A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l0-norm minimization by l1-norm minimization, our method designs a reweighted l1 norm to substitute the l0 norm. The capability of the reweighted l1 norm to bridge the gap between the l0- and l1-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l1-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

We propose a novel algorithm for the recovery of non-sparse, but compressible signals from linear undersampled measurements. The algorithm proposed in this paper consists of two steps. The first step recovers the signal by the l1-norm minimization. Then, the second step decomposes the l1 reconstruction into major and minor components. By using the major components, measurements for the minor components of the target signal are estimated. The minor components are further estimated using the estimated measurements exploiting a maximum a posterior (MAP) estimation, which leads to a ridge regression with the regularization parameter determined using the error bound for the estimated measurements. After a slight modification to the major components, the final estimate is obtained by combining the two estimates. Computational cost of the proposed algorithm is mostly the same as the l1-nom minimization. Simulation results for one-dimensional computer generated signals show that the proposed algorithm gives 11.8% better results on average than the l1-norm minimization and the lasso estimator. Simulations using standard images also show that the proposed algorithm outperforms those conventional methods.

In the log-likelihood ratio (LLR) domain, the belief propagation (BP) decoding algorithm for polar codes incurs high computation complexity due to the computation of the hyperbolic functions in the node update rules. In this paper, we propose a linear approximation method based on the principle of equal spacing to simplify the hyperbolic functions in the BP decoding algorithm. Our method replaces the computation of hyperbolic functions with addition and multiplication operations in the node update rules. Simulation results show that the performance of the modified BP decoding algorithm is almost the same as the original BP decoding algorithm in the low Signal to Noise Ratio (SNR) region, and in the high SNR region the performance of our method is slightly worse. The modified BP decoding algorithm is only implemented with addition and multiplication operations, which greatly reduces computation complexity, and simplifies hardware implementation.

The sparse Fourier transform (SFT) seeks to recover k non-negligible Fourier coefficients from a k-sparse signal of length N (k«N). A single frequency signal can be recovered via the Chinese remainder theorem (CRT) with sub-sampled discrete Fourier transforms (DFTs). However, when there are multiple non-negligible coefficients, more of them may collide, and multiple stages of sub-sampled DFTs are needed to deal with such collisions. In this paper, we propose a combinatorial aliasing-based SFT (CASFT) algorithm that is robust to noise and greatly reduces the number of stages by iteratively recovering coefficients. First, CASFT detects collisions and recovers coefficients via the CRT in a single stage. These coefficients are then subtracted from each stage, and the process iterates through the other stages. With a computational complexity of O(klog klog 2N) and sample complexity of O(klog 2N), CASFT is a novel and efficient SFT algorithm.

A novel multipath mitigation algorithm for binary offset carrier (BOC) signals in the global navigation satellite system (GNSS) is presented. Based on the W2 code correlation reference waveform (CCRW) structure, a series of bipolar reference waveforms (BRWs) is introduced to shape the unambiguous s-curve. The resulted s-curve has a single stable zero-crossing point such that the tracking unambiguity in BOC (1,1) can be solved. At the same time, multipath mitigation capability is improved as well. As verified by simulations, the proposed method matches the multipath mitigation performance of W2 CCRW, and is superior to conventional receiver correlation techniques. This method can be applied in GPS L1 and Galileo E1.

Wireless sensor networks play an important role in several industries. Ad-hoc networks with multi-hop transmissions are considered suitable for wireless sensor networks because of their high scalability and low construction cost. However, a lack of centralized control makes it difficult to respond to congestion when system capacity is exceeded. Therefore, the analysis of system capacity is a critical issue for system design. In this paper, we propose a novel zone division model to analyze the capacity of multi-hop wireless sensor networks using carrier sense multiple access with collision avoidance protocols. We divide the one-hop area to a gateway (GW) into two zones: an outer zone, where access nodes (ANs) can relay packets from multi-hop ANs, and an inner zone where ANs cannot relay packets. Using this approach, we calculate the packet loss for each zone to estimate the capacity, considering the difference in the communication range of the GW and ANs, as well as the collision with hidden nodes. Comparisons with simulation results and the conventional method show that our model achieves higher estimation accuracy.

Copper arc runners are fixed on silver electrical contacts. Break arcs are generated between the contacts in a DC resistive circuit. Circuit current when contacts are closed is 10A. Supply voltage is changed from 200V to 450V. The following results are shown. Cathode spots stay on the cathode surface but anode spots run on the runner when the supply voltage is 250V and over. In cases of the supply voltage is greater than 250V, the break arcs run on the runner when the arcs are successfully extinguished, and stays on the runner in cases of the failure of arc extinction. The arc lengths just before arc extinction with or without the runners are also investigated. The arc lengths are the same with or without the runners for each supply voltage.

Multiple subcarrier passive communication is a new research area which enables a type of frequency division multiple access with wireless and batteryless sensor RF tags just by implementing RF switches to produce dedicated subcarriers. Since the mutual interference among subcarriers is unevenly distributed over the frequency band, careless allocations of subcarrier frequencies may result in degraded network performance and inefficient use of the frequency resource. In this paper, we examine four subcarrier frequency allocation schemes using MATLAB numerical simulations. The four schemes are evaluated in terms of the communication capacity and access fairness among sensor RF tags. We found that the subcarrier allocation scheme plays an important role in multiple subcarrier communication and can improves the communication capacity by 35%.

Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest determines classifications using the majority of multiple trees; so many decision trees must be built. Performance increases with the number of decision trees, requiring memory, and decreases if the number of decision trees is decreased. Because of this, the algorithm is not well suited to implementation on small-scale hardware as an embedded system. As such, we have proposed Boosted Random Forest, which introduces a boosting algorithm into the Random Forest learning method to produce high-performance decision trees that are smaller. When evaluated using databases from the UCI Machine learning Repository, Boosted Random Forest achieved performance as good or better than ordinary Random Forest, while able to reduce memory use by 47%. Thus, it is suitable for implementing Random Forests on embedded hardware with limited memory.

Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.

In this letter, a local pattern coding scheme is proposed to reduce the dimensionality of feature vectors in the local ternary pattern. The proposed method encodes the ternary patterns into a binary pattern by clustering similar ternary patterns. The experimental results show that the proposed method outperforms the previous methods.

Open-loop (OL) transmit diversity is more subject to the influence of channel estimation error than closed-loop (CL) transmit diversity, although it has the merit of providing better performance in fast Doppler frequency environments because it doesn't require a feedback signal. This paper proposes an OL transmit diversity scheme combined with intra-subframe frequency hopping (FH) and iterative decision-feedback channel estimation (DFCE) in a shared channel for discrete Fourier transform (DFT)-precoded orthogonal frequency division multiple access (OFDMA). We apply intra-subframe FH to OL transmit diversity to mitigate the reduction in the diversity gain under high fading correlation conditions among antennas and iterative DFCE to improve the channel estimation accuracy. Computer simulation results show that the required average received signal-to-noise power ratio at the average block error rate (BLER) of 10-2 of the space-time block code (STBC) with intra-subframe FH is reduced to within approximately 0.8dB compared to codebook-based CL transmit diversity when using iterative DFCE at the maximum Doppler frequency of fD =5.55Hz. Moreover, it is shown that STBC with intra-subframe FH and iterative DFCE achieves much better BLER performance compared to CL transmit diversity when fD is higher than approximately 30Hz since the tracking ability of the latter degrades due to the fast fading variation in its feedback loop.

In this paper, a specified hardware architecture of the Fast Mode Decision (FMD) algorithms presented by our previous work is proposed. This architecture is designed as an embedded mode dispatch module. On the basis of this module, some unnecessary modes can be skipped or the mode decision process can be terminated in advanced. In order to maintain a higher compatibility, the FMD algorithms are unitedly designed as an unique module that can be easily embedded into a common video codec for H.265/HEVC. The input and output interfaces between the proposed module and other parts of the codec are designed based on simple but effective protocol. Hardware synthesis results on FPGA demonstrate that the proposed architecture achieves a maximum frequency of about 193 MHz with less than 1% of the total resources consumed. Moreover, the proposed module can improve the overall throughput.

The complexity of the graph isomorphism problem for trapezoid graphs has been open over a decade. This paper shows that the problem is GI-complete. More precisely, we show that the graph isomorphism problem is GI-complete for comparability graphs of partially ordered sets with interval dimension 2 and height 3. In contrast, the problem is known to be solvable in polynomial time for comparability graphs of partially ordered sets with interval dimension at most 2 and height at most 2.

Throughput capacity is of great importance for the design and performance optimization of mobile ad hoc networks (MANETs). We study the exact per node throughput capacity of MANETs under a general 2HR-(g, x, f) routing scheme which combines erasure coding and packet replication techniques. Under this scheme, a source node first encodes a group of g packets into x (x ≥ g) distinct coded packets, and then replicates each of the coded packets to at most f relay nodes which help to forward them to the destination node. All original packets can be recovered once the destination node receives any g distinct coded packets of the group. To study the throughput capacity, we first construct two absorbing Markov chain models to depict the complicated packet delivery process under the routing scheme. Based on these Markov models, an analytical expression of the throughput capacity is derived. Extensive simulation and numerical results are provided to verify the accuracy of theoretical results on throughput capacity and to illustrate how system parameters will affect the throughput capacity in MANETs. Interestingly, we find that the replication of coded packets can improve the throughput capacity when the parameter x is relatively small.

The MPEG-1 layer-III compressed audio format, which is widely known as MP3, is the most popular for audio distribution. However, it is not equipped with security features to protect the content from unauthorized access. Although encryption ensures content security, the naive method of encrypting the entire MP3 file would destroy compliance with the MPEG standard. In this paper, we propose a low-complexity partial encryption method that is embedded during the MP3 encoding process. Our method reduces time consumption by encrypting only the perceptually important parts of an MP3 file rather than the whole file, and the resulting encrypted file is still compatible with the MPEG standard so as to be rendered by any existing MP3 players. For full-quality rendering, decryption using the appropriate cryptographic key is necessary. Moreover, the effect of encryption on audio quality can be flexibly controlled by adjusting the percentage of encryption. On the basis of this feature, we can realize the try-before-purchase model, which is one of the important business models of Digital Rights Management (DRM): users can render encrypted MP3 files for trial and enjoy the contents in original quality by purchasing decryption keys. From our experiments, it turns out that encrypting 2-10% of MP3 data suffices to generate trial music, and furthermore file size increasing after encryption is subtle.

With shortest path bridging MAC (SPBM), shortest path trees are computed based on link metrics from each node to all other participating nodes. When an edge bridge receives a frame, it selects a path along which to forward the frame to its destination node from multiple shortest paths. Blocking ports are eliminated to allow full use of the network links. This approach is expected to use network resources efficiently and to simplify the operating procedure. However, there is only one multipath distribution point in the SPBM network. This type of network can be defined as an end-to-end multipath network. Edge bridges need to split flows to achieve the load balancing of the entire network. This paper proposes a rate-based path selection scheme that can be employed for end-to-end multipath networks including SPBM. The proposed scheme assumes that a path with a low average rate will be congested because the TCP flow rates decrease on a congested path. When a new flow arrives at an edge bridge, it selects the path with the highest average rate since this should provide the new flow with the highest rate. The performance of the proposed scheme is confirmed by computer simulations. The appropriate timeout value is estimated from the expected round trip time (RTT). If an appropriate timeout value is used, the proposed scheme can realize good load balancing. The proposed scheme improves the efficiency of link utilization and throughput fairness. The performance is not affected by differences in the RTT or traffic congestion outside the SPBM network.

This paper presents a particle swarm optimization network (PSON) to improve the search capability of PSO. In PSON, multi-PSOs are connected for the purpose of communication. A variety of network topology can be realized by varying the number of connected PSOs of each PSO. The solving performance and convergence speed can be controlled by changing the network topology. Furthermore, high parallelism is can be realized by assigning PSO to single processor. The stability condition analysis and performance of PSON are shown.

Compressive sensing (CS)-based channel estimation considerably reduces pilot symbols usage by exploiting the sparsity of the propagation channel in the delay-Doppler domain. In this paper, we consider the application of Bayesian approaches to the sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Taking advantage of the block-sparse structure and statistical properties of time-frequency selective channels, the proposed Bayesian method provides a more efficient and accurate estimation of the channel status information (CSI) than do conventional CS-based methods. Moreover, our estimation scheme is not limited to the Gaussian scenario but is also available for channels that have non-Gaussian priors or unknown probability density functions. This characteristic is notably useful when the prior statistics of channel coefficients cannot be precisely estimated. We also design a combo pilot pattern to improve the performance of the proposed estimation scheme. Simulation results demonstrate that our method performs well at high Doppler frequencies.