Protection of the licensed user (LU) and utilization of the spectrum are the most important goals in cognitive radio networks. To achieve the first goal, a cognitive user (CU) is required to sense for a longer time period, but this adversely affects the second goal, i.e., throughput or utilization of the network, because of the reduced time left for transmission in a time slot. This tradeoff can be controlled by simultaneous sensing and data transmission for the whole frame duration. However, increasing the sensing time to the frame duration consumes more energy. We propose a new frame structure in this paper, in which transmission is done for the whole frame duration whereas sensing is performed only until the required detection probability is satisfied. This means the CU is not required to perform sensing for the whole frame duration, and thus, conserves some energy by sensing for a smaller duration. With the proposed frame structure, throughput of all the CUs is estimated for the frame and, based on the estimated throughput and consumed energy in sensing and transmission, the energy efficient pair of CUs (transmitter and receiver) that maximizes system throughput by consuming less energy, is selected for a time slot. The selected CUs transmits data for the whole time slot, whereas sensing is performed only for certain duration. The performance improvement of the proposed scheme is demonstrated through simulations by comparing it with existing schemes.

Building detection from high resolution remote sensing images is challenging due to the high intraclass variability and the difficulty in describing buildings. To address the above difficulties, a novel approach is proposed based on the combination of shape-specific feature extraction and discriminative feature classification. Shape-specific feature can capture complex shapes and structures of buildings. Discriminative feature classification is effective in reflecting similarities among buildings and differences between buildings and backgrounds. Experiments demonstrate the effectiveness of the proposed approach.

Non-orthogonal multiple access (NOMA) makes multiple mobile users share the same frequency band. In a conventional NOMA scheme, a user pair that can be assigned to the same frequency resource is limited, which reduces the amount of capacity improvement possible. This is because a far user demodulates a signal without canceling an underlaid signal for a near user. In addition, semi-orthogonal multiple access (SOMA) modulation has been proposed. This modulation scheme helps to reduce scheduling complexity and demodulation complexity. In this paper, a joint detection scheme is applied to a far user as well as a near user in a NOMA downlink. The joint detection in the far user leads to a more number of user pairs that can be assigned to the same frequency resource through proportional fair scheduling. The total system throughput performance with the joint detection is evaluated with multi-cell system level simulation. Numerical results show that the joint detection in the original NOMA system increases the system throughput more effectively than that with SOMA modulation.

Rapid process scaling and the introduction of the multilevel cell (MLC) concept have lowered costs of NAND Flash memories, but also degraded reliability. For this reason, the memories are depending on strong error correcting codes (ECCs), and this has enabled the memories to be used in wide range of storage applications, including solid-state drives (SSDs). Meanwhile, too strong error correcting capability requires excessive decoding complexity and check bits. In NAND Flash memories, cell errors to neighborhood voltage levels are more probable than those to distant levels. Several ECCs reflecting this characteristics, including limited-magnitude ECCs which correct only errors with a certain limited magnitude and low-density parity check (LDPC) codes, have been proposed. However, as most of these ECCs need the multiple bits in a cell for encoding, they cannot be used with multipage programing, a high speed programming method currently employed in the memories. Also, binary ECCs with Gray codes are no longer optimal when multilevel voltage shifts (MVSs) occur. In this paper, an error correction method reflecting the error characteristic is presented. This method detects errors by a binary ECC as a conventional manner, but a nonbinary value or whole the bits in a cell, are subjected to error correction, so as to be corrected into the most probable neighborhood value. The amount of bit error rate (BER) improvement is depending on the probability of the each error magnitude. In case of 2bit/cell, if only errors of magnitude 1 and 2 can occur and the latter occupies 5% of cell errors, acceptable BER is improved by 4%. This is corresponding to extending 2.4% of endurance. This method needs about 15% longer average latency, 19% longer maximum latency, and 15% lower throughput. However, with using the conventional method until the memories' lifetime number of program/erase cycling, and the proposed method after that, BER improvement can be utilized for extending endurance without latency and throughput degradation until the switch of the methods.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel and pipeline processing. We show results for eight real-world biological datasets. We compare execution times against our previous hardware approach, and TNT, the fastest available parsimony program, which is also accelerated by the Indirect Calculation of Tree Lengths method. Acceleration rates between 34 to 45 per rearrangement, and 2 to 6 for the whole search, are obtained against our previous hardware approach. Acceleration rates between 2 to 36 per rearrangement, and 18 to 112 for the whole search, are obtained against TNT.

Object detection is the first step in the object recognition. According to the detection results, its following works are affected. However, object detection has a heavy resource requirement in terms of, computing power and memory. If an image is enlarged, the computational load required for object detection is also increased. An-integral-image-based method guarantees fast object detection. Once an integral image is generated, the speed of the object detection procedure remains fixed, regardless of the pattern region size. However, this becomes an even greater issue if the image is enlarged. In this paper, we propose the use of directional integral image based object detection. A directional integral image gives direction to an integral image, which can then be calculated from various directions. Furthermore, many unnecessary calculations, which typically occur when a partial integral image is used for object detection, can be avoided. Therefore, the amount of computation is reduced, compared with methods using integral images. In experiments comparing methods, the proposed method required 40% fewer computations.

Malware proliferation has become a serious threat to the Internet in recent years. Most current malware are subspecies of existing malware that have been automatically generated by illegal tools. To conduct an efficient analysis of malware, estimating their functions in advance is effective when we give priority to analyze malware. However, estimating the malware functions has been difficult due to the increasing sophistication of malware. Actually, the previous researches do not estimate the functions of malware sufficiently. In this paper, we propose a new method which estimates the functions of unknown malware from APIs or categories observed by dynamic analysis on a host. We examine whether the proposed method can correctly estimate the malware functions by the supervised machine learning techniques. The results show that our new method can estimate the malware functions with the average accuracy of 83.4% using API information.

This letter proposes a Light Space Partitioned Shadow Maps (LSPSMs) algorithm which implements shadow rendering based on a novel partitioning scheme in light space. In stead of splitting the view frustum like traditional Z-partitioning methods, we split partitions from the projection of refined view frustum in light space. The partitioning scheme is performed dual-directionally while limiting the wasted space. Partitions are created in dynamic number corresponding to the light and view directions. Experiments demonstrate that high quality shadows can be rendered in high efficiency with our algorithm.

This paper proposes novel simplified maximum likelihood detection for XOR physical layer network coding (XOR-PNC) in bi-directional wireless relay systems with Quaternary phase shift keying (QPSK). The proposed detection applies unitary precoding to achieve superior performance without computationally prohibitive exhaustive search. The performance of the XOR employing the proposed simplified MLD with the precoding is analyzed in relay systems with orthogonal frequency division multiplexing (OFDM). The performance of the XOR-PNC with the proposed techniques is also evaluated by computer simulation. The XOR-PNC with the proposed techniques achieves about 7dB better performance than the amplify-and-forward physical layer network coding in the 5-path fading channel at BER=10-4. It is also shown that the XOR-PNC with the proposed techniques achieves better performance than that without precoding.

The diffraction by a thin material strip is analyzed for the H-polarized plane wave incidence using the Wiener-Hopf technique together with approximate boundary conditions. An asymptotic solution is obtained for the case where the thickness and the width of the strip are small and large compared with the wavelength, respectively. The scattered field is evaluated asymptotically based on the saddle point method and a far field expression is derived. Scattering characteristics are discussed in detail via numerical results of the radar cross section.

We propose a novel improved characteristic basis function method (IP-CBFM) for accurately analysing the radar cross section (RCS). This new IP-CBFM incorporates the effect of higher-order multiple scattering and has major influences in analyzing monostatic RCS (MRCS) of single incidence and bistatic RCS (BRCS) problems. We calculated the RCS of two scatterers and could confirm that the proposed IP-CBFM provided higher accuracy than the conventional method while significantly reducing the number of CBF.

This paper presents a prototype of a 3D imaging step-frequency radar system at 10-20GHz suitable for the nondestructive inspection of the walls of wooden houses. Using this prototype, it is possible to obtain data for 3D imaging with a single simple scan and make 3D volume images of braces — broken or not — in the walls of wooden houses using synthetic aperture radar processing. The system is a multistatic radar composed of a one-dimensional array antenna (32 transmitting and 32 receiving antennas, which are resistively loaded printed bowtie antennas) and is able to acquire frequency domain data for all the transmitting and receiving antenna pairs, i.e., 32×32=1024 pairs, in 33ms per position. On the basis of comparisons between two array antenna prototype designs, we investigated the optimal distance between a transmitting array and a receiving array to reduce the direct coupling effect. We produced a prototype multistatic radar system and used it to measure different types of wooden targets in two experiments. In the first experiment, we measured plywood bars behind a decorated gypsum board, simulating a broken wooden brace inside a house wall. In the second experiment, we measured a wooden brace made of Japanese cypress as a target inside a model of a typical (wooden) Japanese house wall. The results of both experiments demonstrate the imaging capability of the radar prototype for nondestructive inspection of the insides of wooden house walls.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

This paper deals with the diffraction of a monochromatic plane wave by a periodic grating. We discuss a problem how to obtain a numerical diffraction efficiency (NDE) satisfying the reciprocity theorem for diffraction efficiencies, because diffraction efficiencies are the subject of the diffraction theories. First, this paper introduces a new formula that decomposes an NDE into two components: the even component and the odd one. The former satisfies the reciprocity theorem for diffraction efficiencies, but the latter does not. Therefore, the even component of an NDE becomes an answer to our problem. On the other hand, the odd component of an NDE represents an unwanted error. Using such the decomposition formula, we then obtain another new formula that decomposes the conventional energy error into two components. One is the energy error made by even components of NDE's. The other is the energy error constructed by unwanted odd ones and it may be used as a reciprocity criterion of a numerical solution. This decomposition formula shows a drawback of the conventional energy balance. The total energy error is newly introduced as a more strict condition for a desirable solution. We point out theoretically that the reciprocal wave solution, an approximate solution satisfying the reciprocity for wave fields, gives another solution to our problem. Numerical examples are given for the diffraction of a TM plane wave by a very rough periodic surface with perfect conductivity. In the case of a numerical solution by the image integral equation of the second kind, we found that the energy error is much reduced by use of the even component of an NDE as an approximate diffraction efficiency or by use of a reciprocal wave solution.

An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.

In ad hoc networks, broadcast forwarding protocols called OR (opportunistic routing) have been proposed to gain path diversity for higher packet delivery rates and shorter end-to-end delays. In general backoff-based OR protocols, each receiver autonomously makes a forwarding decision by using certain metrics to determine if a random backoff time is to be applied. However, each forwarder candidate must wait for the expiration of the backoff timer before forwarding a packet. Moreover, they cannot gain path diversity if the forwarding path includes local sparse areas, and this degrades performance as it strongly depends on the terminal density. In this paper, we propose a novel OR protocol called PRIOR (prioritized forwarding for opportunistic routing). In PRIOR, a terminal, called a prioritized forwarder and which forwards packets without using a backoff time, is selected from among the neighbours. In addition, PRIOR uses lightweight hop-by-hop retransmission control to mitigate the effect of terminal density. Moreover, we introduce an enhancement to PRIOR to reduce unnecessary forwarding by using an explicit acknowledgement. We evaluate PRIOR in comparison with conventional protocols in computer simulations.

Case studies have reported that pedestrian detection methods using vehicle radar are not complete systems because each system has specific limitations at the cost of the calculating amounts, the system complexity or the range resolution. In this letter, we proposed a novel pedestrian detection method by template matching using Gabor filter bank, which was evaluated based on the data observed by 24GHz UWB radar.

We study a use of Gaussian kernels with a wide range of scales for nonlinear function estimation. The estimation task can then be split into two sub-tasks: (i) model selection and (ii) learning (parameter estimation) under the selected model. We propose a fully-adaptive and all-in-one scheme that jointly carries out the two sub-tasks based on the multikernel adaptive filtering framework. The task is cast as an asymptotic minimization problem of an instantaneous fidelity function penalized by two types of block l1-norm regularizers. Those regularizers enhance the sparsity of the solution in two different block structures, leading to efficient model selection and dictionary refinement. The adaptive generalized forward-backward splitting method is derived to deal with the asymptotic minimization problem. Numerical examples show that the scheme achieves the model selection and learning simultaneously, and demonstrate its striking advantages over the multiple kernel learning (MKL) method called SimpleMKL.