Early Twentieth Century Town Planning Improvements in New Zealand

The first decades of the twentieth century were critical for an introduction of town planning views in New Zealand. This was the time when the country was moving in a natural progression from solving the basic problems of establishing new settlements, through extensive infrastructural works (construction of roads and railroads; introduction of electricity and tramways, etc), to the point of early re-evaluations and criticisms of the achieved results. Concurrently the British town planning concepts were, for the first time, discussed among New Zealand architectural professionals and other interested parties as the New Zealand cities and towns were actively being constructed with new and improved infrastructure. This period provided ample opportunity to set up models that could influence future urban developments. This paper will discuss the professional town planning developments in relation to the broader context of urban improvements of the same period. It will explore the concepts that underpinned the developments and evaluate the lasting effects they have had on the development of New Zealand towns and cities

Razvoj hijerarhijske strukture upravljanja mobilnim robotom za praćenje ljudi na bazi robusne stereo robotske vizije

The main topic of this doctoral thesis refers to the development and implementation of the hierarchical control structure in which the algorithms are being executed on the high-level control. By applying the stochastic methods in robotic vision to these algorithms, we can detect people, estimate their position, follow them and recognise their actions in order to carry out tasks where the robot behaves like a human's collaborator. In this thesis, some solutions are offered and present a step forward towards solving the problems that robotic vision system that provides reliable inputs to the control module of the mobile human-collaboration robot, is facing. The robust vision module for human tracking which can be applied in various applications where is necessary for robots to work together with humans and which can be applied on different types of mobile robots, was developed. In this thesis, it is devoted a special attention to the integration, testing and experimental verification of the stochastic algorithms for human tracking such as Kaman and Particle filters, as well as a comparative analysis of algorithms for solving problems of robotic people tracking. A part of the research presented in this thesis is based on a scientific collaboration between the researchers from the Faculty of Mechanical Engineering of Nis and the researchers from the Institute of Automation (IAT) of the University of Bremen. The stereo vision module for a human detection that was developed at the Institute of Automatic Control, University of Bremen (IAT), was used for testing the tracking module which was developed in this thesis. Beside the detection system, the systems for human detection that use 3D sensors, such as the Asus Xtion PRO LIVE 3D sensor were used. The main focus of this thesis was the development of a simulation environment and its control system, as well as the development of modules for human tracking, estimation of a human position and recognition of a human behavior. The simulation environment represents the support to the development and implementation of the real world control system. By adding the appropriate modifications, other mobile robots can easily use this simulation environment. The developed algorithms are evaluated on Faculty of Mechanical Engineering, University of Niš as a part of the doctoral dissertation. In this dissertation, advanced hierarchical control was implemented for the purpose of controlling the mobile robot DaNI, developed by the company National Instruments. This advanced hierarchical control was implemented by using 3D sensor Asus Xtion Pro Live which in the laboratory experimental scenario represents the robotic vision sensor for the detection modules and human tracking. In addition, at the IAT, the vision module which consists of two sub-modules was implemented. These two sub-modules are the stereo vision for a human detection and the tracking module based on the Kalman filter developed in this doctoral thesis

Outage minimization of energy-harvesting wireless sensor network supported by UAV

Due to their adaptability, mobility, and capacity to offer an ideal channel, unmanned aerial vehicles (UAVs) have become a potential option for wireless power transfer and data collection in wireless sensor networks (WSNs). This paper examines energy-constrained WSNs, where data transfer to the data center is facilitated by UAV and sensors rely on radio frequency (RF) energy obtained by a Power Beacon (PB). However, due to energy limitations, sensors can only send data using the harvested energy. We consider a WSN in which the nodes are randomly distributed within a circular area, with the PB placed at the center of the WSN. To evaluate the system performance, we consider the dynamic nature of the wireless channel, which includes factors such as signal reflection, scattering, and diffraction. Through numerical analysis and simulations, the main aim is to identify the optimal system parameters that minimize the outage probability. This analysis provides valuable insights for designing more effective and reliable energy-harvesting WSNs with UAV as data collector. By leveraging UAV in WSNs, system performance can be improved, ensuring data transmission to destination nodes placed at a large distance from the WSN

INPUT VECTOR IMPACT ON SHORT-TERM HEAT LOAD PREDICTION OF SMALL DISTRICT HEATING SYSTEM

Short-term load prediction is very important for advanced decision making in district heating systems. The idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. The common thing for each way of prediction is usage of historical data for certain last period which makes possible development of many methodologies for adequate prediction and control. In this paper, application of feedforward artificial neural network for short-term load prediction for period of 1, 3 and 7 days, of one small district heating system, is presented. Three different input vectors are implemented and their impact on quality of prediction discussed. The simulation results are compared and detailed analysis is done where operation in transient regime is of special importance. Satisfied prediction average error is obtained

FUZZY CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT FOR MOVING TARGET TRACKING

Tracking of moving objects, including humans has important role in mobile robotics. In this paper, the hierarchical control structure for target/human tracking consisted of high and low level control was presented. The low level subsystem deals with the control of the linear and angular velocities using multivariable PD controller whose parameters are obtained by Particle swarm optimization. The position control of the mobile robot represents the high level control, where we use two fuzzy logic Mamdani controllers for distance and angle control. In order to test the effectiveness of the proposed control scheme a simulation was performed. Two cases, when the mobile robot pursues a target moving along a circular path and when the mobile robot pursues a target moving along a rectangle path, were simulated

Vision-Based Inspection of Tyre Tread Depth

In this paper, an approach for visual, non-contact automatic inspection of tyre tread depth based on existing image processing techniques is presented. Histograms of oriented gradient are used for feature extraction from images. In order to analyse which set of features gives the best classification results, a linear support-vector machine classifier was trained and tested using different numbers of pixels and numbers of cells per block. The obtained processing and experimental results are presented in this paper

Hypericum perforatum L. extracts exert cytotoxic effects and show different miRNA signatures in PC-3 and DU 145 prostate cancer cells

Phytochemicals and bioactive substances derived from a wide range of plant extracts have been reported to exert various anticancer effects. Prostate cancer is one of the leading causes of cancer-related deaths within the male population. Prostate cancer-specific miRNA signatures were associated with cancer formation and progression, with various subtypes, and response to therapy. MicroRNA levels of expression were shown to change after the treatment of various compounds and substances extracted from natural products. Natural herbal compounds were shown to induce variations in miRNA expression levels in cancer cells. The aims of this study were to investigate the cytotoxic effects of methanol, ethyl-acetate, and hexane extracts obtained from branch-body part and flowers of Hypericum perforatum L. against humane PC-3 and DU 145 and to test potential miRNA-128/133b/155/193a/206/21/335 signature changes and differences between the two prostate cancer cell lines. Cytotoxic activity of H. perforatum extracts, their effects on cell cycle distribution, and miRNA expression levels were examined in humane PC-3 and DU 145 prostate cancer cells by MTT cell survival assay, flow cytometry, and quantitative real-time PCR. Hexane extract of flowers showed the strongest intensity of cytotoxic activity against PC-3 and DU 145 cells. The highest increase in the percentage of PC-3 cells in the subG1 phase was observed in cell samples treated with hexane extract of flowers and branch-body part. Significant differences in miRNA-128/133b/155/193a/206/21/335 levels were observed between PC-3 and DU 145 cell lines, especially in samples treated with flower extracts compared with the branch-body part. Conclusions: Investigated extracts have significant anticancer potential not only from the aspects of cytotoxicity and cell cycle effects but also from the aspect of lowering oncogenic or increasing tumor-suppressive miRNAs. The best effect might be the increase of tumor-suppressive miR-128 (accompanied by miR-193a) induced by the hexane extract of the flowers, which also exerted the highest cytotoxic activity. Hexane extract of flowers may be the candidate for further investigation for improving the efficiency of standard therapies for PCa. A miRNA signature might be cell-type specific after the treatment with H. perforatum extracts

Gaussian Regression Process for Prediction of Compressive Strength of Thermally Activated Geopolymer Mortars

The primary objective of this research is the development of a prediction model of the compressive strength of geopolymer mortars made with fly ash and granular slag which hardened in different curing conditions. Data for the numerical analysis were obtained by experimental research; for this purpose 45 series of geopolymer mortars were made, 9 of which were cured in ambient conditions at a temperature of 22 °С, and the remaining were exposed to thermal activation for a duration of 24 h at the temperatures of 65 °С, 75 °С, 85 °С and 95 °С. Using machine learning, a Gaussian regression method was developed in which the curing temperature and the percentage mass content of fly ash and granular slag were used as input parameters, and the compressive strength as the output. Based on the results of the developed model, it can be concluded that the Gaussian regression process can be used as a reliable regression method for predicting the compressive strength of geopolymer mortars based on fly ash and granular slag

COMPUTATION OF THE SHORTEST DISTANCE BETWEEN TWO PARAMETRIC DEFINED OBJECTS BY PARTICLE SWARM OPTIMIZATION

The distance computation between objects is an essential component of robot motion planning and controlling the robot to avoid its surrounding obstacles. Distance is used as a measure of how far a robot is from colliding with an obstacle. In this paper a Particle Swarm Optimization algorithm (PSO) for solving the problem of the distance computation between convex objects is presented. Convergence analysis of the suggested method was done via difference equation

KALMAN FILTER AND NARX NEURAL NETWORK FOR ROBOT VISION BASED HUMAN TRACKING

Tracking human is an important and challenging problem in video-based intelligent robot systems. In this paper, a vision-based human tracking system is supposed to provide sensor input for vision-based control of a mobile robot that works in a team helping the human co-worker. A comparison between NARX neural network and Kalman filter in solving the prediction problem of human tracking in robot vision is presented. After collecting video data from a robot, simulation results obtained from the Kalman filter model are used to compare with the simulation results obtained from the NARX Neural network.Key words: robot vision, Kalman filter, neural networks, human trackin