ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

Real time stream processing for Internet of things and sensing environments

Includes bibliographical references.2015 Fall.Improvements in miniaturization and networking capabilities of sensors have contributed to the proliferation of Internet of Things (IoT) and continuous sensing environments. Data streams generated in such settings must keep pace with generation rates and be processed in real time. Challenges in accomplishing this include: high data arrival rates, buffer overflows, context-switches during processing, and object creation overheads. We propose a holistic framework that addresses the CPU, memory, network, and kernel issues involved in stream processing. Our prototype, Neptune, builds on the Granules cloud runtime and leverages its support for scheduling packets and communications based on publish/subscribe, peer to peer, and point-to-point. The framework maximizes bandwidth utilization in the presence of small messages via the use of buffering and dynamic compactions of packets based on their entropy. Our use of thread-pools and batched processing reduces context switches and improves effective CPU utilizations. The framework alleviates memory pressure that can lead to swapping, page faults, and thrashing through efficient reuse of objects. To cope with buffer overflows we rely on flow control and throttling the preceding stages of a processing pipeline. Our correctness criteria included deadlock/livelock avoidance, and ordered and exactly-once processing. Our benchmarks demonstrate the suitability of the Granules/Neptune combination and we contrast our performance with Apache Storm, the dominant stream-processing framework developed by Twitter. At a single node, we are able to achieve a processing rate of ~2 million stream packets per-second. In a distributed cluster setup, we are able to achieve a processing rate of ~100 million stream packets per-second with a near-optimal bandwidth utilization

Image similarity in medical images

Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al

Reference models for network trace anonymization

Network security research can benefit greatly from testing environments that are capable of generating realistic, repeatable and configurable background traffic. In order to conduct network security experiments on systems such as Intrusion Detection Systems and Intrusion Prevention Systems, researchers require isolated testbeds capable of recreating actual network environments, complete with infrastructure and traffic details. Unfortunately, due to privacy and flexibility concerns, actual network traffic is rarely shared by organizations as sensitive information, such as IP addresses, device identity and behavioral information can be inferred from the traffic. Trace data anonymization is one solution to this problem. The research community has responded to this sanitization problem with anonymization tools that aim to remove sensitive information from network traces, and attacks on anonymized traces that aim to evaluate the efficacy of the anonymization schemes. However there is continued lack of a comprehensive model that distills all elements of the sanitization problem in to a functional reference model.;In this thesis we offer such a comprehensive functional reference model that identifies and binds together all the entities required to formulate the problem of network data anonymization. We build a new information flow model that illustrates the overly optimistic nature of inference attacks on anonymized traces. We also provide a probabilistic interpretation of the information model and develop a privacy metric for anonymized traces. Finally, we develop the architecture for a highly configurable, multi-layer network trace collection and sanitization tool. In addition to addressing privacy and flexibility concerns, our architecture allows for uniformity of anonymization and ease of data aggregation

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists