Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure

Improving Location Accuracy And Network Capacity In Mobile Networks

Todays mobile computing must support a wide variety of applications such as location-based services, navigation, HD media streaming and augmented reality. Providing such services requires large network bandwidth and precise localization mechanisms, which face significant challenges. First, new (real-time) localization mechanisms are needed to locate neighboring devices/objects with high accuracy under tight environment constraints, e.g. without infrastructure support. Second, mobile networks need to deliver orders of magnitude more bandwidth to support the exponentially increasing traffic demand, and adapt resource usage to user mobility.In this dissertation, we build effective and practical solutions to address these challenges. Our first research area is to develop new localization mechanisms that utilize the rich set of sensors on smartphones to implement accurate localization systems. We propose two designs. The first system tracks distance to nearby devices with centimeter accuracy by transmitting acoustic signals between the devices. We design robust and efficient signal processing algorithms that measure distances accurately on the fly, thus enabling real-time user motion tracking. Our second system locates a transmitting device in real-time using commodity smart- phones. Driving by the insight that rotating a wireless receiver (smartphone) around a users body can effectively emulate the sensitivity and functionality of a directional antenna, we design a rotation-based measurement algorithm that can accurately predict the direction of the target transmitter and locate the transmitter with a few measurements.Our second research area is to develop next generation mobile networks to significantly boost network capacity. We propose a drastically new outdoor picocell design that leverages millimeter wave 60GHz transmissions to provide multi-Gbps bandwidth for mobile users. Using extensive measurements on off-the-shelf 60GHz radios, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths on 60GHz, and show that 60GHz outdoor picocells are indeed a feasible approach for delivering orders of magnitude increase in network capacity.Finally, we seek to capture and understand user mobility patterns which are essential in mobile network design and deployment. While traditional methods of collecting human mobility traces are expensive and not scalable, we explore a new direction that extracts large-scale mobility traces through widely available geosocial datasets, e.g. Foursquare "check-in" datasets. By comparing raw GPS traces against Foursquare checkins, we analyze the value of using geosocial datasets as representative traces of human mobility. We then develop techniques to both "sanitize" and "repopulate" geosocial traces, thus producing detailed mobility traces more indicative of actual human movement and suitable for mobile network design

Practical Methods for Optimizing Equipment Maintenance Strategies Using an Analytic Hierarchy Process and Prognostic Algorithms

Many large organizations report limited success using Condition Based Maintenance (CbM). This work explains some of the causes for limited success, and recommends practical methods that enable the benefits of CbM. The backbone of CbM is a Prognostics and Health Management (PHM) system. Use of PHM alone does not ensure success; it needs to be integrated into enterprise level processes and culture, and aligned with customer expectations. To integrate PHM, this work recommends a novel life cycle framework, expanding the concept of maintenance into several levels beginning with an overarching maintenance strategy and subordinate policies, tactics, and PHM analytical methods. During the design and in-service phases of the equipment’s life, an organization must prove that a maintenance policy satisfies specific safety and technical requirements, business practices, and is supported by the logistic and resourcing plan to satisfy end-user needs and expectations. These factors often compete with each other because they are designed and considered separately, and serve disparate customers. This work recommends using the Analytic Hierarchy Process (AHP) as a practical method for consolidating input from stakeholders and quantifying the most preferred maintenance policy. AHP forces simultaneous consideration of all factors, resolving conflicts in the trade-space of the decision process. When used within the recommended life cycle framework, it is a vehicle for justifying the decision to transition from generalized high-level concepts down to specific lower-level actions. This work demonstrates AHP using degradation data, prognostic algorithms, cost data, and stakeholder input to select the most preferred maintenance policy for a paint coating system. It concludes the following for this particular system: A proactive maintenance policy is most preferred, and a predictive (CbM) policy is more preferred than predeterminative (time-directed) and corrective policies. A General Path prognostic Model with Bayesian updating (GPM) provides the most accurate prediction of the Remaining Useful Life (RUL). Long periods between inspections and use of categorical variables in inspection reports severely limit the accuracy in predicting the RUL. In summary, this work recommends using the proposed life cycle model, AHP, PHM, a GPM model, and embedded sensors to improve the success of a CbM policy

Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra

Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries

Competent Program Evolution, Doctoral Dissertation, December 2006

Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability

Protecting the infrastructure: 3rd Australian information warfare & security conference 2002

The conference is hosted by the We-B Centre (working with a-business) in the School of Management Information System, the School of Computer & Information Sciences at Edith Cowan University. This year\u27s conference is being held at the Sheraton Perth Hotel in Adelaide Terrace, Perth. Papers for this conference have been written by a wide range of academics and industry specialists. We have attracted participation from both national and international authors and organisations. The papers cover many topics, all within the field of information warfare and its applications, now and into the future. The papers have been grouped into six streams: • Networks • IWAR Strategy • Security • Risk Management • Social/Education • Infrastructur

A systems approach to engineering cancer nanotechnologies

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2010.Vita. Cataloged from PDF version of thesis.Includes bibliographical references (p. 203-210).therapy. Over the past three decades, advances in nanomaterial synthesis have produced impressive nanostructures with unique electromagnetic and therapeutic properties. These represent a powerful toolkit of building blocks through which multi-component nanosystems could be constructed. Yet, while biological systems produce higher-order functions through coordinated interactions between multiple nanoscale components, biomedical nanotechnologies to date have largely lacked systems-scale complexity. Considering that typical in vivo doses of diagnostic or therapeutic nanoparticles exceed I trillion nanoparticles, there is considerable opportunity to construct multi-component, interactive nanoparticle systems that perform sophisticated new functions in vivo. This thesis takes a systems approach to engineering cancer nanotechnologies, where interactions between multiple nanoparticle populations are designed to generate emergent system properties for enhancing the sensing and targeting of cancer cells. In the first section of this thesis, direct nanoparticle interactions are engineered to produce emergent properties for cancer sensing. Three classes of magnetic particles are developed that respectively enable: MRI detection of single cancer-associated proteases, performance of logical AND/OR operations using two cancer-associated proteases, and reversible sensing of antagonistic kinase/phosphatase enzyme pairs.(cont.) In the second section of this thesis, indirect mechanisms of nanoparticle interaction-where nanoparticles communicate at a distance via intermediates-are engineered to amplify nanoparticle targeting to regions of tumor invasion in vivo. Two nanosystems are synthesized wherein intravenously administered nanoparticles that have successfully targeted tumors broadcast the tumor's location to other nanoparticles in circulation to recruit their amplified local accumulation. In mice, one of these systems intravenously delivers >40-fold higher drug doses to tumors than non-communicating controls, leading to durable repression of tumor growth and significantly improved host survival. Together, these systems highlight the potential for interactive nanoparticle systems to perform highly complex functions in vivo. In contrast to the current strategy of injecting large populations of nanoparticles that carry out identical, often competitive functions in vivo, this work promotes a paradigm of 'systems nanotechnology,' directed toward the construction of nanoparticle systems that produce emergent behaviors for enhancing in vivo diagnostics, regenerative medicines, and therapeutics.by Geoffrey von Maltzahn.Ph.D

Understanding Control of Metabolite Dynamics and Heterogeneity

Microbes live in complex and continually changing environments. Rapid shifts in nutrient availability are a common challenge for microbes, and cause changes in intracellular metabolite levels. Microbial response to dynamic environments requires coordination of multiple levels of cellular machinery including gene expression and metabolite concentrations. This coordination is achieved through metabolic control systems, which sense metabolite concentrations and direct cellular activity in response. Several reoccurring control architectures are found throughout diverse metabolic systems, which suggests underlying evolutionary advantages for using these control systems to coordinate metabolism. One common, yet understudied, control architecture is the positive feedback metabolite uptake loop, which features a metabolite responsive-transcription factor (MRTF) that activates genes necessary to uptake its cognate metabolite. Understanding the design principles behind these complex metabolic control systems is a fundamental issue across many biological sub-disciplines since metabolism is a central feature of cellular behavior.The goal of this dissertation is to elucidate how the architecture and parameters of a MRTF-based control system shape metabolite dynamics and heterogenous metabolic response to changing nutrient environments. This dissertation focuses on the Escherichia coli fatty acid degradation system, which employs the positive feedback uptake loop architecture. The function and performance of these control systems to three common metabolic tasks was evaluated. First, after a nutrient depletion, microbes must rapidly turn off metabolic pathways to conserve resources. Second, microbes must maintain sensing ability in the face of metabolic conditions which impact cellular growth rate. Finally, upon abrupt shifts between nutrients, microbes must shift metabolic resources to uptake the new nutrient or otherwise cease growth. This shifting process can be heterogenous, with a sub-population which maintains a non-growing state that confers tolerance to antimicrobial compounds. Taken together, this work provides deeper understanding of the design principles for the control of metabolite dynamics and heterogeneity for applications in metabolic engineering and synthetic biology