Digital System Design By Anand Kumar 34.epubDigital System Design By Anand Kumar 34.epub >>> though time-interleaved analog-to-digital converters (ADCs) help to achieve higher bandwidth with simpler individual ADCs, gain, offset, and time-skew mismatch between the channels degrade the achievable resolution. Of particular interest is the time-skew error between channels which results in nonuniform samples and thereby introducing distortion tones at the output of the time-interleaved ADC. Time-varying digital reconstructors can be used to correct the time-skew errors between the channels in a time-interleaved ADC. However, the complexity of such reconstructors increases as their bandwidth approaches the Nyquist band. In addition to this, the reconstructor needs to be redesigned online every time the time-skew error varies. Design methods that result in minimum reconstructor order require expensive online redesign while those methods that simplify online redesign result in higher reconstructor complexity. This paper proposes a technique that can be used to simplify the online redesign and achieve a low complexity reconstructor at the same time.To summarize, this paper presents three contributions: (1) An analysis of the timing errors in the individual channels of a time-interleaved analog-to-digital converter (TI-ADC); (2) Use of the time-skew errors to directly estimate the reconstruction filters that convert nonuniform samples to uniform samples in the time-interleaved analog-to-digital converter (TI-ADC); and (3) A design methodology for frequency synthesis using digital oscillators.The thesis work is conducted in the division of computer engineering at thedepartment of electrical engineering in Linkping University. During the thesiswork, a configurable Direct Memory Access (DMA) controller was designed andimplemented. The DMA controller runs at 200MHz under 65nm digital CMOS technology. The estimated gate count is 26595. 65a90a948d -gavi/pdf-ready-reckoner-english-grammar-bookl -attract-women-through-honesty-ebook-rargolkes -zhu/tai-lecture-maker-20-full-crackl -sim-weather-radar-crack -structural-analysis-by-dayaratnam-pdf-58
Digital System Design By Anand Kumar 34.epub
At present time, the field of MS has a handful of software systems employing their proprietary or open formats for data storage [6]-[8]. Although many of them are carefully designed to achieve maximum computational performance in simulation, they significantly fall short on storage and handling of the large scale data output. The MS by their nature generate a large amount of data in a streaming fashion - a system could consist of millions of atoms and one single simulation can easily run for tens of thousands of time steps. Figure 1 shows two (small) examples of such simulations. One salient problem of existing systems is the lack of efficient data retrieval and analytical query processing mechanisms.
In this paper, we present our recent research efforts in advancing big data analytic and management systems for scientific simulation domains, which usually generate large datasets with temporal and spatial correlations for analysis. Our research mainly emphasizes on the design of the data management system in supporting intensive data access, query processing, and optimization mechanisms for MS data. The main objective of our study is to produce high performance techniques for the MS community to accelerate the discovery process in biological/medical research. In particular, we introduce the design and development of a Database-Centric Molecular Simulation (DCMS) framework that allows scientists to efficiently retrieve, query, analyze, and share MS data.
In current MS software [9]-[11], simulation data is typically stored in data files, which are further organized into various levels of directories. Data access is enabled by encoding the descriptions of the content in files into the names of files and directories, or storing more detailed descriptions about the file content in separate metadata files. Under the traditional file-based scheme, data/information sharing among MS community involves shipping the raw data packed in files along with the required format information and analysis tools. Due to the sheer volume of MS data, such sharing is extremely difficult, if possible at all. Two MS data analysis projects, BioSimGrid [12] and SimDB [13], store data and perform analysis at the same computer system and allow users remotely send in queries and get back results. This approach is based on the premises that: (1) analysis of MS data involves projection and/or reduction of data to smaller volume; (2) users need to exchange the reduced representation of data, rather than the whole raw data. In a similar project [14], databases are used to store digital movies generated from visualization of MS datasets.
The architecture of the DCMS framework is illustrated in Figure 2, where the solid lines represent command flow and dotted lines represent data flow. At the core of DCMS is an integrated database system, including simulation parameters/states, simulation output data, and metadata for efficient data retrieval. An important design goal of DCMS is to allow scientists to easily transfer, interrogate, visualize, and hypothesize from integrated information obtained from a user-friendly interface as opposed to dealing with raw simulation data. To that end, DCMS provides various user interfaces for data input and query processing.
Data indexing is the most important database technique to improve the efficiency of data retrieval. In DCMS, algorithms for processing primary queries will be exclusively index-based to reduce data access time. To support a rich set of queries, multiple indexes are necessary. However, it is infeasible to maintain excessive number of indexes due to the extremely high storage cost for MS data. Note that MS databases are most likely read-only therefore the maintenance (update) cost of indexes can be ignored. We have designed and tested several novel indexes to handle the various queries in DCMS but finally adopted the following indexes in our implemented system: (1) the B +-tree and a bitmap-based index which are the default indexes provided by PostgreSQL - they provide a certain level of support for some of the MS queries; and (2) a new index named Time-Parameterized Spatial (TPS) tree to provide further performance boost. We accordingly modify the query optimizer of the DBMS to generate query execution plans that take advantage of the aforementioned indexes.
The multi-body functions are all holistic functions[22] therefore cannot be computed in the same way as one-body functions. Current MS software adopts simple yet naïve algorithms to compute the multi-body functions [9]. For example, the SDH is computed by retrieving the locations of all atoms, computing all pairwise distances, and grouping them into the histogram - a O(n2) approach. For a large simulation system where n is big, this algorithm could be intolerably slow. In DCMS, we invested much efforts into algorithmic design related to such queries.
To make the view-based solution work, the main challenge is the design of query optimization algorithms that take views into account. Query optimizers of existing DBMSs are not established for our purpose: they focus on views that are built over various base tables [26],[27] in the database, often as a result of join operations. On the other hand, a view in our system maps a multidimensional data region to a complex aggregate. Such differences require development of novel techniques to address the following research problems.
Preserving data privacy is critical to organizations and research groups that employ external or third party analysts to understand the data, find out interesting patterns, or make new discoveries, but there are concerns on sharing the raw data. Sometimes, scientists have the same concerns over MS data. Privacy can be provided by database management systems through access control mechanisms (ACMs). ACMs limit the data access to users with special privileges, and ACM policies are directly supported by the SQL language. ACMs either restrict or completely grant access to the data. However, third party analysts may not be able to perform the best work without accessing other parts of the data that may depend on the private information. Attempts to provide flexibility ended with differential privacy mechanisms, which also face limitations due to requirements that are difficult to quantify, for both data providers and analysts. Therefore, the problem of preserving privacy from the range of ACMs to differential privacy is inadequately addressed. We performed some fundamental research on this topic within the context of DCMS. In particular, we designed an architecture named security automata model (SAM) to enforce privacy-preserving policies. SAM allows only aggregate queries, as long as privacy is preserved. Once it detects possible risk, differential privacy policy is enforced. It works on basic aggregate queries, liberating data owners from controlling special programs written by the analysts. Sequence of queries from all users in different sessions are monitored to detect the privacy breaches. We integrated this design into DCMS to address the question of how privacy can be defined, enforced, and integrated with existing ACMs using SAM.
Traditionally, database systems are mainly designed for commercial data and applications. In recent years, the scientific community has also adopted database technology in processing scientific data. However, scientific data are different from commercial data in that: (1) the volume of scientific data can be orders of magnitude larger; (2) data are often multidimensional and continuous; and (3) queries against scientific data are more complex. The above differences bring significant challenges to system design in scientific databases.
AK carried out the design, implementation, and experiments related to query processing, data compression, and data security. VG participated in experiments related to query processing and cache-based query optimization. MB designed and implemented index structures and the DCMS web interface. JF helped in the development of data loading and transformation module of DCMS. YT was in charge of the overall design of the DCMS system and algorithms related to analytical queries. XZ, SP, and YX participated in the system and web interface design. XZ also carried out the design and tuning of the data compression framework. YT drafted most parts of this manuscript while AK and VG also contributed to writing. All authors read and approved the final manuscript. 2ff7e9595c
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