Γ (Gamma): cloud-based analog circuit design system

Includes bibliographical references.2016 Summer.With ever increasing demand for lower power consumption, lower cost, and higher performance, designing analog circuits to meet design specifications has become an increasing challenging task, On one hand, analog circuit designers must have intimate knowledge about the underlining silicon process technology's capability to achieve the desired specifications. On the other hand, they must understand the impact of tweaking circuits to satisfy a given specification on all circuit performance parameters. Analog designers have traditionally learned to tackle design problems with numerous circuit simulations using accurate circuit simulators such as SPICE, and have increasingly relied on trial-and-error approaches to reach a converging point. However, the increased complexity with each generation of silicon technology and high dimensionality of searching for solutions, even for some simple analog circuits, have made trial-and-error approaches extremely inefficient, causing long design cycles and often missed market opportunities. Novel rapid and accurate circuit evaluation methods that are tightly integrated with circuit search and optimization methods are needed to aid design productivity. Furthermore, the current design environment with fully distributed licensing and supporting structures is cumbersome at best to allow efficient and up-to-date support for design engineers. With increasing support and licensing costs, fewer and fewer design centers can afford it. Cloud-based software as a service (SaaS) model provides new opportunities for CAD applications. It enables immediate software delivery and update to customers at very low cost. SaaS tools benefit from fast feedback and sharing channels between users and developers and run on hardware resources tailored and provided for them by software vendors. However, web-based tools must perform in a very short turn-around schedule and be always responsive. A new class of analog design tools is presented in this dissertation. The tools provide effective design aid to analog circuit designers with a dash-board control of many important circuit parameters. Fast and accurate circuit evaluations are achieved using a novel lookup-table transistor models (LUT) with novel built-in features tightly integrated with the search engine to achieve desired speed and accuracy. This enables circuit evaluation time several orders faster than SPICE simulations. The proposed architecture for analog design attempts to break the traditional analog design flow using SPICE based trial-and-error methods by providing designers with useful information about the effects of prior design decisions they have made and potential next steps they can take to meet specifications. Benefiting from the advantages offered by web-hosted architectures, the proposed architecture incorporates SaaS as its operating model. The application of the proposed architecture is illustrated by an analog circuit sizer and optimizer. The Γ (Gamma) sizer and optimizer show how web-based design-decision supporting tool can help analog circuit designers to reduce design time and achieve high quality circuit

Temporal multimodal video and lifelog retrieval

The past decades have seen exponential growth of both consumption and production of data, with multimedia such as images and videos contributing significantly to said growth. The widespread proliferation of smartphones has provided everyday users with the ability to consume and produce such content easily. As the complexity and diversity of multimedia data has grown, so has the need for more complex retrieval models which address the information needs of users. Finding relevant multimedia content is central in many scenarios, from internet search engines and medical retrieval to querying one's personal multimedia archive, also called lifelog. Traditional retrieval models have often focused on queries targeting small units of retrieval, yet users usually remember temporal context and expect results to include this. However, there is little research into enabling these information needs in interactive multimedia retrieval. In this thesis, we aim to close this research gap by making several contributions to multimedia retrieval with a focus on two scenarios, namely video and lifelog retrieval. We provide a retrieval model for complex information needs with temporal components, including a data model for multimedia retrieval, a query model for complex information needs, and a modular and adaptable query execution model which includes novel algorithms for result fusion. The concepts and models are implemented in vitrivr, an open-source multimodal multimedia retrieval system, which covers all aspects from extraction to query formulation and browsing. vitrivr has proven its usefulness in evaluation campaigns and is now used in two large-scale interdisciplinary research projects. We show the feasibility and effectiveness of our contributions in two ways: firstly, through results from user-centric evaluations which pit different user-system combinations against one another. Secondly, we perform a system-centric evaluation by creating a new dataset for temporal information needs in video and lifelog retrieval with which we quantitatively evaluate our models. The results show significant benefits for systems that enable users to specify more complex information needs with temporal components. Participation in interactive retrieval evaluation campaigns over multiple years provides insight into possible future developments and challenges of such campaigns

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Region-based saliency estimation for 3D shape analysis and understanding

The detection of salient regions is an important pre-processing step for many 3D shape analysis and understanding tasks. This paper proposes a novel method for saliency detection in 3D free form shapes. Firstly, we smooth the surface normals by a bilateral filter. Such a method is capable of smoothing the surfaces and retaining the local details. Secondly, a novel method is proposed for the estimation of the saliency value of each vertex. To this end, two new features are defined: Retinex-based Importance Feature (RIF) and Relative Normal Distance (RND). They are based on the human visual perception characteristics and surface geometry respectively. Since the vertex based method cannot guarantee that the detected salient regions are semantically continuous and complete, we propose to refine such values based on surface patches. The detected saliency is finally used to guide the existing techniques for mesh simplification, interest point detection, and overlapping point cloud registration. The comparative studies based on real data from three publicly accessible databases show that the proposed method usually outperforms five selected state of the art ones both qualitatively and quantitatively for saliency detection and 3D shape analysis and understanding