蔬菜水果脆片加工工艺分析

对蔬果脆片的特点、真空低温油炸工艺的原理以及工艺流程做了阐述。着重对关键工艺做了分析，对蔬果脆片质量的提高、设备的改进提出了建议。     

一种基于神经网络的跳频信号频率预测方法

对跳频通信进行有效干扰的关键在于对跳频信号的频率进行较准的预测,本文就此提出了一种基于神经网络的对跳频信号频率进行预测的新方法,该方法利用了神经网络的非线性映射能力,能够较好的对目标进行预测,仿真实验的结果表明该方法有限强的实用性,值得进一步研究。     

宁夏农业综合开发地理信息辅助决策系统

为更好地满足农业开发规划在分析、选择、决策等方面的需求,构建地理信息辅助决策规划支持系统,为决策与规划提供数据、信息、知识等层面的辅助支持.在数据层面,主要利用GIS展示各类农业综合开发相关数据;在信息层面,主要利用多种分析工具,通过对数据的概括、归纳以及汇总分析得到决策相关信息;知识层面,主要建设决策规划知识库,通过专家或者是利用工具获取相关知识.     

基于手机信令和导航数据的出行方式识别方法*

基于手机信令识别居民出行方式对于智慧交通规划具有重要意义。本文通过结合信令和导航地图数据,利用聚类算法以及时间关联性算法,实现步行、驾车、公共交通等出行方式的识别。结果表明,结合导航地图数据后识别正确率得到明显提高,与只利用手机信令的识别方法相比,整体准确率提升超过15%,具有较高的识别准确率,同时算法执行时间为187秒,效率较高。整体而言,该识别算法适合在实际工程环境中使用。     

Cross‐Collection Map Inference by Intrinsic Alignment of Shape Spaces

Inferring maps between shapes is a long standing problem in geometry processing. The less similar the shapes are, the harder it is to compute a map, or even define criteria to evaluate it. In many cases, shapes appear as part of a collection, e.g. an animation or a series of faces or poses of the same character, where the shapes are similar enough, such that maps within the collection are easy to obtain. Our main observation is that given two collections of shapes whose “shape space” structure is similar, it is possible to find a correspondence between the collections, and then compute a cross‐collection map. The cross‐map is given as a functional correspondence, and thus it is more appropriate in cases where a bijective point‐to‐point map is not well defined. Our core idea is to treat each collection as a point‐sampling from a low‐dimensional shape‐space manifold, and use dimensionality reduction techniques to find a low‐dimensional Euclidean embedding of this sampling. To measure distances on the shape‐space manifold, we use the recently introduced shape differences, which lead to a similar low‐dimensional structure of the shape spaces, even if the shapes themselves are quite different. This allows us to use standard affine registration for point‐clouds to align the shape‐spaces, and then find a functional cross‐map using a linear solve. We demonstrate the results of our algorithm on various shape collections and discuss its properties.     

Structuring unreliable radio networks

In this paper we study the problem of building a constant-degree connected dominating set (CCDS), a network structure that can be used as a communication backbone, in the dual graph radio network model (Clementi et al. in J Parallel Distrib Comput 64:89–96, 2004; Kuhn et al. in Proceedings of the international symposium on principles of distributed computing 2009, Distrib Comput 24(3–4):187–206 2011, Proceedings of the international symposium on principles of distributed computing 2010). This model includes two types of links: reliable, which always deliver messages, and unreliable, which sometimes fail to deliver messages. Real networks compensate for this differing quality by deploying low-layer detection protocols to filter unreliable from reliable links. With this in mind, we begin by presenting an algorithm that solves the CCDS problem in the dual graph model under the assumption that every process $u$ is provided with a local link detector set consisting of every neighbor connected to $u$ by a reliable link. The algorithm solves the CCDS problem in $O\left( \frac{\varDelta \log ^2{n}}{b} + \log ^3{n}\right) $ rounds, with high probability, where $\varDelta $ is the maximum degree in the reliable link graph, $n$ is the network size, and $b$ is an upper bound in bits on the message size. The algorithm works by first building a Maximal Independent Set (MIS) in $\log ^3{n}$ time, and then leveraging the local topology knowledge to efficiently connect nearby MIS processes. A natural follow-up question is whether the link detector must be perfectly reliable to solve the CCDS problem. With this in mind, we first describe an algorithm that builds a CCDS in $O(\varDelta $ polylog $(n))$ time under the assumption of $O(1)$ unreliable links included in each link detector set. We then prove this algorithm to be (almost) tight by showing that the possible inclusion of only a single unreliable link in each process’s local link detector set is sufficient to require $\varOmega (\varDelta )$ rounds to solve the CCDS problem, regardless of message size. We conclude by discussing how to apply our algorithm in the setting where the topology of reliable and unreliable links can change over time.     

On Minimum Witnesses for Boolean Matrix Multiplication

Minimum witnesses for Boolean matrix multiplication play an important role in several graph algorithms. For two Boolean matrices A and B of order n, with one of the matrices having at most m nonzero entries, the fastest known algorithms for computing the minimum witnesses of their product run in either O(n ^2.575) time or in O(n ²+mnlog(n ²/m)/log² n) time. We present a new algorithm for this problem. Our algorithm runs either in time $$\tilde{O}\bigl(n^{\frac{3}{4-\omega}}m^{1-\frac{1}{4-\omega }}\bigr) $$ where ω<2.376 is the matrix multiplication exponent, or, if fast rectangular matrix multiplication is used, in time $$O\bigl(n^{1.939}m^{0.318}\bigr). $$ In particular, if ω?1<α<2 where m=n ^α , the new algorithm is faster than both of the aforementioned algorithms.     

Using MDA for integration of heterogeneous components in software supply chains

Software product lines are increasingly built using components from specialized suppliers. A company that is in the middle of a supply chain has to integrate components from its suppliers and offer (partially configured) products to its customers. To satisfy both the variability required by each customer and the variability required to satisfy different customers’ needs, it may be necessary for such a company to use components from different suppliers, partly offering the same feature set. This leads to a product line with alternative components, possibly using different mechanisms for interfacing, binding and variability, which commonly occurs in embedded software development.     

基于深度神经网络的Morse码自动译码算法

在军用和民用领域,Morse电报一直是一种重要的短波通信手段,但目前的自动译码算法仍然存在准确率低、无法适应低信噪比和不稳定的信号等问题。该文引入深度学习方法构建了一个Morse码自动识别系统,神经网络模型由卷积神经网络、双向长短时记忆网络和连接时序分类层组成,结构简单,且能够实现端到端的训练。相关实验表明,该译码系统在不同信噪比、不同码速、信号出现频率漂移以及不同发报手法引起的码长偏差等情况下,均能取得较好的识别效果,性能优于传统的自动识别算法。