检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

33 篇 会议
1 篇 期刊文献

馆藏范围

34 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

27 篇 工学
- 23 篇 计算机科学与技术...
- 17 篇 软件工程
- 9 篇 信息与通信工程
- 5 篇 电气工程
- 4 篇 电子科学与技术（可...
- 3 篇 动力工程及工程热...
- 2 篇 控制科学与工程
- 1 篇 力学（可授工学、理...
- 1 篇 机械工程
- 1 篇 光学工程
- 1 篇 仪器科学与技术
- 1 篇 建筑学
- 1 篇 化学工程与技术
- 1 篇 核科学与技术
- 1 篇 农业工程
- 1 篇 环境科学与工程（可...
- 1 篇 生物工程
- 1 篇 网络空间安全
9 篇 理学
- 4 篇 数学
- 3 篇 化学
- 2 篇 生物学
- 1 篇 物理学
5 篇 管理学
- 3 篇 管理科学与工程(可...
- 3 篇 图书情报与档案管...
- 2 篇 工商管理
1 篇 经济学
- 1 篇 应用经济学
1 篇 医学
- 1 篇 特种医学

主题

5 篇 signal processin...
4 篇 signal processin...
3 篇 estimation
2 篇 signal to noise ...
2 篇 vectors
2 篇 computational mo...
2 篇 costs
2 篇 radar
2 篇 microcomputers
2 篇 machine learning...
2 篇 stochastic proce...
2 篇 radio frequency
1 篇 sensor systems
1 篇 low noise amplif...
1 篇 catalytic sensor
1 篇 probability dens...
1 篇 smart homes
1 篇 mechanical fault...
1 篇 self-noise cance...
1 篇 eigenvalues and ...

机构

2 篇 teaching center ...
2 篇 school of inform...
2 篇 tianjin key labo...
2 篇 department of el...
1 篇 nanyang technolo...
1 篇 institute of pho...
1 篇 department of el...
1 篇 tech univ cluj n...
1 篇 niroo res inst h...
1 篇 xìan eurasia uni...
1 篇 univ liverpool d...
1 篇 school of electr...
1 篇 iiit delhi infos...
1 篇 univ laval dept ...
1 篇 pacific northwes...
1 篇 school of inform...
1 篇 surveillance sys...
1 篇 univ liverpool d...
1 篇 the sixty-third ...
1 篇 univ loughboroug...

作者

2 篇 sun guiling
2 篇 ralph jason f.
2 篇 wang hai
2 篇 weiss stephan
2 篇 jiang xiaomei
2 篇 zhang ying
2 篇 wang zhihong
1 篇 coda elizabeth
1 篇 ni bingqian
1 篇 cantin audrey
1 篇 davies mike
1 篇 vann michael
1 篇 chen wen-hua
1 篇 junquan deng
1 篇 spirjakin denis
1 篇 ferhi oussama
1 篇 bhole dheeral
1 篇 marcus prochaska
1 篇 kumar pawan
1 篇 tian xiaoqing

语言

34 篇 英文

检索条件"任意字段=11th Sensor Signal Processing for Defence Conference, SSPD 2022"

共 34 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Optimal Bernoulli point estimation with applications 11

Optimal Bernoulli point estimation with applications

引用

11th sensor signal processing for defence conference (sspd)

作者： Narykov, Alexey Uney, Murat Ralph, Jason F. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3BX Merseyside England

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

this paper develops optimal procedures for point estimation with Bernoulli filters. these filters are of interest to radar and sonar surveillance because they are designed for stochastic targets that can enter and exit the surveillance region at random instances. Because of this property they are not served by the minimum mean square estimator, which is the most widely used approach to optimal point estimation. Instead of the squared error loss, this paper proposes an application-oriented loss function that is compatible with Bernoulli filters, and it develops two significant practical estimators: the minimum probability of error estimate (which is based on the rule of ideal observer), and the minimum mean operational loss estimate (which models a simple defence scenario).

关键词： Surveillance Estimation Stochastic processes Sonar Radar signal processing Observers

来源：评论

学校读者我要写书评

暂无评论

Fast Trajectory Forecasting With Automatic Identification System Broadcasts 11

Fast Trajectory Forecasting With Automatic Identification Sy...

引用

11th sensor signal processing for defence conference (sspd)

作者： Wang, Yicheng Uncy, Murat Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

this work proposes a fast trajectory forecasting algorithm to use with automatic identification system (AIS) broadcasts of vessels. the algorithm involves fast sub-optimal model parameter estimation from AIS messages and the computation of Gaussian location predictions for a series of future timestamps. the underlying trajectory model is a stochastic process that uses six parameters to generate near-constant velocity trajectories. these parameters include the desired cruise heading and speed of the vessel and velocity standard deviations along the heading direction and its perpendicular complement. We demonstrate the performance of our approach using a real AIS data set.

关键词： Computational modeling signal processing algorithms Stochastic processes signal processing Predictive models Prediction algorithms Trajectory

来源：评论

学校读者我要写书评

暂无评论

Optimizing sonobuoy placement using multiobjective machine learning 11

Optimizing sonobuoy placement using multiobjective machine l...

引用

11th sensor signal processing for defence conference (sspd)

作者： Taylor, Christopher M. Maskell, Simon Ralph, Jason F. Univ Liverpool Dept Elect Engn & Elect Liverpool England

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

We present a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. the problem is modelled as a biobjective one, where the aim is to both minimize uncertainty over target localization and minimize sensor placement time. We develop a two-phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto-nondominated solutions to a static problem, and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. Initial results show that our approach generates significant improvements over standard grid patterns.

关键词： Location awareness sensor placement Uncertainty Machine learning algorithms Biological system modeling signal processing algorithms Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Space-Time Covariance Estimation Based on a System Identification Approach 11

Enhanced Space-Time Covariance Estimation Based on a System ...

引用

11th sensor signal processing for defence conference (sspd)

作者： Khattak, Faizan A. Proudler, Ian K. Weiss, Stephan Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

the error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

关键词： Estimation signal processing Eigenvalues and eigenfunctions Data models System identification signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Learning Low-Rank Models From Compressive Measurements for Efficient Projection Design 11

Learning Low-Rank Models From Compressive Measurements for E...

引用

11th sensor signal processing for defence conference (sspd)

作者： Coutts, Fraser K. thompson, John Mulgrew, Bernard Univ Edinburgh Inst Digital Commun Edinburgh EH9 3FG Scotland

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

Recent research has uncovered information-theoretic means to design projection matrices in scenarios where one information source is compressively sampled in the presence of a secondary source. Furthermore, if both sources can be approximated by Gaussian mixture (GM) models, it has been shown that it is possible to learn the characteristics of the secondary source from compressive measurements only. In this work, we investigate techniques that exploit low-rank GM approximations to the true distributions to reduce computational complexity and memory requirements during source learning and projection design. Two novel alternative projection design strategies are also introduced. these are tested against an existing strategy to determine which approach is superior for low size, weight, and power (SWAP) applications. Experimental results validate the benefits of the proposed low-rank strategies and reveal that all projection design algorithms offer similar levels of performance.

关键词： Computational modeling Memory management signal processing algorithms signal processing Computational complexity

来源：评论

学校读者我要写书评

暂无评论

A Polynomial Subspace Projection Approach for the Detection of Weak Voice Activity 11

A Polynomial Subspace Projection Approach for the Detection ...

引用

11th sensor signal processing for defence conference (sspd)

作者： Neo, Vincent W. Weiss, Stephan Naylor, Patrick A. Imperial Coll London Dept Elect & Elect Engn London England Univ Strathclyde Dept Elect & Elect Engn Glasgow Scotland

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

A voice activity detection (VAD) algorithm identifies whether or not time frames contain speech. It is essential for many military and commercial speech processing applications, including speech enhancement, speech coding, speaker identification, and automatic speech recognition. In this work, we adopt earlier work on detecting weak transient signals and propose a polynomial subspace projection pre-processor to improve an existing VAD algorithm. the proposed multi-channel pre-processor projects the microphone signals onto a lower dimensional subspace which attempts to remove the interferer components and thus eases the detection of the speech target. Compared to applying the same VAD to the microphone signal, the proposed approach almost always improves the F1 and balanced accuracy scores even in adverse environments, e.g. -30 dB SIR, which may be typical of operations involving noisy machinery and signal jamming scenarios.

关键词： Voice activity detection polynomial matrix eigenvalue decomposition multi-channel signal processing

来源：评论

学校读者我要写书评

暂无评论

Compressive Self-Noise Cancellation in Underwater Acoustics 11

Compressive Self-Noise Cancellation in Underwater Acoustics

引用

11th sensor signal processing for defence conference (sspd)

作者： Kumar, Pawan Nathwani, Karan Abrol, Vinayak Kumar, Suresh IIT Jammu Dept Elect Engn Jammu India IIIT Delhi Infosys Ctr AI Delhi India NPOL Kochi Naval Res Board Kochi Kerala India

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

the purpose of sonar is to detect the stealthy target in shallow water. the main barrier to locating the target is sonar's self-noise. Existing subspace-based noise suppression methods typically employ eigenanalysis-based methods involving high computational complexity. Recent approaches based on compressed sensing (CS) or sparse representations (SR) are computationally efficient. It is not straightforward to extend existing CS/SR-based methods for self-noise cancellation as, first, the energy of interference is much higher than the target, and second, it also exhibits similar sparsity properties. this work presents a novel method to combine the advantages of a subspace-based noise cancellation approach with low complexity of working with fewer CS measurements. Both target recovery and self-noise cancellation are done in the compressive domain only. Experimental results demonstrate the robustness of the proposed approach for both narrowband and broadband targets at very low signal-to-interference-noise (SINR).

关键词： Self-noise cancellation compressed sensing underwater acoustics sensor array

来源：评论

学校读者我要写书评

暂无评论

2022 sensor signal processing for defence conference, sspd 2022 - Proceedings

2022 Sensor Signal Processing for Defence Conference, SSPD 2...

引用

11th sensor signal processing for defence conference, sspd 2022

ISBN: (纸本)9781665483483

the proceedings contain 21 papers. the topics discussed include: automatic approximation for 1-dimensional feedback-loop computations: a PID benchmark;efficient joint surface detection and depth estimation of single-photon lidar data using assumed density filtering;an extension to the Frenet-Serret and bishop invariant extended Kalman filters for tracking accelerating targets;state estimation of the spread of COVID-19 in Saudi Arabia using extended Kalman filter;optimal Bernoulli point estimation with applications;high resolution DOA estimation for contiguous target with large power difference;compressive self-noise cancellation in underwater acoustics;unsupervised expectation propagation method for large-scale sparse linear inverse problems;and movement classification and segmentation using event-based sensing and spiking neural networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Movement Classification and Segmentation Using Event-Based Sensing and Spiking Neural Networks 11

Movement Classification and Segmentation Using Event-Based S...

引用

11th sensor signal processing for defence conference (sspd)

作者： Kirkland, Paul Di Caterina, Gaetano Univ Strathclyde Neuromorph Sensor Signal Proc Lab Glasgow Scotland

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

the development of Spiking Neural Networks (SNN) and the discipline of Neuromorphic Engineering has resulted in a paradigm shift in how Machine Learning (ML) and Computer Vision (CV) problems are approached. At the heart of this shift is the adoption of event-based sensing and processing methods. the production of sparse and asynchronous events that are dynamically connected to the scene is possible with an event-based vision sensor, allowing for the acquisition of not just spatial data but also high-fidelity temporal data. In this work, we describe a novel method for performing instance segmentation of objects, only using their spatio-temporal movement patterns, by utilising the weights of an unsupervised Spiking Convolutional Neural Network that was originally trained for object recognition and extending it to instance segmentation. this takes advantage of the network's spatial and temporal characteristics encoded within its internal feature representation, to offer this additional discriminative ability. We demonstrate this through a track path identification problem, where 6 identical blobs complete complex movement patterns within the same area at the same time. the network is able to successfully identify all 6 individual movements and segment the movement patterns belonging to each. the work then also explains how these methods map into the more complex Track before Detect problem. A complex track initiation problem, where detection can only be completed after an integration period, due to the low signal, high noise environment. these problem characteristics seem to complement the properties of event-based sensing and processing and initial test results are shown.

关键词： Neuromorphic Engineering Neuromorphic Algorithms SNN STDP Computer Vision Unsupervised Learning Instance Segmentation Event-Based Vision

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Spectral Filling in Radio Frequency Applications 11

Deep Learning for Spectral Filling in Radio Frequency Applic...

引用

11th sensor signal processing for defence conference (sspd)

作者： Setzler, Matthew Coda, Elizabeth Rounds, Jeremiah Vann, Michael Girard, Michael Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (数字)9781665483483

ISBN: (纸本)9781665483483

Due to the Internet of things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. this poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with already-deployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, "around" the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.

关键词： deep learning signal generation communications machine learning radio frequency

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共4页 << < 1 2 3 4 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：