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This article proposes a method of estimating the concentrations of particulate matter (PM2.5 and PM10) using public data, including road-traffic closed-circuit television (CCTV) images, Smart Seoul City data sensor environment information, and Korea Meteorological Administration data. The region-of-interest images and full scenes derived from CCTV footage were used as the basis for the deep learning model, which combines a convolutional neural network and long short-term memory, to establish the particulate matter (PM) concentration prediction methodology. In the experiment, the prediction accuracies corresponding to various types of mean values were calculated by training the model with various mean measurement values for the surface PM2.5 and PM10 concentrations, as well as the corresponding CCTV images and weather data at different time points. In the experiments performed under relatively stable PM concentrations, R2 generally exceeded 0.9 and tended to increase with an increasing range of mean concentration values. In particular, in sections with rapid changes in the PM concentration within an hourly interval, higher R2 values were obtained by the model trained with the average PM concentrations of the time series before and after image capture, outperforming the method that used prior mean observation values and better reflecting the current PM concentration.

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

A new interconnection network for massively parallel computing is introduced. This network is called an optical multi-mesh hypercube (OMMH) network. The OMMH integrates positive features of both hypercube (small diameter, high connectivity, symmetry, simple control and routing, fault tolerance, etc.) and mesh (constant node degree and scalability) topologies and at the same time circumvents their limitations (e.g., the lack of scalability of hypercubes, and the large diameter of meshes). The OMMH can maintain a constant node degree regardless of the increase in the network size. In addition, the flexibility of the OMMH network makes it well suited for optical implementations. This paper presents the OMMH topology, analyzes its architectural properties and potentials for massively parallel computing, and compares it to the hypercube. Moreover, it also presents a three-dimensional optical design methodology based on free-space optics. The proposed optical implementation has totally space-invariant connection patterns at every node, which enables the OMMH to be highly amenable to optical implementation using simple and efficient large space-bandwidth product space-invariant optical elements. >

Two important parameters of a network for massively parallel computers are scalability and modularity. Scalability has two aspects: size and time (or generation). Size scalability refers to the property that the size of the network can be increased with nominal effect on the existing configuration. Also, the increase in size is expected to result in a linear increase in performance. Time scalability implies that the communication capabilities of a network should be large enough to support the evolution of processing elements through generations. A modular network enables the construction of a large network out of many smaller ones. The lack of these two important parameters has limited the use of certain types of interconnection networks in the area of massively parallel computers. We present a new modular optical interconnection network, called an optical multimesh hypercube (OMMH), which is both size and time scalable. The OMMH combines positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the torus (constant node degree and size scalability) networks. Also presented is a three-dimensional optical implementation of the OMMH network. A basic building block of the OMMH network is a hypercube module that is constructed with free-space optics to provide compact and high-density localized hypercube connections. The OMMH network is then constructed by the connection of such basic building blocks with multiwavelength optical fibers to realize torus connections. The proposed implementation methodology is intended to exploit the advantages of both space-invariant free-space and multiwavelength fiber-based optical interconnect technologies. The analysis of the proposed implementation shows that such a network is optically feasible in terms of the physical size and the optical power budget.

We present a new design methodology for constructing three-dimensional space-invariant hypercube interconnection networks. The methodology is based on a graph bipartitioning technique and permits the construction of larger hypercube networks from smaller networks in a systematic and incremental fashion. Owing to their totally space-invariant nature, the resulting three-dimensional hypercube networks are amenable to optical implementations with simple optical hardware such as multiple imaging components and space-invariant holographic techniques.

The authors present an improved approach to one-step C-testability of orthogonal two-dimensional iterative logic arrays. This is an improvement of the approach of W. Huang and F. Lombardi (1988) and H. Elhuni et al. (1986). A group of sufficient conditions to test two-dimensional iterative logic arrays with a constant number of test vectors independent of the array size (C-testability) is stated. It is proved that the proposed approach requires a smaller number of test vectors than in previous works. >

An important issue in the design of interconnection networks for massively parallel computers is scalability. Size-scalability refers to the property that the number of nodes in the network can be increased with negligible effect on the existing configuration and generation-scalability implies that the communication capabilities of a network should be large enough to support the evolution of processing elements through generations. The lack of size-scalability has limited the use of certain types of interconnection networks (e.g. hypercube) in the area of massively parallel computing. The authors present a new optical interconnection network, called an Optical Multi-Mesh Hypercube (OMMH), which is both size- and generation-scalable while combining positive features of both the hypercube (small diameter, high connectivity, symmetry, simple routing, and fault tolerance) and the mesh (constant node degree and scalability) networks. Also presented is a three-dimensional optical implementation methodology of the OMMH network. >

Among many protocols, the TCP/IP protocol suite is the most widely used form of networking computers. With the advent of high-speed communication paradigms such as asynchronous transfer mode (ATM) and the advances of transmission media technologies such as optical fibers, the physical transmission medium is no longer the performance bottleneck in communication systems. Thus, transport layers are currently receiving much attention, since they don't seem to convey the large bandwidths of the lower layers to the application users properly. Many other transport protocols have been proposed and implemented, which might perform better than TCP/IP. However, due to the popularity of TCP/IP, no other protocols seem to ready to take its place in the near future. Thus, emerging bandwidth-hungry applications steadily require higher and higher TCP/IP performance. In this paper, we analyze the performance of TCP/IP under the UNIX operating system in exchanging data once a connection is established. By measuring accurate data for the various aspects of the protocol implementation, we clearly illustrate the major bottlenecks and determine the upper bounds on performance. We also measure memory bandwidth requirements in processing high-speed TCP/IP. Empirical results show that the TCP/IP protocol itself can handle up to 85 Mbps to process data under the UNIX operating system when a proper data link layer interface is provided, requiring a memory bandwidth of 172 MByte/s.

As an alternative to the hypercube, the binary de Bruijn (BdB) network is recently receiving much attention. The BdB not only provides a logarithmic diameter fault tolerance, and simple routing but also requires fewer links than the hypercube for the same network size. Additionally, a major advantage of the BdB network is a constant node degree: the number of edges per node is independent of the network size. This makes it very desirable for large scale parallel systems. However, due to its asymmetrical nature and global connectivity it is posing a major challenge for VLSI technology. Optics, owing to its three dimensional and global connectivity nature seems to be very suitable for implementing BdB networks. We present an implementation methodology for BdB networks. The distinctive feature of the proposed implementation methodology is partitionability of the network into a few primitive operations that can be efficiently implemented. We further show feasibility of the presented design methodology by proposing an optical implementation of the BdB network.

Free-space optical interconnection networks can be classified into two types, space variant and space invariant, according to the degree of space variance [1]. The degree of space variance determines the network’s complexity and regularity. A totally space variant network allows a completely arbitrary interconnection between nodes, whereas a totally space invariant network has a definite, regular structure with all the nodes having the same connection patterns. In terms of physical implementations, the degree of space variance can be interpreted as the degree of sharing beam steering optics among the nodes of a given network. In other words, all nodes in a totally space-invariant network can share a single beam steering optics (considering multiple fanouts as one steering optics) to realize the given network topology, whereas, in a totally space variant network, each node requires a distinct beam steering optics. This is one of the reasons why space variant networks require complex optical implementations that often result in low interconnection density and high cost.

A new design methodology for constructing three-dimensional optical space-invariant hypercube interconnection networks is presented. The methodology allows the construction of larger hypercube networks from smaller networks in a systematic and incremental fashion. It is shown that the proposed methodology greatly improves area utilization as compared with previous methods. Due to its totally space-invariant nature, the resulting three-dimensional hypercube networks are highly amenable to optical implementations using simple optical hardware such as multiple imaging components and space-invariant holographic techniques.

One of the most popular interconnection networks for parallel computers today is the binary n-cube, also called the hypercube, interconnection network. The attractiveness of the hypercube topology is its small diameter, simple and efficient message routing algorithms, strong connectivity, regularity, symmetry, and fault-tolerance. Unfortunately, conventional VLSI technology seems to be reaching its fundamental physical limits and is, therefore, unable to implement large-scale interconnection networks based on the hypercube topology (or any other topology for that matter) that provide high communication bandwidth, short latency, and reasonable power requirements[1].

We propose a novel compiler-directed cache management scheme which allows parallel invalidation of a subset of array elements. The scheme limits non-stale data invalidations using a novel memory allocation technique. Its correctness is proved using a flow graph model. It is also shown that the scheme provides more cacheability than the previous compiler-directed ones and has lower overhead in determining read hit at runtime. A new performance parameter called unwanted invalidation ratio, for compiler-directed coherence schemes is also proposed.

A new design methodology for constructing optical space-invariant hypercube interconnection networks for connection of a two-dimensional array of inputs to a two-dimensional array of outputs is presented. The methodology permits the construction of larger hypercube networks from smaller networks in a systematic and incremental fashion. It is shown that the proposed methodology greatly improves area utilization as compared with previous methods. An example network is provided that illustrates the proposed design method. Owing to their totally space-invariant nature, the resulting three-dimensional hypercube networks are highly amenable to optical implementations by use of simple optical hardware such as multiple-imaging components and space-invariant holographic techniques. We present a space-invariant optical implementation technique for the realization of such networks. A theoretical analysis of the physical limitations of the implementation method is also presented. The analysis indicates that two-dimensional arrays of 512 × 512 nodes interconnected in a hypercube (18-cube) topology could be implemented.