This paper proposes route advertisement policies (RAP) and an inbound traffic engineering (ITE) technique for a multihomed autonomous system (AS) employing the Border Gateway Protocol (BGP) and provider aggregatable (PA) addressing. The proposed RAP avail the advantage of address aggregation benefit of PA addressing. If multiple address spaces are allocated to each of the ASes that are multihomed to multiple upstream ASes, reduction of the forwarding information base (FIB) and quick convergence are achieved. However, multihoming based on PA addressing raises two issues. First, more specific address information is hidden due to address aggregation. Second, multiple allocated address spaces per AS does not provide the capability of ITE. To cope with these two limitations, we propose i) RAP to ensure connectivity among ASes with fewer routes installed in the FIB of each top-tier AS, and ii) an ITE technique to control inbound routes into multihomed ASes. Our ITE technique does not increase the RIB and FIB sizes in the Internet core. We implement the proposed RAP in an emulation environment with BGP using the Quagga software suite and our developed Hierarchical Automatic Number Allocation (HANA) protocols. We use HANA as a tool to automatically allocate hierarchical PA addresses to ASes. We confirm that with our proposed policies the FIB and RIB (routing information base) sizes in tier-1 ASes do not change with the increase of tier-3 ASes, and the number of BGP update messages exchanged is reduced by up to 69.9% from that achieved with conventional BGP RAP. We also confirmed that our proposed ITE technique, based on selective prefix advertisement, can indeed control inbound traffic into a multihomed AS employing PA addressing.

This paper discusses the effect of pre-grouping on vector classification based on the self-organizing map (SOM). The SOM is an unsupervised learning neural network, and is used to form clusters of vectors using its topology preserving nature. The use of SOMs for practical applications, however, may pose difficulties in achieving high recognition accuracy. For example, in image recognition, the accuracy is degraded due to the variation of lighting conditions. This paper considers the effect of pre-grouping of feature vectors on such types of applications. The proposed pre-grouping functionality is also based on the SOM and introduced into a new parallel configuration of the previously proposed SOM-Hebb classifers. The overall system is implemented and applied to position identification from images obtained in indoor and outdoor settings. The system first performs the grouping of images according to the rough representation of the brightness profile of images, and then assigns each SOM-Hebb classifier in the parallel configuration to one of the groups. Recognition parameters of each classifier are tuned for the vectors belonging to its group. Comparison between the recognition systems with and without the grouping shows that the grouping can improve recognition accuracy.

Stereoscopic vision technology is applied in a wide range of fields, from 3D movies to medical care. Stereoscopic vision makes it possible to observe images in parallax between both eyes. However, parallax images cannot be used all the time due to a situation called “occlusion”, in which an object is hidden in the depths by another object. In this case, different images are projected on the right and left retina. Here, we propose a psychology experiment to elucidate the function of parvocellular cells in the LGN of the visual cortex of the brain using occlusion perception. As a new psychology experiment to clarify whether parvocellular cells in the LGN of the visual cortex, said to process chromatic and luminance information, can detect a disagreement between the retinal images produced by each eye, we measured convergence eye movement when gazing at the rim of a column under occlusion using an equiluminance random dot pattern. When eye movement prevented the disagreement of the retinal images caused by occlusion, we thought that convergence eye movement to move both eyes in front of the rim or divergence eye movement to move them behind the rim would occur. In other words, we thought that we could clarify whether there was parvocellular system process agreement or disagreement between the right and left retinal images under equiluminance. Therefore, we examined whether a system to detect disagreement between the retinal images exists in the brain when gazing at the rim of a column onto which an equiluminance random dot texture was mapped. Results suggested that the mechanism to avoid disagreement between the retinal images of the eyes caused by occlusion occurs in the parvocellular cells, which mainly process color information, as well as in the magnocellular cells, which process binocular disparity.

In this research, we propose an effective stability analysis method to impacting systems with periodically moving borders (periodic borders). First, we describe an n-dimensional impacting system with periodic borders. Subsequently, we present an algorithm based on a stability analysis method using the monodromy matrix for calculating stability of the waveform. This approach requires the state-transition matrix be related to the impact phenomenon, which is known as the saltation matrix. In an earlier study, the expression for the saltation matrix was derived assuming a static border (fixed border). In this research, we derive an expression for the saltation matrix for a periodic border. We confirm the performance of the proposed method, which is also applicable to systems with fixed borders, by applying it to an impacting system with a periodic border. Using this approach, we analyze the bifurcation of an impacting system with a periodic border by computing the evolution of the stable and unstable periodic waveform. We demonstrate a discontinuous change of the periodic points, which occurs when a periodic point collides with a border, in the one-parameter bifurcation diagram.

Based on Bezout's theorem, the feasibility condition for interference alignment (IA) is established in a two-way small cell network where part of cells transmit in downlink while the others in uplink. Moreover, the sufficient and necessary condition for the two-way network to achieve as many degrees of freedom (DoFs) as the traditional one-way network is presented. We find that in certain cases every small cell can independently decide to work in either downlink mode or uplink mode as needed without causing performance degradation of IA.

We consider a physical-layer network coding (PNC) scheme based on M-ary continuous phase frequency shift keying (M-CPFSK) modulation for a bidirectional relay network. In this scheme, the maximum-likelihood sequence detection (MLSD) algorithm for the relay receiver over Rayleigh fading channels is discussed. Moreover, an upper bound on the minimum Euclidean distance for the superimposed signals is analyzed and the corresponding lower bound for the average symbol error rate (SER) at the relay is derived. Numerical results are also sustained by simulations which corroborate the exactness of the theoretical analysis.

In this letter, we present a new expression of ergodic capacity for two-wave with diffuse power (TWDP) fading channels. The derived formula is relatively concise and consists of well-known functions even in infinite series form. Especially, the truncated approximate expression and asymptotic formula are also presented, which enable us to obtain useful and physical insights on the effect of TWDP fading on the ergodic capacity for various fading conditions.

This paper proposes a lossy image coding method for still images. In this method, recursive and non-recursive type intra prediction techniques are adaptively selected on a block-by-block basis. The recursive-type intra prediction technique applies a linear predictor to each pel within a prediction block in a recursive manner, and thus typically produces smooth image values. In this paper, the non-recursive type intra prediction technique is extended from the angular prediction technique adopted in the H.265/HEVC video coding standard to enable interpolative prediction to the maximum possible extent. The experimental results indicate that the proposed method achieves better coding performance than the conventional method that only uses the recursive-type prediction technique.

An accurate but energy-efficient estimation of a position is important as the number of mobile computing systems grow rapidly. A challenge is to develop a highly accurate but energy efficient estimation method. A particle filter is a key algorithm to estimate and track the position of an object which exhibits non-linear movement behavior. However, it requires high usage of computation resources and energy. In this paper, we propose a scheme which can dynamically adjust the number of particles according to the accuracy of the reference signal for positioning and reduce the energy consumption by 37% on Cortex A7.

The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.

One of the most promising compression methods for XML documents is the one that translates a given document to a tree grammar that generates it. A feature of this compression is that the internal structures are kept in production rules of the grammar. This enables us to directly manipulate the tree structure without decompression. However, previous studies assume that a given XML document does not have data values because they focus on direct retrieval and manipulation of the tree structure. This paper proposes a direct update method for XML documents with data values and shows the effectiveness of the proposed method based on experiments conducted on our implemented tool.

We have previously introduced the static dependency pair method that proves termination by analyzing the static recursive structure of various extensions of term rewriting systems for handling higher-order functions. The key is to succeed with the formalization of recursive structures based on the notion of strong computability, which is introduced for the termination of typed λ-calculi. To bring the static dependency pair method close to existing functional programs, we also extend the method to term rewriting models in which functional abstractions with patterns are permitted. Since the static dependency pair method is not sound in general, we formulate a class; namely, accessibility, in which the method works well. The static dependency pair method is a very natural reasoning; therefore, our extension differs only slightly from previous results. On the other hand, a soundness proof is dramatically difficult.

The traditional tests are planned and designed at the early stages, but it is possible to execute test cases after implementing source code. Since there is a time difference between design stage and testing stage, by the time a software design error is found it will be too late. To solve this problem, this paper suggests a virtual pre-testing process. While the virtual pre-testing process can find software and testing errors before the developing stage, it can automatically generate and execute test cases with modeling and simulation (M&S) in a virtual environment. The first part of this method is to create test cases with state transition tree based on state diagram, which include state, transition, instruction pair, and all path coverage. The second part is to model and simulate a virtual target, which then pre-test the target with test cases. In other words, these generated test cases are automatically transformed into the event list. This simultaneously executes test cases to the simulated target within a virtual environment. As a result, it is possible to find the design and test error at the early stages of the development cycle and in turn can reduce development time and cost as much as possible.

Automatically recognizing pain and estimating pain intensity is an emerging research area that has promising applications in the medical and healthcare field, and this task possesses a crucial role in the diagnosis and treatment of patients who have limited ability to communicate verbally and remains a challenge in pattern recognition. Recently, deep learning has achieved impressive results in many domains. However, deep architectures require a significant amount of labeled data for training, and they may fail to outperform conventional handcrafted features due to insufficient data, which is also the problem faced by pain detection. Furthermore, the latest studies show that handcrafted features may provide complementary information to deep-learned features; hence, combining these features may result in improved performance. Motived by the above considerations, in this paper, we propose an innovative method based on the combination of deep spatiotemporal and handcrafted features for pain intensity estimation. We use C3D, a deep 3-dimensional convolutional network that takes a continuous sequence of video frames as input, to extract spatiotemporal facial features. C3D models the appearance and motion of videos simultaneously. For handcrafted features, we propose extracting the geometric information by computing the distance between normalized facial landmarks per frame and the ones of the mean face shape, and we extract the appearance information using the histogram of oriented gradients (HOG) features around normalized facial landmarks per frame. Two levels of SVRs are trained using spatiotemporal, geometric and appearance features to obtain estimation results. We tested our proposed method on the UNBC-McMaster shoulder pain expression archive database and obtained experimental results that outperform the current state-of-the-art.

Synthetic aperture radar (SAR) image classification is a popular yet challenging research topic in the field of SAR image interpretation. This paper presents a new classification method based on extreme learning machine (ELM) and the superpixel-guided composite kernels (SGCK). By introducing the generalized likelihood ratio (GLR) similarity, a modified simple linear iterative clustering (SLIC) algorithm is firstly developed to generate superpixel for SAR image. Instead of using a fixed-size region, the shape-adaptive superpixel is used to exploit the spatial information, which is effective to classify the pixels in the detailed and near-edge regions. Following the framework of composite kernels, the SGCK is constructed base on the spatial information and backscatter intensity information. Finally, the SGCK is incorporated an ELM classifier. Experimental results on both simulated SAR image and real SAR image demonstrate that the proposed framework is superior to some traditional classification methods.

In this paper, we propose a highly accurate method for estimating the quality of images compressed using fractal image compression. Using an iterated function system, fractal image compression compresses images by exploiting their self-similarity, thereby achieving high levels of performance; however, we cannot always use fractal image compression as a standard compression technique because some compressed images are of low quality. Generally, sufficient time is required for encoding and decoding an image before it can be determined whether the compressed image is of low quality or not. Therefore, in our previous study, we proposed a method to estimate the quality of images compressed using fractal image compression. Our previous method estimated the quality using image features of a given image without actually encoding and decoding the image, thereby providing an estimate rather quickly; however, estimation accuracy was not entirely sufficient. Therefore, in this paper, we extend our previously proposed method for improving estimation accuracy. Our improved method adopts a new image feature, namely lacunarity. Results of simulation showed that the proposed method achieves higher levels of accuracy than those of our previous method.

Distributed Reflective Denial of Services (DRDoS) attacks have gained huge popularity and become a major factor in a number of massive cyber-attacks. Usually, the attackers launch this kind of attack with small volume of requests to generate a large volume of attack traffic aiming at the victim by using IP spoofing from legitimate hosts. There have been several approaches, such as static threshold based approach and confirmation-based approach, focusing on DRDoS attack detection at victim's side. However, these approaches have significant disadvantages: (1) they are only passive defences after the attack and (2) it is hard to trace back the attackers. To address this problem, considerable attention has been paid to the study of detecting DRDoS attack at source side. Because the existing proposals following this direction are supposed to be ineffective to deal with small volume of attack traffic, there is still a room for improvement. In this paper, we propose a novel method to detect DRDoS attack request traffic on SDN(Software Defined Network)-enabled gateways in the source side of attack traffic. Our method adjusts the sampling rate and provides a traffic-aware adaptive threshold along with the margin based on analysing observed traffic behind gateways. Experimental results show that the proposed method is a promising solution to detect DRDoS attack request in the source side.

A novel adaptive code division multiplexing system with hybrid electrical and optical codes is proposed for flexible and dynamic resource allocation in next generation asynchronous optical access networks. We analyze the performance of a 10Gbps × 12 optical node unit, using hierarchical 8-level optical and 4-level electrical phase shift keying codes.

The 1-bit band-pass delta-sigma modulator (BP-DSM) achieves high resolution if it uses an oversampling technique. This method can generate concurrent dual-band RF signals from a digitally modulated signal using a 1-bit digital pulse train. It was previously reported that the adjacent channel leakage ratio (ACLR) deteriorates owing to the asymmetrical waveform created by the pulse transition mismatch error of the rising and falling waveforms in the time domain and that the ACLR can be improved by distortion compensation. However, the reported distortion compensation method can only be performed for single-band transmission, and it fails to support multi-band transmission because the asymmetrical waveform compensated signal extends over a wide frequency range and is itself a harmful distortion outside the target band. Unfortunately, the increase of out-of-band power causes the BP-DSM unstable. We therefore propose a distortion compensator for a concurrent dual-band 1-bit BP-DSM that consists of a noise transfer function with a quasi-elliptic filter that can control the out-of-band gain frequency response against out-of-band oscillation. We demonstrate that dual-band LTE signals, each with 40MHz (2×20MHz) bandwidth, at 1.5 and 3.0GHz, can be compensated concurrently for spurious distortion under various combinations of rising and falling times and ACLR of up to 48dB, each with 120MHz bandwidth, including the double sided adjacent channels and next adjacent channels, is achieved.

In this paper, we propose the first identity-based dynamic multi-cast key distribution (ID-DMKD) protocol which is secure against maximum exposure of secret information (e.g., secret keys and session-specific randomness). In DMKD protocols, users share a common session key without revealing any information of the session key to the semi-honest server, and can join/leave to/from the group at any time even after establishing the session key. Most of the known DMKD protocols are insecure if some secret information is exposed. Recently, an exposure resilient DMKD protocol was introduced, however, each user must manage his/her certificate by using the public-key infrastructure. We solve this problem by constructing the DMKD protocol authenticated by user's ID (i.e., without certificate). We introduce a formal security definition for ID-DMKD by extending the previous definition for DMKD. We must carefully consider exposure of the server's static secret key in the ID-DMKD setting because exposure of the server's static secret key causes exposure of all users' static secret keys. We prove that our protocol is secure in our security model in the standard model. Another advantage of our protocol is scalability: communication and computation costs of each user are independent from the number of users. Furthermore, we show how to extend our protocol to achieve non-interactive join by using certificateless encryption. Such an extension is useful in applications that the group members frequently change like group chat services.