney function. Concurrent hypercalcaemia was noted and prompted further investigation, which led to the diagnosis of sarcoidosis. This is a rare phenomenon and is an important consideration in the patient with acute kidney injury and hypercalcaemia, without an apparent explanation. Rapid improvement in both kidney function and hypercalcaemia occurred in response to treatment

Numerical Solution of Burgers\u27 equation arising in Longitudinal Dispersion Phenomena in Fluid Flow through Porous Media by Crank-Nicolson Scheme

The present paper discusses the numerical solution of the Burgers’ equation arising in longitudinal dispersion phenomenon in fluid flow through porous media. In the porous medium pure water, salt water or contaminated water disperse in longitudinal direction gives rise to a non-linear partial differential equation in the form of Burgers’ equation. The equation is solved by using Crank-Nicolson finite difference scheme with appropriate initial and boundary conditions. The longitudinal dispersion phenomenon may be miscible or immiscible fluid flow through porous media. The problem of miscible displacement can be seen in the coastal areas, where fresh water beds are gradually displaced by sea water. Longitudinal dispersion phenomenon plays an important role to control salinity of the soil in western seashore region of the Gujarat state in India. To control salinity, the government of Gujarat has developed many check dams from where contaminated water diverted and poured to the soil of the farms, where the crops of cumin seed (jeera), fennel (saunf) and other grains are grown. In this region due to the infiltration of this infiltered water, free surface of sweet water table rises, consequently, saline seawater cannot cross the threshold in the nearby area of the seashore. In such a way, the dispersion of contaminated water plays key role to solve salinity problem. The immiscible dispersion also plays an important role in petroleum engineering during secondary oil recovery process, in which water or gas is injected in oil formatted area to drive the oil towards production well. An unconditionally stable Crank-Nicolson finite difference scheme has been employed to find the concentration C(X, T) of salty or contaminated water dispersion in uni-direction. The outcome is consistent with physical phenomenon of longitudinal dispersion in miscible fluid flow through porous media. It is concluded, that the concentration C(X, T) decreases as distance X as well as time T increases. The tables and graphs are developed by using MATLAB coding

Lively Streets: Exploring the relationship between built environment and social behavior

Streets constitute a significant part of open public space and are the most important symbols of the public realm. Streets that cater to the functional, social, and leisure needs of people have been positively associated with economic growth, physical health of people, and a sense of community. Increasingly, scholars suggest thinking of the street as a social space rather than just a channel for movement. Despite such suggestions, few studies have addressed the relationships between social behavior and the environmental quality of the street. Moreover, the studies that have, tend to separate the study of physical features from land uses, and hence do not deal with the interrelationships between behavioral patterns and the physical features of the street, and its sociability. This dissertation was an empirical examination of behavioral responses, perceptions, and attitudes of people to the physical characteristics, use, and management of the neighborhood commercial street in two cities and one town in the Boston metropolitan area. It used methods based in environment-behavior sciences involving extensive observations of these streets over eight months, and interviews with people using these streets to understand their behaviors and perceptions. The biggest competitive advantage of neighborhood commercial streets is their ability to support social interaction. The findings reveal that people were equally concerned with the social and physical dimensions of the street. The presence of community places and the street's landuse and physical character determined the use of the street. People preferred settings that had stores that were community-gathering places, which held special collective meanings for the people of the neighborhood and were thus destinations to meet friends and to see other people and activities; that had a variety of stores on the block, particularly those that served daily shopping needs; that had unique independently operated stores with friendly service, a distinctive character and ambience, and personalized shop-windows and entrances; that were pedestrian-friendly with ample sidewalk space with seating and other street furniture, and shade and shelter; and that had buildings with permeable and articulated street facades providing sheltered small-scale spaces

MalDet-Malware Detection Using Deep Learning and LSTM based Approach to Classify Malware

Computer security requires malware detection. Recent research manually uncovers hazardous features using machine learning-based techniques. MalDet, a cutting-edge malware detection method, is recommended in this paper. MalDet classifies malware using a stacking ensemble and learns from grayscale images and opcode sequences using CNN and LSTM networks. According to the evaluation, MalDet's malware detection validation accuracy is 99.89%. MalDet outperforms other previous research with 99.36% detection accuracy and a significant detection speedup on the Microsoft malware dataset. We classified nine malware families for MalDet