Distributed power control is crucial for ensuring reliable communication and minimizing energy consumption in wireless communication networks, particularly under random switching topologies and uncertain network dynamics. Existing methods primarily rely on traditional norms or control paradigms, often facing computational challenges due to inherent nonconvexities. In this paper, we propose a novel distributed optimization framework for wireless networks using DC programming to minimize the L1/L∞-Hankel norm of the system. Unlike conventional approaches that may yield suboptimal solutions or suffer from computational inefficiency, the DC programming approach systematically decomposes the nonconvex Hankel norm minimization problem into tractable convex subproblems, ensuring rapid convergence and globally optimal or near-optimal solutions. Our method specifically addresses dynamic topologies modeled by random switching and parameter uncertainties, significantly improving robustness and interference management. Simulation results demonstrate that the proposed framework effectively maintains the desired SINR levels and accelerates convergence of global solution.

Software Defined Network (SDN) is revolutionizing the networking industry by a potential of enabling flexible and uniform management by decoupling the control plane and forwarding plane. However, there are several issues on scalability and adaption against conventional network protocols to implement on the large scaled network. To address these issues, we propose an implementation method which runs SDN controller and Quagga on SDN switch in this paper. By controlling information of each SDN controller uniformly with the upper layer controller, it could be able to handle each problems of implementing SDN without missing the advantage of SDN. Furthermore, by implementing prototype of our model, we will discuss some issues on the implementation of SDN system.

In this paper, we propose an efficient calibration method to estimate the pointing direction via a human pointing gesture to facilitate robot interaction. The ways in which pointing gestures are used by humans to indicate an object are individually diverse. In addition, people do not always point at the object carefully, which means there is a divergence between the line from the eye to the tip of the index finger and the line of sight. Hence, we focus on adapting to these individual ways of pointing to improve the accuracy of target object identification by means of an effective calibration process. We model these individual ways as two offsets, the horizontal offset and the vertical offset. After locating the head and fingertip positions, we learn these offsets for each individual through a training process with the person pointing at the camera. Experimental results show that our proposed method outperforms other conventional head-hand, head-fingertip, and eye-fingertip-based pointing recognition methods.

In this paper, we present a practical and automatic comic-like image layout system for event photos or event movies. We focus on the comic-like layout with cropping. The comic-like layouts allow viewers to track events in time sequential order like comic books, in which event images are cropped and arranged in panels from top left to bottom right on a screen so that the resultant layout has variety of image shapes and gives a pleasant impression. The main drawback of the conventional method is that an important region such as a face or a salient object area is sometimes cropped. In order to avoid excessive cropping of important regions while maintaining the image order, our system achieves adaptive layout to input images by generating all possible templates suitable for comic and deciding appropriate templates based on our proposed layout evaluation. In the demo, we will show that our full automatic system generates comic-like layouts immediately. Furthermore, the system allows a user to select his/her favorite layout from the multiple ones with different looks.

This paper presents an analytical and experimental evaluation of sensorless speed vector control of an axial gap self bearing motor, in which rotor speed and position are estimated by using Sliding mode observer. The approach is based on the estimation of the motor back-EMF (or induced voltage) through a sliding mode observer with help of measured stator currents and reference voltages. In order to achieve an accurate estimation of the rotor speed and position, an adaptive gain of sliding mode controller is proposed. In addition, during the operation of system, low pass filters are used to smooth the estimated variables. Experiment is implemented based on dSpace1104 with two three phase inverters. Results confirm that axial force and rotating torque can be controlled independently and motor can get the good performance in steady state at the average and high speed range, so further work is being continued to improve the experiment for this proposal.

This paper proposes a method to control texture resolution for rendering large-scale 3D urban maps. Recently, performance of computers has been developed rapidly, but it is still hard to directly handle the large-scale 3D maps on PCs, because of its high requirement for hardware capability. Thus the data reduction is essential for rendering of the large map. Since on the 3D maps, texture data, in general, tend to be far larger than geometry data, it is more effective to reduce the texture data by exploiting the LOD (Level of Detail) in order to decrease whole data size. To achieve this, we propose a method to control the resolution of the texture. In our method, the textures are classified into some classes. The appropriate texture resolutions are decided according to their rendered sizes on a display and their importance, and the classes they belong to. We verify the validity of our resolution control method by some experiments.

A PCAAG introduced by Morita and Ueno is a parallel array generator on a partitioned cellular automaton (PCA) that generates an array language (i.e. a set of symbol arrays). A "reversible" PCAAG (RPCAAG) is a backward deterministic PCAAG, and thus parsing of two-dimensional patterns can be performed without backtracking by an "inverse" system of the RPCAAG. Hence, a parallel pattern recognition mechanism on a deterministic cellular automaton can be directly obtained from a RPCAAG that generates the pattern set. In this paper, we investigate the generating ability of RPCAAGs and their subclass. It is shown that the ability of RPCAAGs is characterized by two-dimensional deterministic Turing machines, i.e. they are universal in their generating ability. We then investigate a monotonic RPCAAG (MRPCAAG), which is a special type of an RPCAAG that satisfies monotonic constraint. We show that the generating ability of MRPCAAGs is exactly characterized by two-dimensional deterministic linear-bounded automata.

We propose a new system of generating array languages in parallel, based on a partitioned cellular automaton (PCA), a kind of cellular automaton. This system is called a PCA array generator (PCAAG). The characteristic of PCAAG is that a”reversible” version is easily defined. A reversible PCA (RPCA) is a backward deterministic PCA, and we can construct a deterministic “inverse” PCA that undoes the operations of the RPCA. Thus if an array language is generated by an RPCA, it can be parsed in parallel by a deterministic inverse PCA without backtracking. We also define two subclasses of PCAAG, and give examples of them that generate geometrical figures.