Abstract In order to cut down the management expense, the government must initiatively reduce the contact hours with people. Owing to the declination of people¡¦s daily time available and the decrease of expecting counter services, it is demanded that the services provided by the government be more timesaving and convenient. People's accepting degree on network and computerized government, an invisible customer services, is therefore elevated. In response to Territory Bureau's computerization exercise, the Ministry of Interior has founded a group, "management and research group for Territory Bureau's computer network", to work on the program since 1980. The prevalence of mobile communication and the application of in-time internet network these years have led to establishment of Territory Bureau's computerized system and led to transformation of the Bureau's exercise from manpower to computer network service. It is already a trend to deal work via Internet. It is urgent that government promotes computerization to the Bureau of Territory to make it more flexible and agile in order to provide people with more integral, convenient and versatile services. This study, from the users' viewpoint, explores the factors that influence the implement of Territory Bureau's computer network. The purpose is to understand the factors that influence the users and to provide further information for future expansion of the Bureau's program. Since the Territory Bureau's computer-network system is an innovative technology, the study of people's attitude and acceptation toward such technology is innovative as well. "Technology acceptance model" is the theoretical background in this study that explains users' behavioral intention in adopting the Territory Bureau's network information. By applying the theory of "diffusion of innovation", the influences of "relative advantage", "compatibility" and "complexity" for using the above system is investigated and discussed. Statistical questionnaires' survey is adopted in this study. The results show that the more "positive attitude" people have, the more "behavioral intention" they have in utilizing Territory Bureau's Internet information. The following factors are found to affect people's "positive attitude": more "relative advantage", "compatibility" and "complexity" people know about; more "perceived usefulness" and "perceived ease of use" people realize toward Territory Bureau's internet informatio

This work is motivated from error-correcting codes in the brain. To counteract the effect of representation noise, a large number of neurons participate in encoding even low-dimensional variables. In many brain areas, the mean firing rates of neurons as a function of represented variable, called the tuning curve, have unimodal shape centered at different values, defining a unary code. This dissertation focuses on a new type of neural code where neurons have periodic tuning curves, with a diversity of periods. Neurons that exhibit this tuning are grid cells of the entorhinal cortex, which represent self-location in two-dimensional space. First, we investigate mutual information between such multi-scale codes and the coded variable as a function of tuning curve width. For decoding, we consider maximum likelihood (ML) and plausible neural network (NN) based models. For unary neural codes, Fisher information increases with narrower tuning, regardless of the decoding method. By contrast, for the multi-scale neural code, the optimal tuning curve width depends on the decoding method. While narrow tuning is optimal for ML decoding, a finite width, matched to statistics of the noise, is optimal with a NN decoder. This finding may explain why actual neural tuning curves have relatively wide tuning. Next, motivated by the observation that multi-scale codes involve non-trivial decoding, we examine a decoding algorithm based on belief propagation (BP) because BP promises certain gains in decoding efficiency. The decoding problem is first formulated as a subset selection problem on a graph and then approximately solved by BP. Even though the graph has many cycles, BP converges to a fixed point after few iterations. The mean square error of BP approaches to that of ML at high signal-to-noise ratios. Finally, using the multi-scale code, we propose a joint source-channel coding scheme that allows separate senders to transmit complementary information over additive Gaussian noise channels without cooperation. The receiver decodes one sender's codeword using the other as side information and achieves a lower distortion using the same number of transmissions. The proposed scheme offers a new framework to design distributed joint source-channel codes for continuous variables. / text

This diploma thesis deals with the information system in organization AIESEC Slovakia. Through appropriate methods and analysis, the current state of the IS will be assessed. Based on results of these analyzes, there will be proposed changes that will lead ultimately to higher efficiency of the organization and improvement of quality of services provided.

Cooperative communication is seen as a key concept to achieve ultra-reliable communication in upcoming fifth-generation mobile networks (5G). A promising cooperative communication concept is multiterminal source-channel coding, which attracted recent attention in the research community. This thesis lays theoretical foundations for understanding the performance of multiterminal source-channel codes in a vast variety of cooperative communication networks. To this end, we decouple the multiterminal source-channel code into a multiterminal source code and multiple point-to-point channel codes. This way, we are able to adjust the multiterminal source code to any cooperative communication network without modification of the channel codes. We analyse the performance in terms of the outage probability in two steps: at first, we evaluate the instantaneous performance of the multiterminal source-channel codes for fixed channel realizations; and secondly, we average the instantaneous performance over the fading process. Based on the performance analysis, we evaluate the performance of multiterminal source-channel codes in three cooperative communication networks, namely relay, wireless sensor, and multi-connectivity networks. For all three networks, we identify the corresponding multiterminal source code and analyse its performance by the rate region for binary memoryless sources. Based on the rate region, we derive the outage probability for additive white Gaussian noise channels with quasi-static Rayleigh fading. We find results for the exact outage probability in integral form and closed-form solutions for the asymptotic outage probability at high signal-to-noise ratio. The importance of our results is fourfold: (i) we give the ultimate performance limits of the cooperative communication networks under investigation; (ii) the optimality of practical schemes can be evaluated with respect to our results, (iii) our results are suitable for link-level abstraction which reduces complexity in network-level simulation; and (iv) our results demonstrate that all three cooperative communication networks are key technologies to enable 5G applications, such as device to device and machine to machine communications, internet of things, and internet of vehicles. In addition, we evaluate the performance improvement of multiterminal source-channel codes over other (non-)cooperative communications concepts in terms of the transmit power reduction given a certain outage probability level. Moreover, we compare our theoretical results to simulated frame-error-rates of practical coding schemes. Our results manifest the superiority of multiterminal source-channel codes over other (non-)cooperative communications concepts.

A converse method is developed for the source broadcast problem. Specifically, it is shown that the separation architecture is optimal for a variant of the source broadcast problem and the associated source-channel separation theorem can be leveraged, via a reduction argument, to establish a necessary condition for the original problem, which uni es several existing results in the literature. Somewhat surprisingly, this method, albeit based on the source-channel separation theorem, can be used to prove the optimality of non-separation based schemes and determine the performance limits in certain scenarios where the separation architecture is suboptimal. / Thesis / Master of Applied Science (MASc)

A large number of complex systems can be modelled as networks of interacting units. From a mathematical point of view the topology of such systems can be represented as graphs of which the nodes represent individual elements of the system and the edges interactions or relations between them. In recent years networks have become a principal tool for analyzing complex systems in many different fields. This thesis introduces an information theoretic approach for finding characteristic connectivity patterns of networks, also called network motifs. Network motifs are sometimes also referred to as basic building blocks of complex networks. Many real world networks contain a statistically surprising number of certain subgraph patterns called network motifs. In biological and technological networks motifs are thought to contribute to the overall function of the network by performing modular tasks such as information processing. Therefore, methods for identifying network motifs are of great scientific interest. In the prevalent approach to motif analysis network motifs are defined to be subgraphs that occur significantly more often in a network when compared to a null model that preserves certain features of the network. However, defining appropriate null models and sampling these has proven to be challenging. This thesis introduces an alternative approach to motif analysis which looks at motifs as regularities of a network that can be exploited to obtain a more efficient representation of the network. The approach is based on finding a subgraph cover that represents the network using minimal total information. Here, a subgraph cover is a set of subgraphs such that every edge of the graph is contained in at least one subgraph in the cover while the total information of a subgraph cover is the information required to specify the connectivity patterns occurring in the cover together with their position in the graph. The thesis also studies the connection between motif analysis and random graph models for networks. Developing random graph models that incorporate high densities of triangles and other motifs has long been a goal of network research. In recent years, two such model have been proposed . However, their applications have remained limited because of the lack of a method for fitting such models to networks. In this thesis, we address this problem by showing that these models can be formulated as ensembles of subgraph covers and that the total information optimal subgraph covers can be used to match networks with such models. Moreover, these models can be solved analytically for many of their properties allowing for more accurate modelling of networks in general. Finally, the thesis also analyzes the problem of finding a total information optimal subgraph cover with respect to its computational complexity. The problem turns out to be NP-hard hence, we propose a greedy heuristic for it. Empirical results for several real world networks from different fields are presented. In order to test the presented algorithm we also consider some synthetic networks with predetermined motif structure.

A wireless relay network consists of multiple source nodes, multiple destination nodes, and possibly many relay nodes in between to facilitate its transmission. It is clear that the performance of such networks highly depends on information for- warding strategies adopted at the relay nodes. This dissertation studies a particular information forwarding strategy called compute-and-forward. Compute-and-forward is a novel paradigm that tries to incorporate the idea of network coding within the physical layer and hence is often referred to as physical layer network coding. The main idea is to exploit the superposition nature of the wireless medium to directly compute or decode functions of transmitted signals at intermediate relays in a net- work. Thus, the coding performed at the physical layer serves the purpose of error correction as well as permits recovery of functions of transmitted signals. For the bidirectional relaying problem with Gaussian channels, it has been shown by Wilson et al. and Nam et al. that the compute-and-forward paradigm is asymptotically optimal and achieves the capacity region to within 1 bit; however, similar results beyond the memoryless case are still lacking. This is mainly because channels with memory would destroy the lattice structure that is most crucial for the compute-and-forward paradigm. Hence, how to extend compute-and-forward to such channels has been a challenging issue. This motivates this study of the extension of compute-and-forward to channels with memory, such as inter-symbol interference. The bidirectional relaying problem with parallel Gaussian channels is also studied, which is a relevant model for the Gaussian bidirectional channel with inter-symbol interference and that with multiple-input multiple-output channels. Motivated by the recent success of linear finite-field deterministic model, we first investigate the corresponding deterministic parallel bidirectional relay channel and fully characterize its capacity region. Two compute-and-forward schemes are then proposed for the Gaussian model and the capacity region is approximately characterized to within a constant gap. The design of coding schemes for the compute-and-forward paradigm with low decoding complexity is then considered. Based on the separation-based framework proposed previously by Tunali et al., this study proposes a family of constellations that are suitable for the compute-and-forward paradigm. Moreover, by using Chinese remainder theorem, it is shown that the proposed constellations are isomorphic to product fields and therefore can be put into a multilevel coding framework. This study then proposes multilevel coding for the proposed constellations and uses multistage decoding to further reduce decoding complexity.

While IS research into on-line Inter-Personal (IP) Social Networks (SN) is highly visible, there has been surprisingly little focus on the use of on-line social networks for Inter-Organizational (IO) communications, interactions, and goal achievement. We explore the issues and challenges facing organizations in their design and use of inter-organizational social network information systems (IO SNIS). Artifact design principles are drawn from a new and insightful model that contrasts the advantages of existing innovative inter-personal (IP) SNIS artifacts with Social Network Theory on differences between IP and IO Social Networks. This research extends the existing streams of IS social networking research into the inter-organizational domain and encourages additional IS research into the analysis, design, and build of artifacts that animate the social behavior of organizations. We develop a key design concept for IO SNIS and establish the design principles underlying the general artifact design and the specific design features that apply the design constructs to an exemplar IO social domain. This dissertation uses Action Design Research (ADR) approach within the Design Science Research (DSR) paradigm to formulate the research opportunity and anticipate a practice-inspired and theory-ingrained artifact. The researcher works with a practitioner team in the domain of mid-market private equity (MMPE) to explore the model and evaluate existing on-line inter-organizational artifacts to establish specific design features for an IO SNIS artifact. We find that the design principles can generalize from the IO SNIS Design Concept Model to other IO Social domains and that the design features can be used to build an instantiation of IO SNIS in the Private Equity domain.

Network Coding is a technique which looks beyond traditional store-and-forward approach followed by routers and switches in communication networks, and as an extension introduces maps termed as ‘local encoding kernels’ and ‘global encoding kernels’ defined for each communication link in the network. The purpose of both these maps is to define rules as to how to combine the packets input on the node to form a packet going out on an edge. The paradigm of network coding was formally and for the first time introduced by Ahlswede et al. in [1], where they also demonstrated its use in case of single-source multiple-sink network multicast, although with use of much complex mathematical apparatus. In [1], examples of networks are also presented where it is shown that network coding can improve the overall throughput of the network which can not otherwise be realized by the conventional store-and-forward approach. The main result in [1], i.e. the capacity of single-source multiple-sinks information network is nothing but the minimum of the max-flows from source to each sink, was again proved by Li, Yeung, and Cai in [2] where they showed that only linear operations suffice to achieve the capacity of multicast network. The authors in [2] defined generalizations to the multicast problem, which they termed as linear broadcast, linear dispersion, and Generic LCM as strict generalizations of linear multicast, and showed how to build linear network codes for each of these cases. For the case of linear multicast, Koetter and Medard in [3] developed an algebraic framework using tools from algebraic geometry which also proved the multicast max-flow min-cut theorem proved in [1] and [2]. It was shown that if the size of the finite field is bigger than a certain threshold, then there always exists a solution to the linear multicast, provided it is solvable. In other words, a solvable linear multicast always has a solution in any finite field whose cardinality is greater than the threshold value. The framework in [3] also dealt with the general linear network coding problem involving multiple sources and multiple sinks with non-uniform demand functions at the sinks, but did not touched upon the key problem of finding the characteristic(s) of the field in which it may have solution. It was noted in [5] that a solvable network may not have a linear solution at all, and then introduced the notion of general linear network coding, where the authors conjectured that every solvable network must have a general linear solution. This was refuted by Dougherty, Freiling, Zeger in [6], where the authors explicitly constructed example of a solvable network which has no general linear solution, and also networks which have solution in a finite field of char 2, and not in any other finite field. But an algorithm to find the characteristic of the field in which a scalar or general linear solution(if at all) exists did not find any mention in [3] or [6]. It was a simultaneous discovery by us(as part of this thesis) as well as by Dougherty, Freiling, Zeger in [7] to determine the characteristics algorithmically. Applications of Network Coding techniques to wireless networks are seen in literature( [8], [9], [10]), where [8] provided a variant of max-flow min-cut theorem for wireless networks in the form of linear programming constraints. A new architecture termed as COPE was introduced in [10] which used opportunistic listening and opportunistic coding in wireless mesh networks.

We study a scalable approach to information fusion for large sensor networks. The algorithm, field inversion by consensus and compressed sensing (FICCS), is a distributed method for detection, localization, and estimation of a propagating field generated by an unknown number of point sources. The approach combines results in the areas of distributed average consensus and compressed sensing to form low dimensional linear projections of all sensor readings throughout the network, allowing each node to reconstruct a global estimate of the field. Compressed sensing is applied to continuous source localization by quantizing the potential locations of sources, transforming the model of sensor observations to a finite discretized linear model. We study the effects of structured modeling errors induced by spatial quantization and the robustness of ℓ1 penalty methods for field inversion. We develop a perturbations method to analyze the effects of spatial quantization error in compressed sensing and provide a model-robust version of noise-aware basis pursuit with an upperbound on the sparse reconstruction error. Numerical simulations illustrate system design considerations by measuring the performance of decentralized field reconstruction, detection performance of point phenomena, comparing trade-offs of quantization parameters, and studying various sparse estimators. The method is extended to time-varying systems using a recursive sparse estimator that incorporates priors into ℓ1 penalized least squares. This thesis presents the advantages of inter-sensor measurement mixing as a means of efficiently spreading information throughout a network, while identifying sparse estimation as an enabling technology for scalable distributed field reconstruction systems.