Given a tree T with edge lengths and edge weights, and a value B, the length-constrained heaviest path problem is to find a path in T with maximum path weight whose path length is at most B. We present a linear time algorithm for the problem when the edge lengths are uniform, i.e., all one. This algorithm with slight modification can be used to find the heaviest path of length exactly B in T in linear time.

The reaction path from acetyl acetone (pentane-2,4-dione) to lutidine derivative is calculated at the HF/3-21G + ZPC level(ZPC=zero point energy correction), and MP2/6-31G(d,p) + ZPC level. As a model for porous glass, H2Si=O and (OH)2Si=O make chemical bonds or strong complex with FLUORAL-P that decrease the activation energy of a H2O elimination reaction.

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method.

We examine the relationship between 116 VHF sensor events recorded by the VHF sensor on the Maido-1 satellite and lightning strokes detected by the World Wide Lightning Location Network (WWLLN) to show that most of the VHF sensor events were caused by lightning discharges. For each VHF sensor event, the WWLLN events within 1400 km from the subsatellite point and within 1 sec, 30 sec, and 300 sec of the VHF sensor trigger time are analyzed. We find that the coincidence rates in the North and South American continents, and in Southeast/East Asia and the Australian continent are greater than 0.90. Those in the African and European continents, and in the Pacific and Atlantic Oceans are less than 0.61. These high enough coincidence rates indicate that the VHF sensor events were emitted from lightning, although the coincidence rates in the other regions are quite low because of the low detection efficiency of the WWLLN in the regions. We also focus on 6 coincident events measured by both the VHF sensor and the WWLLN. The incidence angles of the EM waves for the VHF sensor are estimated from the group delay characteristics of the recorded EM waveforms. Compared with the WWLLN lightning locations, the two incidence angles are temporally and spatially coincident. These results indicate that a large fraction of the VHF sensor events are emitted by lightning discharges.

The GPS trajectory databases serve as bases for many intelligent applications that need to extract some trajectories for future processing or mining. When doing such tasks, spatio-temporal range queries based methods, which find all sub-trajectories within the given spatial extent and time interval, are commonly used. However, the history trajectory indexes of such methods suffer from two problems. First, temporal and spatial factors are not considered simutaneously, resulting in low performance when processing spatio-temporal queries. Second, the efficiency of indexes is sensitive to query size. The query performance changes dramatically as the query size changed. This paper proposes workload-aware Adaptive OcTree based Trajectory clustering Index (ATTI) aiming at optimizing trajectory storage and index performance. The contributions are three-folds. First, the distribution and time delay of the trajectory storage are introduced into the cost model of spatio-temporal range query; Second, the distribution of spatial division is dynamically adjusted based on GPS update workload; Third, the query workload adaptive mechanism is proposed based on virtual OcTree forest. A wide range of experiments are carried out over Microsoft GeoLife project dataset, and the results show that query delay of ATTI could be about 50% shorter than that of the nested index.

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.

This paper proposes a novel face recognition approach using a centralized gradient pattern image and image covariance-based facial feature extraction algorithms, i.e. a two-dimensional principal component analysis and an alternative two-dimensional principal component analysis. The centralized gradient pattern image is obtained by AND operation of a modified center-symmetric local binary pattern image and a modified local directional pattern image, and it is then utilized as input image for the facial feature extraction based on image covariance. To verify the proposed face recognition method, the performance evaluation was carried out using various recognition algorithms on the Yale B, the extended Yale B and the CMU-PIE illumination databases. From the experimental results, the proposed method showed the best recognition accuracy compared to different approaches, and we confirmed that the proposed approach is robust to illumination variation.

PCA-L1 (principal component analysis based on L1-norm maximization) is an approximate solution of L1-PCA (PCA based on the L1-norm), and has robustness against outliers compared with traditional PCA. However, the more dimensions the feature space has, the more calculation time PCA-L1 consumes. This paper focuses on an initialization procedure of PCA-L1 algorithm, and proposes a fast method of PCA-L1 using Gram-Schmidt orthogonalization. Experimental results on face recognition show that the proposed method works faster than conventional PCA-L1 without decrease of recognition accuracy.

In this paper, we investigate a relationship between the length-decreasing self-reducibility and the many-one-like reducibilities for partial multivalued functions. We show that if any parsimonious (many-one or metric many-one) complete function for NPMV (or NPMVg) is length-decreasing self-reducible, then any function in NPMV (or NPMVg) has a polynomial-time computable refinement. This result implies that there exists an NPMV (or NPMVg)-complete function which is not length-decreasing self-reducible unless P = NP.

In this paper, we investigate the impact of different sub-band spreading bandwidth (SSBW) on a direct sequence (DS) multiband (MB) ultra wideband (UWB) system in multipath and narrowband interference over realistic UWB channel models based on actual measurements. As an approach to effectively mitigate multipath and narrowband interference, the DS-MB-UWB system employs multiple sub-bands instead of a wide single band for data transmission. By using spreading chips with different duration settings, the SSBW can be manipulated. As a result, it is observed that increasing SSBW does not always improve system performance. Optimum SSBW values exist and are found to vary in accordance to different operating parameters such as the number of sub-bands and types of propagation channel model. Additionally, we have also found that system performance in the presence of narrowband interference is heavily dependent on the number of employed sub-bands.

We investigated oxidation time dependence of graphene oxide employing modified Hummer method by dynamic light scattering. Oxidation reaction proceeded rapidly within about 24 hours, and was saturated. It is suggested that graphene oxides were not able to freely fragment. This implies that the oxidation reactions occur at the limited sites.

To obtain an ultra-short high-intensity pulse source, we investigated the amplification characteristics of two types of pulses (dissipative soliton and stretched pulses) produced by our Yb-doped fiber laser oscillator. Our results show that the dissipative soliton pulse can be amplified with less deterioration than the stretched pulse.

In this paper, an extended best linear unbiased estimator (EBLUE) based on a periodic training sequence is proposed and investigated for frequency offset estimation in orthogonal frequency division multiplexing (OFDM) systems. The structure of EBLUE is general and flexible so it adapts to different complexity constraints, and is attractive in practical implementation. Performance analysis and design strategy of EBLUE are provided to realize the best tradeoff between performance and complexity. Moreover, closed-form results of both weight and performance make EBLUE even more attractive in practical implementation. Both the performance and complexity of EBLUE are compared with other proposals and the Cramer-Rao lower bound (CRLB) to demonstrate the merit of EBLUE.

An 8-bit 100-kS/s successive approximation (SA) analog-to-digital converter (ADC) is proposed for measuring EEG and MEG signals in an 88 point. The architectures of a SA ADC with a single-ended analog input and a split-capacitor-based digital-to-analog converter (SC-DAC) are used to reduce the power consumption and chip area of the entire ADC. The proposed SA ADC uses a time-domain comparator that has an input offset self-calibration circuit. It also includes a serial output interface to support a daisy channel that reduces the number of channels for the multi-point sensor interface. It is designed by using a 0.35-µm 1-poly 6-metal CMOS process with a 3.3 V supply to implement together with a conventional analog circuit such as a low-noise-amplifier. The measured DNL and INL of the SA ADC are +0.63/-0.46 and +0.46/-0.51 LSB, respectively. The SNDR is 48.39 dB for a 1.11 kHz analog input signal at a sampling rate of 100 kS/s. The power consumption and core area are 38.71 µW and 0.059 mm2, respectively.

We extend the Nonparametric Discriminant Analysis (NDA) algorithm to a semi-supervised dimensionality reduction technique, called Semi-supervised Nonparametric Discriminant Analysis (SNDA). SNDA preserves the inherent advantages of NDA, that is, relaxing the Gaussian assumption required for the traditional LDA-based methods. SNDA takes advantage of both the discriminating power provided by the NDA method and the locality-preserving power provided by the manifold learning. Specifically, the labeled data points are used to maximize the separability between different classes and both the labeled and unlabeled data points are used to build a graph incorporating neighborhood information of the data set. Experiments on synthetic as well as real datasets demonstrate the effectiveness of the proposed approach.

In this paper, we propose a new method for wideband spectrum sensing using compressed measurements of the received wideband signal; we can directly separate information of the sub-channels and perform detection in each. Wideband spectrum sensing empowers us to rapidly access the vacant sub-channels in high utilization regime. Regarding the fact that at each time instant some sub-channels are vacant, the received signal is sparse in some bases. Then we could apply the Compressive Sensing (CS) algorithms and take the compressed measurements. On the other hand, the primary user signals in different sub-channels could have different modulation types; therefore, the signal in each sub-channel is chosen among a signal space. Knowing these signal spaces, the secondary user could separate information of different sub-channels employing the compressed measurements. We perform filtering and detection based on these compressed measurements; this decreases the computational complexity of the wideband spectrum sensing. In addition, we model the received wideband signal as a vector which has a block-sparse representation on a basis consisting of all sub-channel bases whose elements occur in clusters. Based on this feature of the received signal, we propose another wideband spectrum sensing method with lower computational complexity. In order to evaluate the performance of the proposed method, we employ the Monte-Carlo simulation. According to simulations if the compression rate is selected appropriately according to the CS theorems and the problem model, the detection performance of our method leads to the performance of the ideal filter bank-based method, which uses the ideal and impractical narrow band filters.

Precoding is an excellent choice for complementing the MIMO systems. Linear precoding techniques offer better performance at low signal-to-noise ratios (SNRs) while non-linear techniques perform better at higher SNRs. In addition, the non-linear techniques can achieve near optimal capacity at the expense of reasonable levels of complexity. However, precoding depends on the knowledge of the wireless channel. Recent work on MIMO systems have shown that channel-knowledge at the transmitter, in either full or partial forms, can increase the channel capacity and system performance considerably. Therefore, hybrid techniques should be deployed in order to obtain a better trade-off in terms of complexity and performance. In this paper, we present a hybrid precoding technique which deals with the condition of partial channel-knowledge while offering robustness against the effects of correlation and poorly scattered channels while at the same time keeping low levels of complexity and high performance.

Probabilistic Packet Marking (PPM) is a scheme for IP traceback where each packet is marked randomly with an IP address of one router on the attack path in order for the victim to trace the source of attacks. In previous work, a network coding approach to PPM (PPM+NC) where each packet is marked with a random linear combination of router IP addresses was introduced to reduce number of packets required to infer the attack path. However, the previous work lacks a formal proof for benefit of network coding to PPM and its proposed scheme is restricted. In this paper, we propose a novel method to prove a strong theorem for benefit of network coding to PPM in the general case, which compares different perspectives (interests of collecting) at the collector in PPM+NC scheme. Then we propose Core PPM+NC schemes based on our core network coding approach to PPM. From experiments, we show that our Core PPM+NC schemes actually require less number of packets than previous schemes to infer the attack path. In addition, based on the relationship between Coupon Collector's Problem (CCP) and PPM, we prove that there exists numerous designs that CCP still benefits from network coding.

In this paper, we propose a high-speed full-duplex optical wireless communication system using a single channel imaging receiver for personal area network applications. This receiver is composed of an imaging lens, a small sensitive-area photodiode, and a 2-aixs actuator and it can reject most of the background light. Compared with the previously proposed system with single wide field-of-view (FOV) non-imaging receiver, the coverage area at 12.5 Gb/s is extended by > 20%. Furthermore, since the rough location information of the user is available in our proposed system, instead of searching for the focused light spot over a large area on the focal plane of the lens, only a small possible area needs to be scanned. In addition, by pre-setting a proper comparison threshold when searching for the focused light spot, the time needed for searching can be further reduced. Proof-of-concept experiments have been carried out and the results show that with this partial searching algorithm and pre-set threshold, better performance is achieved.

In spectrum sharing cognitive radio (CR) networks, secondary user (SU) is allowed to share the same spectrum band concurrently with primary user (PU), with the condition that the SU causes no harmful interference to the PU. In this letter, the ergodic and outage capacity loss constraints are proposed to protect the PU according to its service types. We investigate the performance of the SU in terms of ergodic capacity under various power allocation policies of the PU. Specifically, three PU power allocation policies are considered, namely waterfilling, truncated channel inversion with fixed rate (TIFR) and constant power allocation. We obtain the ergodic capacities of the SU under the three PU power allocation policies. The numerical results show that the PU waterfilling and TIFR power allocation policies are superior to the PU constant power allocation in terms of the capacity of the PU. In particular, it is shown that, with respect to the ergodic capacity of the SU, the PU waterfilling power allocation is superior to the PU constant power allocation, while the PU TIFR power allocation is inferior to the PU constant power allocation.