EFFICIENT CAMERA SELECTION FOR MAXIMIZED TARGET COVERAGE IN UNDERWATER ACOUSTIC SENSOR NETWORKS

In Underwater Acoustic Sensor Networks (UWASNs), cameras have recently been deployed for enhanced monitoring. However, their use has faced several obstacles. Since video capturing and processing consume significant amounts of camera battery power, they are kept in sleep mode and activated only when ultrasonic sensors detect a target. The present study proposes a camera relocation structure in UWASNs to maximize the coverage of detected targets with the least possible vertical camera movement. This approach determines the coverage of each acoustic sensor in advance by getting the most applicable cameras in terms of orientation and frustum of camera in 3-D that are covered by such sensors. Whenever a target is exposed, this information is then used and shared with other sensors that detected the same target. Compared to a flooding-based approach, experiment results indicate that this proposed solution can quickly capture the detected targets with the least camera movement

In Pursuit of Aviation Cybersecurity: Experiences and Lessons From a Competitive Approach

The passive and independent localization of aircraft has been the subject of much cyberphysical security research. We designed a multistage open competition focusing on the offline batch localization problem using opportunistic data sources. We discuss setup, results, and lessons learned

Semantic In-Network Complex Event Processing for an Energy Efficient Wireless Sensor Network

Wireless Sensor Networks (WSNs) consist of spatially distributed sensor nodes that perform monitoring tasks in a region and the gateway nodes that provide the acquired sensor data to the end user. With advances in the WSN technology, it has now become possible to have different types of sensor nodes within a region to monitor the environment. This provides the flexibility to monitor the environment in a more extensive manner than before. Sensor nodes are severely constrained devices with very limited battery sources and their resource scarcity remains a challenge. In traditional WSNs, the sensor nodes are used only for capturing data that is analysed later in more powerful gateway nodes. This continuous communication of data between sensor nodes and gateway nodes wastes energy at the sensor nodes, and consequently, the overall network lifetime is greatly reduced. Existing approaches to reduce energy consumption by processing at the sensor node level only work for homogeneous networks. This thesis presents a sensor node architecture for heterogeneous WSNs, called SEPSen, where data is processed locally at the sensor node level to reduce energy consumption. We use ontology fragments at the sensor nodes to enable data exchange between heterogeneous sensor nodes within the WSN. We employ a rule engine based on a pattern matching algorithm for filtering events at the sensor node level. The event routing towards the gateway nodes is performed using a context-aware routing scheme that takes both the energy consumption and the heterogeneity of the sensor nodes into account. As a proof of concept, we present a prototypical implementation of the SEPSen design in a simulation environment. By providing semantic support, in-network data processing capabilities and context-aware routing in SEPSen, the sensor nodes (1) communicate with each other despite their different sensor types, (2) filter events at the their own level to conserve the limited sensor node energy resources and (3) share the nodes' knowledge bases for collaboration between the sensor nodes using node-centric context-awareness in changing conditions. The SEPSen prototype has been evaluated based on a test case for water quality management. The results from the experiments show that the energy saved in SEPSen reaches almost 50% by processing events at the sensor node level and the overall network lifetime is increased by at least a factor of two against the shortest-path-first (Min-Hop) routing approach

Mobile Brain and Body Imaging during Walking Motor Tasks

Mobile brain and body imaging (MoBI) presents new and promising methods for moving traditional research studies out of a controlled laboratory and into the real world. Most current neuroimaging techniques require subjects to be stationary in laboratory settings because of both hardware and software limitations. Recent developments in mobile brain imaging have utilized Electroencephalography (EEG) in conjunction with advanced signal processing techniques such as Independent Component Analysis (ICA) to overcome these obstacles and study humans doing complex tasks in non-traditional environments. In my first study, I used high density EEG to examine the cortical dynamics of subjects walking on a split-belt treadmill with legs moving independently of each other at different speeds to investigate how humans adapt to novel perturbations. I found significantly increased low and high frequency spectral power across all sensorimotor and parietal neural sources during split-belt adaptation compared to normal walking, which provides insight into the brain areas and patterns used to accommodate locomotor adaptation. In my second study I combined multi-modal sensing and biometric devices including EEG, eye tracking, heart rate, accelerometers, and salivary cortisol into a portable setup that subjects wore indoors on a treadmill using virtual reality as well as outdoors in a public arboretum. Subjects walked for 1 hour each indoors and outdoors while completing a free viewing visual search oddball task in virtual reality and in real life. I reported on the methods for how to set this experiment up, synchronize all data, and standardize the data in order to make it usable as an open access dataset that has been made available to the public online. My third study used this data set to examine the P300 event-related potential response during both indoors in virtual reality and outdoors in the arboretum. I found a significantly increased amplitude response between 250 to 400 ms across the centro-parietal electrodes that distinguished target flags from distractor flags during visual search for both indoor and outdoor environments. And finally, for my fourth study I used the same data set to look at the behavioral and neural correlates associated with gait dynamics when subjects walked indoors on a treadmill vs outdoors in variable terrain while also doing the visual search task. I found significant EEG power differences across multiple neural sources that showed increased spectral fluctuations throughout the gait cycle when subjects walked outdoors compared to indoors on a treadmill. The collective studies in this dissertation present new ways of using mobile brain and body imaging devices to expand our knowledge of the neural dynamics involved in humans moving in complex ways and in variable environments outside of traditional laboratories.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147691/1/ghanada_1.pd

Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems

자동차 사양 변경을 실시간 반영하는 데이터 기반 디자인 접근 방법

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2020. 8. 곽노준.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines. Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance. The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers. The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders. The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle. The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process. In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study. The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.자동차 산업은 친환경 전기/수소 자동차의 확대와 제조 공정에서의 모듈 단순화를 통해서 외부 환경의 변화에 따른 새로운 국면을 맞이하고 있다. 하지만 기존의 자동차 산업에서 구조화된 생산 가이드라인과 기간 단위 생산 계획에 맞춰진 여러 이해관계자들과의 갈등은 변화에 대응하는 방안이 관성과 부딪히는 문제로 나타날 수 있다. 예를 들어, 갑작스럽게 생산에 필요한 부품을 변경해야 하거나 특정 상황에 적용되는 디자인을 변경할 경우, 주어진 가이드라인에 따라 디자이너가 직접 의견을 반영하기 어려운 경우가 많다. 자동차 디자인은 차종의 철학과 이념을 나타내고 해당 차량제원으로 최대의 가치를 끌어내고자 하는 종합적인 과정이다. 본 연구에서는 여러 원천의 데이터를 기반으로 자동차 디자인 과정에서 활용할 수 있도록 디자인에 필요한 부품/모듈 구성요소들에 대한 정보를 실시간으로 표시해주는 시스템을 고안하였다. 이를 적용하여 자동차 디자인 과정에서 예상 못한 외부 문제가 발생했을 때 선택할 구성 부품을 대체하거나 사전에 해당 부품을 이해하고 디자인에 활용할 수 있도록 세 가지 정보 제공 채널을 구성하였다. 첫 번째는 자동차 공장 내 실시간 데이터 집계를 Google Analytics를 활용하여 시각화하고, 이를 공장 자체의 자가 성장 캐릭터에 반영하여 디자이너에게 제공하는 방식이다. 이를 통해 종합상황실 등의 복잡한 인력 체계 없이도 생산 및 품질 현황 데이터를 실시간으로 확인 가능하도록 하였다. 두 번째는 차량용 주차보조 센서 카메라를 차량 부착 뿐만 아니라 인벤토리와 물류센터의 CCTV에도 적용하여 주변상황을 인식하고 분석할 수 있도록 구성하였다. 차량의 조립 생산 단계에서 부품 단위의 이동, 운송, 출하를 거쳐 완성차의 주행 단계에 이르기까지 데이터 흐름을 파악하는 것이 디자인 부문에 필요한 정보를 제공할 수 있는 방법으로 활용되었다. 이를 통해 기존 이해관계자들의 큰 반발 없이 내부의 카메라 기능으로부터 부품 리소스와 운송 상태를 실시간 파악 및 기록 가능하도록 하였다. 마지막으로 공공 API와 센서 기반의 사물인터넷을 활용해서 도로 위 차량 사고가 발생한 위치에서의 현장 수리를 위한 차량 부품 즉시 수급 및 데이터베이스화 방법도 개발 되었다. 이는 디자이너로 하여금 가벼운 접촉 사고에서의 부품 교체 행태에 대한 정보를 얻게 하여 차량의 디자인에 반영 가능하도록 하였다. 시나리오를 바탕으로 이 세 가지 정보 제공 채널을 활용할 경우, 자동차 디자인 과정에서 불러들여오는 부품 및 모듈의 구성 요소들을 디자이너가 정확히 알고 반영할 수 있다는 장점이 부각되었다. 정보 제공의 인터페이스를 쉽게 구성하기 위해서, 실제로 디자이너들이 자동차 개발 과정에서 디자인 프로세스 상에서 활용하는 CAD software에 세 가지 채널들로부터 들어오는 정보를 사례별 컬러로 표시하고, 이를 시선추적 사용성 평가를 통해 현업 디자이너들이 사용하기 쉽게 개선한 과정도 본 연구에 포함시켜 설명하였다.1 Introduction 1 1.1 Research Background 1 1.2 Objective and Scope 2 1.3 Environmental Changes 3 1.4 Research Method 3 1.4.1 Causal Inference with Graphical Model 3 1.4.2 Design Thinking Methodology with Co-Evolution 4 1.4.3 Required Resources 4 1.5 Research Flow 4 2 Data-driven Design 7 2.1 Big Data and Data Management 6 2.1.1 Artificial Intelligence and Data Economy 6 2.1.2 API (Application Programming Interface) 7 2.1.3 AI driven Data Management for Designer 7 2.2 Datatype from Automotive Industry 8 2.2.1 Data-driven Management in Automotive Industry 8 2.2.2 Automotive Parts Case Studies 8 2.2.3 Parameter for Generative Design 9 2.3 Examples of Data-driven Design 9 2.3.1 Responsive-reactive 9 2.3.2 Dynamic Document Design 9 2.3.3 Insignts from Data-driven Design 10 3 Benchmark of Data-driven Automotive Design 12 3.1 Method of Global Benchmarking 11 3.2 Automotive Design 11 3.2.1 HMI Design and UI/UX 11 3.2.2 Hardware Design 12 3.2.3 Software Design 12 3.2.4 Convergence Design Process Model 13 3.3 Component Design Management 14 4 Vehicle Specification Design in Mobility Industry 16 4.1 Definition of Vehicle Specification 16 4.2 Field Study 17 4.3 Hypothesis 18 5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21 5.1 Production Level 31 5.1.1 Background and Input 31 5.1.2 Data Process from Inventory to Designer 41 5.1.3 Output to Designer 51 5.2 Delivery Level 61 5.2.1 Background and Input 61 5.2.2 Data Process from Inventory to Designer 71 5.2.3 Output to Designer 81 5.3 Consumer Level 91 5.3.1 Background and Input 91 5.3.2 Data Process from Inventory to Designer 101 5.3.3 Output to Designer 111 6 Two Applications for Vehicle Designer 86 6.1 Real-time Dashboard DB for Decision Making 123 6.1.1 Searchable Infographic as a Designer's Tool 123 6.1.2 Scope and Method 123 6.1.3 Implementation 123 6.1.4 Result 124 6.1.5 Evaluation 124 6.1.6 Summary 124 6.2 Application to CAD for vehicle designer 124 6.2.1 CAD as a Designer's Tool 124 6.2.2 Scope and Method 125 6.2.3 Implementation and the Display of the CAD Software 125 6.2.4 Result 125 6.2.5 Evaluation: Usability Test with Eyetracking 126 6.2.6 Summary 128 7 Conclusion 96 7.1 Summary of Case Studies and Application Release 129 7.2 Impact of the Research 130 7.3 Further Study 131Docto

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

PREDICTION OF RESPIRATORY MOTION

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier

Establishing a Framework for the development of Multimodal Virtual Reality Interfaces with Applicability in Education and Clinical Practice

The development of Virtual Reality (VR) and Augmented Reality (AR) content with multiple sources of both input and output has led to countless contributions in a great many number of fields, among which medicine and education. Nevertheless, the actual process of integrating the existing VR/AR media and subsequently setting it to purpose is yet a highly scattered and esoteric undertaking. Moreover, seldom do the architectures that derive from such ventures comprise haptic feedback in their implementation, which in turn deprives users from relying on one of the paramount aspects of human interaction, their sense of touch. Determined to circumvent these issues, the present dissertation proposes a centralized albeit modularized framework that thus enables the conception of multimodal VR/AR applications in a novel and straightforward manner. In order to accomplish this, the aforesaid framework makes use of a stereoscopic VR Head Mounted Display (HMD) from Oculus Rift©, a hand tracking controller from Leap Motion©, a custom-made VR mount that allows for the assemblage of the two preceding peripherals and a wearable device of our own design. The latter is a glove that encompasses two core modules in its innings, one that is able to convey haptic feedback to its wearer and another that deals with the non-intrusive acquisition, processing and registering of his/her Electrocardiogram (ECG), Electromyogram (EMG) and Electrodermal Activity (EDA). The software elements of the aforementioned features were all interfaced through Unity3D©, a powerful game engine whose popularity in academic and scientific endeavors is evermore increasing. Upon completion of our system, it was time to substantiate our initial claim with thoroughly developed experiences that would attest to its worth. With this premise in mind, we devised a comprehensive repository of interfaces, amid which three merit special consideration: Brain Connectivity Leap (BCL), Ode to Passive Haptic Learning (PHL) and a Surgical Simulator

Robot Planning in Adversarial Environments Using Tree Search Techniques

One of the main advantages of robots is that they can be used in environments that are dangerous for humans. Robots can not only be used for tasks in known and safe areas but also in environments that may have adversaries. When planning the robot's actions in such scenarios, we have to consider the outcomes of a robot's actions based on the actions taken by the adversary, as well as the information available to the robot and the adversary. The goal of this dissertation is to design planning strategies that improve the robot's performance in adversarial environments. Specifically, we study how the availability of information affects the planning process and the outcome. We also study how to improve the computational efficiency by exploiting the structural properties of the underlying setting. We adopt a game-theoretic formulation and study two scenarios: adversarial active target tracking and reconnaissance in environments with adversaries. A conservative approach is to plan the robot's action assuming a worst-case adversary with complete knowledge of the robot's state and objective. We start with such a "symmetric" information game for the adversarial target tracking scenario with noisy sensing. By using the properties of the Kalman filter, we design a pruning strategy to improve the efficiency of a tree search algorithm. We investigate the performance limits of the asymmetric version where the adversary can inject false sensing data. We then study a reconnaissance scenario where the robot and the adversary have symmetric information. We design an algorithm that allows a robot to scan more area while avoiding being detected by the adversary. The symmetric adversarial model may yield too conservative plans when the adversary may not have the same information as the robot. Furthermore, the information available to the adversary may change during execution. We then investigate the dynamic version of this asymmetric information game and show how much the robot can exploit the asymmetry in information using tree search techniques. Specifically, we study scenarios where the information available to the adversary changes during execution. We devise a new algorithm for this asymmetric information game with theoretical performance guarantees and evaluate those approaches through experiments. We use qualitative examples to show how the new algorithm can outperform symmetric minimax and use quantitative experiments to show how much the improvement is