Silicon-quantum-dots (Si-QDs) with an areal density as high as 1012 cm - 2 were self-assembled on thermally-grown SiO2 by low pressure CVD using Si2H6, in which OH-terminated SiO2 surface prior to the Si CVD was exposed to GeH4 to create nucleation sites uniformly. After thermal oxidation of Si-QDs surface, two-dimensional electronic transport through the Si-QDs array was measured with co-planar Al electrodes evaporated on the array surface. Random telegraph signals were clearly observed at constant applied bias conditions in dark condition and under light irradiation at room temperature. The result indicates the charging and discharging of a dot adjacent to the percolation current path in the dots array.

The Distributed Denial of Service attack (DDoS) is one of the major threats to network security that exhausts network bandwidth and resources. Recently, an efficient approach Live Baiting was proposed for detecting the identities of DDoS attackers in web service using low state overhead without requiring either the models of legitimate requests nor anomalous behavior. However, Live Baiting has two limitations. First, the detection algorithm adopted in Live Baiting starts with a suspects list containing all clients, which leads to a high false positive probability especially for large web service with a huge number of clients. Second, Live Baiting adopts a fixed threshold based on the expected number of requests in each bucket during the detection interval without the consideration of daily and weekly traffic variations. In order to address the above limitations, we first distinguish the clients activities (Active and Non-Active clients during the detection interval) in the detection process and then further propose a new adaptive threshold based on the Change Point Detection method, such that we can improve the false positive probability and avoid the dependence of detection on sites and access patterns. Extensive trace-driven simulation has been conducted on real Web trace to demonstrate the detection efficiency of the proposed scheme in comparison with the Live Baiting detection scheme.

This paper describes an efficient image enhancement method based on the Multi-Scale Retinex (MSR) approach for pre-processing of video applications. The processing amount is drastically reduced to 4 orders less than that of the original MSR, and 1 order less than the latest fast MSR method. For the efficient processing, our proposed method employs multi-stage and multi-rate filter processing which is constructed by a x-y separable and polyphase structure. In addition, the MSR association is effectively implemented during the above multi-stage processing. The method also modifies a weighting function for enhancement to improve color rendition of bright areas in an image. A variety of evaluation results show that the performance of our simplified method is similar to those of the original MSR, in terms of visual perception, contrast enhancement effects, and hue changes. Moreover, experimental results show that pre-processing of the proposed method contributes to clear foreground object separation.

We designed and fabricated 4H-SiC PIN avalanche photodiodes (APD) for UV detection. The thickness of an intrinsic layer in a PIN structure was optimized in order to achieve the highest quantum efficiency at the wavelength of interest. The optimized 4H-SiC PIN APDs exhibited a maximum external quantum efficiency of >80% at the wavelength of 280 nm and a gain greater than 40000. Both electrical and optical characteristics of the fabricated APDs were in agreement with those predicted from simulation.

Quantum key distribution (QKD) systems can generate unconditionally secure common keys between remote users. Improvements of QKD performance, particularly in key generation rate, have been required to meet current network traffic. A high-speed QKD system should be equipped with low-loss receivers with high visibility, highly efficient photon detectors with small dark count probability. A solution to these issues is to employ planar lightwave circuit (PLC) interferometers, single photon detection circuits and modules, together with multi-wavelength channels transmission using wavelength division multiplexing (WDM) technique.

In performing the program operation of the NAND-type flash memory array, the program-inhibited cell is applied by a positive voltage at the gate, i.e., word-line (WL) on the floating channel while the program cell is applied by program voltage as the two ends, drain select line (DSL) and source select line (SSL), are turned on with grounded bit-line (BL). In this manner, the self-boosting of silicon channel to avoid unwanted program operation is made possible. As the flash memory device is aggressively scaled down and the channel doping concentration is increased accordingly, the coupling phenomena among WL, floating gate (FG)/storage node, and silicon channel, which are crucial factors in the self-boosting scheme, should be investigated more thoroughly. In this work, the dependences of self-boosting of channel potential on channel length and doping concentration in the 2-D conventional planar and 3-D FinFET NAND-type flash memory devices based on bulk-silicon are investigated by both 2-D and 3-D numerical device simulations. Since there hardly exists realistic ways of measuring the channel potential by physical probing, the series of simulation works are believed to offer practical insights in the variation of channel potential inside a flash memory device.

With the emergence of bandwidth-greedy application services, high-speed transport protocols are expected to effectively and aggressively use large amounts of bandwidth in current broadband and multimedia networks. However, when high-speed transport protocols compete with other standard TCP flows, they can occupy most of the available bandwidth leading to disruption of service. To deploy high-speed transport protocols on the Internet, such unfair situations must be improved. In this paper, therefore, we propose a method to improve fairness, called Kyushu-TCP (KTCP), which introduces a non-aggressive period in the congestion avoidance phase to give other standard TCP flows more chances of increasing their transmission rates. This method improves fairness in terms of the throughput by estimating the stably available bandwidth-delay product and adjusting its transmission rate based on this estimation. We show the effectiveness of the proposed method through simulations.

The recent growth in available network bandwidth envisions the wide-spread use of broadband applications such as uncompressed HD-SDI (High-definition serial digital interface) over IP. These cutting-edge applications are also driving the development of a media-oriented infrastructure for networked collaboration. This paper introduces imCast, a high-quality digital media platform dealing with uncompressed HD-SDI over IP, and discusses its internal architecture in depth. imCast mainly provides cost-effective hardware-based approaches for high-quality media acquisition and presentation; flexible software-based approaches for presentation; and allows for economical network transmission. Experimental results (taken over best-effort IP networks) will demonstrate the functional feasibility and performance of imCast.

In the last decade, the technique of packet classification has been widely deployed in various network devices, including routers, firewalls and network intrusion detection systems. In this work, we improve the performance of packet classification by using multiple hash tables. The existing hash-based algorithms have superior scalability with respect to the required space; however, their search performance may not be comparable to other algorithms. To improve the search performance, we propose a tuple reordering algorithm to minimize the number of accessed hash tables with the aid of bitmaps. We also use pre-computation to ensure the accuracy of our search procedure. Performance evaluation based on both real and synthetic filter databases shows that our scheme is effective and scalable and the pre-computation cost is moderate.

We exhibit a simple procedure to find how classical signals should be processed in cluster-state quantum computation. Using stabilizers characterizing a cluster state, we can easily find a precise classical signal-flow that is required in performing cluster-state computation.

In this paper, we study multi-layer transmission for wireless multimedia multicast in a cell. Under the assumptions that the users in a cell are uniformly well distributed and that the BS has no channel side information, we find the optimal number of transmission layers and power allocation. This result can be used in highly dynamic dense networks and jamming networks where channel side information at the transmitter is somewhat useless.

DOCSIS is the defacto industry standard for cable internet to the home. In this letter, we examine the delay characteristics of commercially deployed DOCSIS networks. We focus on four mechanisms of the DOCSIS MAC operation and develop a computationally simple simulator to reproduce the phenomena produced by these mechanisms. In reproducing these phenomena using our simulator, we demonstrate that the simulator properly encapsulates the core mechanisms of DOCSIS and effectively simulates the delay of packets.

We report successful bottom-up synthesis of small diameter silicon nanowires (SiNWs) on SiO2 and Si3N4 surfaces. SiNWs with diameter comparable to the diameter of the Au nano-particles (10-20 nm) were grown on these surfaces, as well as on Si substrates which are commonly used for the nanowire growth. The growth temperature for obtaining a high density of SiNWs on SiO2 and Si3N4 substrates is higher (460-470) than that of the case of normal Si substrates (440). The growth on patterned substrates demonstrates that SiNWs can be selectively grown. Furthermore, the guided growth over metal structures is also shown to be possible. Selective growth of SiNWs on pre-patterned surfaces opens up the possibility of self-aligning SiNWs for the integration of complex device structures.

This paper presents an adaptive maximum power point tracking (MPPT) algorithm. The aim is to dynamically adjust the step length for updating duty ratio (or operating voltage) so as to make full utilization of the output power of photovoltaic (PV) systems, even under the rapidly changing atmospheric conditions. To this end, the average slope in terms of voltage and power is exploited for reducing the harmful effect of noise and error (incurred in measurement or quantization) on the slope. Also, a statistical decision-making scheme is employed for reliably deciding the time instant at which atmospheric conditions actually change. Empirical study has adduced grounds for its dominance over existing references.

Nowadays, the number of web-browser targeted attacks that lead users to adversaries' web sites and exploit web browser vulnerabilities is increasing, and a clarification of their methods and countermeasures is urgently needed. In this paper, we introduce the design and implementation of a new client honeypot for drive-by-download attacks that has the capacity to detect and investigate a variety of malicious web sites. On the basis of the problems of existing client honeypots, we enumerate the requirements of a client honeypot: 1) detection accuracy and variety, 2) collection variety, 3) performance efficiency, and 4) safety and stability. We improve our system with regard to these requirements. The key features of our developed system are stepwise detection focusing on exploit phases, multiple crawler processing, tracking of malware distribution networks, and malware infection prevention. Our evaluation of our developed system in a laboratory experiment and field experiment indicated that its detection variety and crawling performance are higher than those of existing client honeypots. In addition, our system is able to collect information for countermeasures and is secure and stable for continuous operation. We conclude that our system can investigate malicious web sites comprehensively and support countermeasures.

Network monitoring systems that detect and analyze malicious activities as well as respond against them, are becoming increasingly important. As malwares, such as worms, viruses, and bots, can inflict significant damages on both infrastructure and end user, technologies for identifying such propagating malwares are in great demand. In the large-scale darknet monitoring operation, we can see that malwares have various kinds of scan patterns that involves choosing destination IP addresses. Since many of those oscillations seemed to have a natural periodicity, as if they were signal waveforms, we considered to apply a spectrum analysis methodology so as to extract a feature of malware. With a focus on such scan patterns, this paper proposes a novel concept of malware feature extraction and a distinct analysis method named "SPectrum Analysis for Distinction and Extraction of malware features (SPADE)". Through several evaluations using real scan traffic, we show that SPADE has the significant advantage of recognizing the similarities and dissimilarities between the same and different types of malwares.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

This letter presents an optimal user selection algorithm that provides a maximum sum-rate in a zero-forcing based Multiuser MIMO system for downlink. The proposed technique forms a primary group of users whose channel power exceeds a predetermined threshold. Through computer simulations, we have found that the proposed method outperforms the conventional technique yielding a sum rate that is 0.33 bps/Hz higher when the transmit SNR is 10 dB and the total number of users and transmit antennas in the cell is 100 and 4, respectively.

Robust object tracking is required by many vision applications, and it will be useful for the motion analysis of moving object if we can not only track the object, but also make clear the corresponding relation of each part between consecutive frames. For this purpose, we propose a new method for moving object extraction and tracking based on the exclusive block matching. We build a cost matrix consisting of the similarities between the current frame's and the previous frame's blocks and obtain the corresponding relation by solving one-to-one matching as linear assignment problem. In addition, we can track the trajectory of occluded blocks by dealing with multi-frames simultaneously.

Due to the development and popularization of high-definition televisions, digital video cameras, Blu-ray discs, digital broadcasting, IP television and so on, it plays an important role to identify and quantify video quality degradations. In this paper, we propose SV-CIELAB which is an objective video quality assessment (VQA) method using a spatio-velocity contrast sensitivity function (SV-CSF). In SV-CIELAB, motion information in videos is effectively utilized for filtering unnecessary information in the spatial frequency domain. As the filter to apply videos, we used the SV-CSF. It is a modulation transfer function of the human visual system, and consists of the relationship among contrast sensitivities, spatial frequencies and velocities of perceived stimuli. In the filtering process, the SV-CSF cannot be directly applied in the spatial frequency domain because spatial coordinate information is required when using velocity information. For filtering by the SV-CSF, we obtain video frames separated in spatial frequency domain. By using velocity information, the separated frames with limited spatial frequencies are weighted by contrast sensitivities in the SV-CSF model. In SV-CIELAB, the criteria are obtained by calculating image differences between filtered original and distorted videos. For the validation of SV-CIELAB, subjective evaluation experiments were conducted. The subjective experimental results were compared with SV-CIELAB and the conventional VQA methods such as CIELAB color difference, Spatial-CIELAB, signal to noise ratio and so on. From the experimental results, it was shown that SV-CIELAB is a more efficient VQA method than the conventional methods.