Terrain-Specific: Contemporary Landscape Architecture in Canada

The Canadian landscape has typically captured a global imaginary of a pristine wild, but how might its urban designed landscapes be distinctly understood? Foregrounded by the landscape transformations accelerated by climate change, the book Innate Terrain: Canadian Landscape Architecture, edited by Professor Alissa North from the University of Toronto, highlights landscape architecture projects situated on the unique Canadian terrain. Providing further provocation on Canadian landscape architecture, Innate Terrain seeks to fill the literary gap on contemporary landscape perspectives, distinguishing Canadian landscape architecture from global practice, and particularly, its well-documented American counterpart. Landscape architecture in the Canadian context has evolved and established its own distinct identity, one imbued with national and local sensitivities. Informed by diverse environmental and cultural contexts, Canadian-designed landscapes reflect and refer to the prevailing ecosystems of Canada’s innate terrain. Contrary to the preceding International Style, landscape architecture projects in Canada have adopted the ethos of Critical Regionalism in the second half of the 20th century. Contemporary Canadian practitioners are designing landscapes that are deeply informed by their surrounding geographical context while emphasizing cultural specificity. Central to this cultural specificity, addressed by a new generation of landscape architects, is the increasing recognition of Indigenous Traditional Knowledge within the discipline. Canadian landscape architects have collaborated with First Nations, Inuit, and Métis communities, including the keepers of this knowledge, to develop land management strategies and design landscape interventions.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.     

An efficient implicit regularized Lagrangian twin support vector regression

Twin support vector regression (TSVR) and Lagrangian TSVR (LTSVR) satisfy only empirical risk minimization principle. Moreover, the matrices in their formulations are always positive semi-definite. To overcome these problems, we propose an efficient implicit Lagrangian formulation for the dual regularized twin support vector regression, called IRLTSVR for short. By introducing a regularization term to each objective function, the optimization problems in our IRLTSVR are positive definite and implement the structural risk minimization principle. Moreover, the 1-norm of the vector of slack variable is replaced with 2-norm to make the objective functions strongly convex. Our IRLTSVR solves two systems of linear equations instead of solving two quadratic programming problems (QPPs) in TSVR and one large QPP in SVR, which makes the learning speed of IRLTSVR faster than TSVR and SVR. Particularly, we compare three implementations of IRLTSVR with existing approaches. Computational results on several synthetic and real-world benchmark datasets clearly indicate the effectiveness and applicability of the IRLTSVR in comparison to SVR, TSVR and LTSVR.     

ADJACENT-ERROR CORRECTING BINARY PERFECT CODES

Abstract

A new type of codes that correct only a given number of adjacent errors in the binary case have been introduced in this paper. These codes are adjacent-error correcting perfect and form a doubly infinite class, i.e., for a given number of adjacent errors these exist for different lengths as also for different number of adjacent errors these can be developed for specific lengths.     

Neural network based non-invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time-consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non-invasive method, but color perception may vary from person to person. This study aims to develop a computerized non-invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non-invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.     

New pseudo-dynamic analysis of two-layered cohesive-friction soil slope and its numerical validation

Natural slopes consist of non-homogeneous soil profiles with distinct characteristics from slopes made of homogeneous soil. In this study, the limit equilibrium modified pseudo-dynamic method is used to analyze the stability of two-layered c-φ soil slopes in which the failure surface is assumed to be a logarithmic spiral. The zero-stress boundary condition at the ground surface under the seismic loading condition is satisfied. New formulations derived from an analytical method are proposed for the predicting the seismic response in two-layered soil. A detailed parametric study was performed in which various parameters (seismic accelerations, damping, cohesion, and angle of internal friction) were varied. The results of the present method were compared with those in the available literature. The present analytical analysis was also verified against the finite element analysis results.