Motion planning of a mechanical snake using neural networks

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 88-90In this thesis, an optimal strategy is developed to get a mechanical snake (a robot composed of a sequence of articulated links), which is located arbitrarily in an enclosed region, out of the region through a specified exit without violating certain constraints. This task is done in two stages: Finding an optimal path that can be tracked, and tracking the optimal path found. Each stage is implemented by a neural network. Neural network of the second stage is constructed by direct evaluation of the weights after designing an efficient structure. Two independent neural networks are designed to implement the first stage, one trained to implement an algorithm we have derived to generate minimal paths and the other trained using multi-stage neural network approach. For the second design, the intuitive multi-stage neural network back propagation approach in the literature is formalized.Fidan, BarışM.S

SAUNALARIN ÖNEMİ VE TARİHSEL GELİŞİMİ

Doğada, sıcak ve soğuk olmak üzere birbirine zıt iki kutup vardır. Sauna bu tezatların bir uygulama mekanıdır. Sauna, hava ve su vasıtasıyla soğutulan bir sıcak hava banyosudur. Bu banyo ile insan bedeni, soğuk ile sıcak arasındaki doğanın mücadelesini küçük çapta da olsa uygulamasını yaşamaktadır. Buna göre; sauna, sıcak ile soğuğun etkileri arasındaki sonsuz çekişmeyi temsil etmektedir. Sauna, bu iki büyük tesiri ideal bir şekilde bünyesinde toplamaktadır.Bu nedenle, özellikle az güneş gören Finlandiya gibi kuzey ülkelerinde ve Avrupa’da yaygın bir şekilde kullanılan, ancak ekonomik ve kültürel nedenlerden dolayı ülkemizde çok fazla bilinmeyen saunanın tanıtılması amaçlanmıştır.Bu çalışmada, saunaların tarihsel gelişimi, önemi, özellikleri, çeşitleri, sınıflandırılması ve faydaları hakkında literatür taramaları yapılmıştır

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments

Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions

Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations. We propose a method that transforms the recorded motion sensor sequences invariantly to sensor unit orientation. The method is based on estimating the sensor unit orientation and representing the sensor data with respect to the Earth frame. We also calculate the sensor rotations between consecutive time samples and represent them by quaternions in the Earth frame. We incorporate our method in the pre-processing stage of the standard activity recognition scheme and provide a comparative evaluation with the existing methods based on seven state-of-the-art classifiers and a publicly available dataset. The standard system with fixed sensor unit orientations cannot handle incorrectly oriented sensors, resulting in an average accuracy reduction of 31.8%. Our method results in an accuracy drop of only 4.7% on average compared to the standard system, outperforming the existing approaches that cause an accuracy degradation between 8.4 and 18.8%. We also consider stationary and non-stationary activities separately and evaluate the performance of each method for these two groups of activities. All of the methods perform significantly better in distinguishing non-stationary activities, our method resulting in an accuracy drop of 2.1% in this case. Our method clearly surpasses the remaining methods in classifying stationary activities where some of the methods noticeably fail. The proposed method is applicable to a wide range of wearable systems to make them robust against variable sensor unit orientations by transforming the sensor data at the pre-processing stage

Localization and Tracking of Implantable Biomedical Sensors

Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems