modern navigation guidance and control proce ing ching fang lin pdf

Modern Navigation Guidance And Control Proce Ing Ching Fang Lin Pdf

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Lin C.-f. Modern Navigation, Guidance, And Control Processing 1991

Toggle navigation. Search By Year All Increased production efficiency combined with a slowdown in Moore's law and the end of Dennard scaling have made hardware accelerators increasingly important. Accelerators have become available on many different systems from the cloud to embedded systems. This modern computing paradigm makes specialized hardware available at scale in a way it never has before. While accelerators have shown great efficiency in terms of power consumption and performance, matching software functions with the best available hardware remains problematic without manual selection.

Since there is some software representation of each accelerator's function, selection can be automated via code analysis. Static similarity analysis has traditionally been based on solving satisfiable modulo theorems SMT , but continuous logic networks CLNs have provided a faster and more efficient alternative to traditional SMT-solving by replacing boolean functions with smooth estimations. These smooth estimates create the opportunity to leverage gradient descent to learn the solution. We present AccFinder, the first CLN-based code similarity solution and evaluate its effectiveness on a realistically complex accelerator benchmark.

Modern software engineering practices rely on program comprehension as the most basic underlying component for improving developer productivity and software reliability. Software developers are often tasked to work with unfamiliar code in order to remove security vulnerabilities, port and refactor legacy code, and enhance software with new features desired by users.

Automatic identification of behavioral clones, or behaviorally-similar code, is one program comprehension technique that can provide developers with assistance. The idea is to identify other code that "does the same thing" and that may be more intuitive; better documented; or familiar to the developer, to help them understand the code at hand. Unlike the detection of syntactic or structural code clones, behavioral clone detection requires executing workloads or test cases to find code that executes similarly on the same inputs.

However, a key problem in behavioral clone detection that has not received adequate attention is the "preponderance of the evidence" problem, which advocates for more convincing evidence from nontrivial test case executions to gain confidence in the behavioral similarities. In other words, similar outputs for some inputs matter more than for others.

We present a novel system, SABER, to address the "preponderance of the evidence" problem, for which we adapt the legal metaphor of "more likely to be true than not true" burden of proof. We develop a novel test case generation methodology with three primary dynamic analysis techniques for identifying important behavioral clones. Further, we investigate filtering and weighting schemes to guide developers toward the most convincing behavioral similarities germane to specific software engineering tasks, such as code review, debugging, and introducing new features.

Then the developers need to test whether their candidate patch indeed fixes the bug, without breaking other functionality, while racing to deploy before cyberattackers pounce on exposed user installations. This can be challenging when the bug discovery was due to factors that arose, perhaps transiently, in a specific user environment.

If recording execution traces when the bad behavior occurred, record-replay technology faithfully replays the execution, in the developer environment, as if the program were executing in that user environment under the same conditions as the bug manifested.

This includes intermediate program states dependent on system calls, memory layout, etc. So the bug is reproduced, and many modern record-replay tools also integrate bug reproduction with interactive debuggers to help locate the root cause, but how do developers check whether their patch indeed eliminates the bug under those same conditions?

State-of-the-art record-replay does not support replaying candidate patches that modify the program in ways that diverge program state from the original recording, but successful repairs necessarily diverge so the bug no longer manifests. This work builds on recordreplay, and binary rewriting, to automatically generate and run tests for candidate patches. Unlike conventional ad hoc testing, each test is reproducible and can be applied to as many prospective patches as needed until developers are satisfied.

The proposed approach also enables users to make new recordings of her own workloads with the original version of the program, and automatically generate and run the corresponding ad hoc tests on the patched version, to validate that the patch does not introduce new problems before adopting.

We ultimately produced such a compiler, relying on the Glasgow Haskell Compiler GHC as a front-end and writing our own back-end that performed a series of lowering transformations to restructure such constructs as recursion, polymorphism, and frst-order functions, into a form suitable for hardware, then transform the now-restricted functional IR into a datafow representation that is then finally transformed into synthesizable SystemVerilog.

Many HLS systems produce efficient hardware designs for regular algorithms i. HLS tools typically provide imperative, side-effectful languages to the designer, which makes it difficult to correctly specify and optimize complex, memory-bound applications. In this dissertation, I present an alternative HLS methodology that leverages properties of functional languages to synthesize hardware for irregular algorithms.

The main contribution is an optimizing compiler that translates pure functional programs into modular, parallel dataflow networks in hardware. I give an overview of this compiler, explain how its source and target together enable parallelism in the face of irregularity, and present two specific optimizations that further exploit this parallelism.

Taken together, this dissertation verifies my thesis that pure functional programs exhibiting irregular memory access patterns can be compiled into specialized hardware and optimized for parallelism. This work extends the scope of modern HLS toolchains. By relying on properties of pure functional languages, our compiler can synthesize hardware from programs containing constructs that commercial HLS tools prohibit, e.

Hardware designers may thus use our compiler in conjunction with existing HLS systems to accelerate a wider class of algorithms than before. This master thesis opens with a description of several text summarization methods based on machine learning approaches inspired by reinforcement learning. While in many cases Maximum Likelihood Estimation MLE approaches work well for text summarization, they tend to suffer from poor generalization. We show that techniques which expose the model to more opportunities to learn from data tend to generalize better and generate summaries with less lead bias.

In our experiments we show that out of the box these new models do not perform significantly better than MLE when evaluated using Rouge, however do possess interesting properties which may be used to assemble more sophisticated and better performing summarization systems. The main theme of the thesis is getting machine learning models to generalize better using ideas from reinforcement learning.

We develop a new labeling scheme inspired by Reward Augmented Maximum Likelihood RAML methods developed originally for the machine translation task, and discuss how difficult it is to develop models which sample from their own distribution while estimating the gradient e.

We show that RAML can be seen as a compromise between direct optimization of the model towards optimal expected reward using Monte Carlo methods which may fail to converge, and standard MLE methods which fail to explore the entire space of summaries, overfit during training by capturing prominent position features and thus perform poorly on unseen data.

To that end we describe and show results of domain transfer experiments, where we train the model on one dataset and evaluate on another, and position distribution experiments, in which we show how the distribution of positions of our models differ from the distribution in MLE. We also show that our models work better on documents which are less lead biased, while standard MLE models get significantly worse performance on those documents in particular.

Another topic covered in the thesis is Query Focused text summarization, where a search query is used to produce a summary with the query in mind. The summary needs to be relevant to the query, rather than solely contain important information from the document. We use ii the recently published Squad dataset and adapt it for the Query Focused summarization task.

We also train deep learning Query Focused models for summarization and discuss problems associated with that approach. Finally we describe a method to reuse an already trained QA model for the Query Focused text summarization by introducing a reduction of the QA task into the Query Focused text summarization.

Email privacy is of crucial importance. Existing email encryption approaches are comprehensive but seldom used due to their complexity and inconvenience. We take a new approach to simplify email encryption and improve its usability by implementing receiver-controlled encryption: newly received messages are transparently downloaded and encrypted to a locally-generated key; the original message is then replaced. To avoid the problem of users having to move a single private key between devices, we implement per-device key pairs: only public keys need be synchronized to a single device.

Compromising an email account or email server only provides access to encrypted emails. Mail, has acceptable overhead, and that users consider it intuitive and easy to use. On the one hand, some people claim it can be accomplished safely; others dispute that.

In an attempt to make progress, a National Academies study committee propounded a framework to use when analyzing proposed solutions. Robot Learning in Simulation for Grasping and Manipulation. Teaching a robot to acquire complex motor skills in complicated environments is one of the most ambitious problems facing roboticists today.

Grasp planning is a subset of this problem which can be solved through complex geometric and physical analysis or computationally expensive data driven analysis.

As grasping problems become more difficult, building analytical models becomes challenging. Consequently, we aim to learn a grasping policy through a simulation-based data driven approach. We present POS, a concurrency testing approach that directly samples the partial orders of a concurrent program.

POS uses a novel priority-based scheduling algorithm that naturally considers partial order information dynamically, and guarantees that each partial order will be explored with significant probability. This probabilistic guarantee of error detection is exponentially better than state-of-the-art sampling approaches.

Besides theoretical guarantees, POS is extremely simple and lightweight to implement. Stretchcam is a thin camera with a lens capable of zooming with small actuations. In our design, an elastic lens array is placed on top of a sparse, rigid array of pixels. This lens array is then stretched using a small mechanical motion in order to change the field of view of the system.

We present in this paper the characterization of such a system and simulations which demonstrate the capabilities of stretchcam. We follow this with the presentation of images captured from a prototype device of the proposed design. Our prototype system is able to achieve 1. As Internet of Things IoT devices gain more popularity, device management gradually becomes a major issue to IoT device users.

To manage an IoT device, the user first needs to join it to an existing network. Then, the IoT device has to be authenticated by the user. The authentication process often requires a two-way communication between the new device and a trusted entity, which is typically a hand- held device owned by the user.

To ease and standardize this process, we present the Device Enrollment Protocol DEP as a solution to the enrollment problem described above.

The application allows the user to authenticate IoT devices and join them to an existing protected network. However, RNNs are still often used as a black box with limited understanding of the hidden representation that they learn.

Existing approaches such as visualization are limited by the manual effort to examine the visualizations and require considerable expertise, while neural attention models change, rather than interpret, the model.

We propose a technique to search for neurons based on existing interpretable models, features, or programs. State machine replication SMR leverages distributed consensus protocols such as PAXOS to keep multiple replicas of a program consistent in face of replica failures or network partitions.

This fault tolerance is enticing on implementing a principled SMR system that replicates general programs, especially server programs that demand high availability. Unfortunately, SMR assumes deterministic execution, but most server programs are multithreaded and thus non-deterministic.

Moreover, existing SMR systems provide narrow state machine interfaces to suit specific programs, and it can be quite strenuous and error-prone to orchestrate a general program into these interfaces This paper presents CRANE, an SMR system that trans- parently replicates general server programs. It leverages deterministic multithreading specifically, our prior system PARROT to make multithreaded replicas deterministic.

It uses a new technique we call time bubbling to efficiently tackle a difficult challenge of non-deterministic network input timing. Evaluation on five widely used server programs e. Deobfuscating Android Applications through Deep Learning. Android applications are nearly always obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations.

Obfuscators might hide the true intent of code by renaming variables, modifying the control flow of methods, or inserting additional code. Prior approaches toward automated deobfuscation of Android applications have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relation- ships between identifiers e. Both approaches can be easily defeated by a motivated obfuscator.

MACNETO makes few assumptions about the kinds of modifications that an obfuscator might perform, and we show that it has high precision when applied to two different state-of-the-art obfuscators: ProGuard and Allatori.

Technical Reports

WTCS best papers Volume 2 is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of Education, Psychology and Computer Science to disseminate their latest research results and exchange views on the future research directions of these fields. Each paper has been peer-reviewed by at least 2 program committee members and selected by the volume editor Prof. On behalf of the WTCS , we would like to express our sincere appreciation to all of authors and referees for their efforts reviewing the papers. Hoping you can find lots of profound research ideas and results on the related fields of Education, Psychology and Computer Science. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available.

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This book places major emphasis on the practical applications of advanced Navigation Guidance and Control NGC Systems, treating the subject more from an engineering than mathematical perspective. Convert currency. Add to Basket. Book Description Pearson Education, Condition: New.

Multi-vehicle swarms have many applications that include searching, target tracking, and mapping unfamiliar environments. Much of the research on quadrotor swarms has focused on its use for military, communication, industrial and farming applications. Wing-wake interaction phenomena are often exploited in the natural world, for example in fish schooling and bird flocking. This is also an interesting area of research with engineering applications such as blade-vortex interactions in helicopters and wind

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Du kanske gillar. Inbunden Engelska, Spara som favorit. Skickas inom vardagar. This work offers detailed information on the modelling, design, analysis, simulation and evaluation MDASE of advanced navigation, guidance and control NGC systems. The book emphasizes the practical applications of advanced systems and discusses approaches to designing. The book also includes information about the status of major aerospace programmes, as well as trends in aerospace technology; covers a range of applications from military and commercial aircraft to spacecraft and missile and weapon systems; offers practical tools and approaches for solving real-world aerospace engineering problems; and provides a detailed evaluation of modern analysis techniques and advanced control system design.

The oceanic circulation south of Africa is characterised by a complex dynamics with a strong variability due to the presence of the Agulhas current and numerous eddies. This area of interest is also the locati Citation: Geoscience Letters 8

Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available. Front Matter Pages i-xiv. Pages

Modern navigation, guidance, and control processing. Syndicate of the University of Cambridge, PDF Three dimensional mid-course guidance state equations. Siouris, year

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    Modern Navigation,. Guidance, and. Control Processing. Ching-Fang Lin. American GNC Corporation. 3. Prentice Hall, Englewood Cliffs, New Jersey

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