This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

In this paper, we present a construction method of non-binary low-density parity-check (LDPC) convolutional codes. Our construction method is an extension of Felstrom and Zigangirov construction [1] for non-binary LDPC convolutional codes. The rate-compatibility of the non-binary convolutional code is also discussed. The proposed rate-compatible code is designed from one single mother (2,4)-regular non-binary LDPC convolutional code of rate 1/2. Higher-rate codes are produced by puncturing the mother code and lower-rate codes are produced by multiplicatively repeating the mother code. Simulation results show that non-binary LDPC convolutional codes of rate 1/2 outperform state-of-the-art binary LDPC convolutional codes with comparable constraint bit length. Also the derived low-rate and high-rate non-binary LDPC convolutional codes exhibit good decoding performance without loss of large gap to the Shannon limits.

In this letter, we propose an effective method to retrieve images from a 2D shape image database using discrete shock graphs combined with an adaptive selection algorithm. Experimental results show that our method is more accurate and fast than conventional approaches and reduces computational complexity.

An on-chip temperature compensation active bias circuit having tunable temperature slope has been proposed, and its application to an X-band GaAs FET monolithic microwave integrated circuit (MMIC) power amplifier (PA) is described. The proposed bias circuit can adjust the temperature slope of gate voltage according to the bias condition of the PA, and also realizes the higher temperature slope of the gate voltage by employing the diode and the FET which operates at near threshold voltage (Vt) in the bias circuit. As a result, the gain of PAs operated at any bias conditions is kept almost constant against temperature by applying the proposed bias circuit. Moreover, the proposed bias circuit can be integrated in the same chip with the MMIC PA since it does not need off-chip components, and operates with only negative voltage source. The fabricated results of the on-chip temperature compensation active bias circuit shows that the temperature slope of the gate voltage varies from 2.1 to 6.3 mV/, which is enough to compensate the gain of not only class-B PA but also class-A PA. The gain deviation of the developed GaAs FET MMIC PA with the proposed bias circuit has been reduced from 3.3 dB to 0.6 dB in the temperature range of 100.

On the huge-scale array antenna for SSPS (space solar power systems), the problem of faulty elements and effect of mutual coupling between array elements should be considered in practice. In this paper, the effect of faulty elements as well as mutual coupling on the performance of the huge-scale array antenna are analyzed by using the proposed IEM/LAC. The result shows that effect of faulty elements and mutual coupling on the actual gain of the huge-scale array antenna are significant.

In this paper, a rat-race hybrid coupler based on an open complementary split ring resonator (OCSRR) is presented. By embedding the OCSRR in the microstrip transmission line, slow-wave effect is introduced to achieve size reduction. The proposed rat-race coupler size is 37% smaller than the conventional rat-race coupler. Besides, the proposed coupler provides better third harmonic suppression up to 35 dB. The simulated results are compared with the measured data and good agreement is reported.

Energy minimization has become one of the primary goals in the embedded real-time domains. Consequently, scratch-pad memory has been employed as partial or entire replacement for cache memory due to its better energy efficiency. However, most previous approaches were not applicable to a preemptive multi-task environment. We propose three methods of partitioning and allocation of scratch-pad memory for fixed-priority-based preemptive multi-task systems. The three methods, i.e., spatial, temporal, and hybrid methods, achieve energy reduction in the instruction memory subsystems. With the spatial method, each task occupies its exclusive space in scratch-pad memory. With the temporal method, the running task uses entire scratch-pad space. The content of scratch-pad memory is swapped out as a task executes or gets preempted. The hybrid method is based on the spatial one but a higher priority task can temporarily use the space of lower priority task. The amount of space is prioritized for higher priority tasks. We formulate each method as an integer programming problem that simultaneously determines (1) partitioning of scratch-pad memory space for the tasks, and (2) allocation of program code to scratch-pad memory space for each task. Our methods not only support the real-time task scheduling but also consider aggressively the periods and priorities of tasks for the energy minimization. Additionally, we implement an RTOS-hardware cooperative support mechanism for runtime code allocation to the scratch-pad memory space. We have made the experiments with the fully functional real-time operating system. The experimental results have demonstrated the effectiveness of our techniques. Up to 73% energy reduction compared to a conventional method was achieved.

With the increasing demand of high video quality and large image size, adaptive interpolation filter (AIF) addresses these issues and conquers the time varying effects resulting in increased coding efficiency, comparing with recent H.264 standard. However, currently most AIF algorithms are based on either frame level or macroblock (MB) level, which are not flexible enough for different video contents in a real codec system, and most of them are facing a severe time consuming problem. This paper proposes a content based coarse to fine AIF algorithm, which can adapt to video contents by adding different filters and conditions from coarse to fine. The overall algorithm has been mainly made up by 3 schemes: frequency analysis based frame level skip interpolation, motion vector modeling based region level interpolation, and edge detection based macroblock level interpolation. According to the experiments, AIF are discovered to be more effective in the high frequency frames, therefore, the condition to skip low frequency frames for generating AIF coefficients has been set. Moreover, by utilizing the motion vector information of previous frames the region level based interpolation has been designed, and Laplacian of Gaussian based macroblock level interpolation has been proposed to drive the interpolation process from coarse to fine. Six 720p and six 1080p video sequences which cover most typical video types have been tested for evaluating the proposed algorithm. The experimental results show that the proposed algorithm reduce total encoding time about 41% for 720p and 25% for 1080p sequences averagely, comparing with Key Technology Areas (KTA) Enhanced AIF algorithm, while obtains a BDPSNR gain up to 0.004 and 3.122 BDBR reduction.

This paper proposes a reversible two dimensional (2D) discrete wavelet transform (DWT) for lossless coding which is compatible with the irreversible 2D 9-7 DWT for lossy coding in the JPEG 2000. Since all the filters and scalings are factorized into a product of lifting steps, and signal values are rounded into integers, the proposed DWT is reversible and applicable to lossless coding of 2D signals. We replace a part of the separable 2D transfer function of the 2D DWT by a non separable 2D lifting structure, so that the number of rounding operations is decreased. We also investigate performance of the DWT under octave decomposition case and theoretically endorse it. As a result, reduction of the rounding errors due to the replacement was confirmed. It means that compatibility of the reversible DWT to the irreversible 2D 9-7 DWT is improved.

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, lower multi-path distortion and others. To further utilize the vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subcarrier and bit allocation among users. In this paper, we propose a 0-1 integer programming model formulating the optimal subcarrier and bit allocation problem of the multiuser OFDM. We proved that the continuous relaxation of our formulation is tighter than the previous convex optimization formulation based on perspective function and the Lagrangian dual bound of our formulation is equivalent to the linear programming relaxation bound. The proposed Lagrangian dual is seperable with respect to subcarriers and allows an efficient dual maximization algorithm. We compared the performance of the integer programming formulation and the Lagrangian dual of our formulation and the continuous relaxation and the primal heuristic proposed in [3]. Computer simulation on a system employing M-ary quadrature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multipaths is carried out with the optimal subcarrier and bit allocation solution generated by the 0-1 integer programming model.

The marking construction problem (MCP) of Petri nets is defined as follows: “Given a Petri net N, an initial marking Mi and a target marking Mt, construct a marking that is closest to Mt among those which can be reached from Mi by firing transitions.” MCP includes the well-known marking reachability problem of Petri nets. MCP is known to be NP-hard, and we propose two schemas of heuristic algorithms: (i) not using any algorithm for the maximum legal firing sequence problem (MAX LFS) or (ii) using an algorithm for MAX LFS. Moreover, this paper proposes four pseudo-polynomial time algorithms: MCG and MCA for (i), and MCHFk and MC_feideq_a for (ii), where MCA (MC_feideq_a, respectively) is an improved version of MCG (MCHFk). Their performance is evaluated through results of computing experiment.

A non-audible murmur (NAM), a very weak whisper sound produced without vocal fold vibration, has been researched in the development of a silent-speech communication tool for functional speech disorders as well as human-to-human/machine interfaces with inaudible voice input. The NAM can be detected using a specially designed microphone, called a NAM microphone, attached to the neck. However, the detected NAM signal has a low signal-to-noise ratio and severely suppressed high-frequency component. To improve NAM clarity, the mechanism of a NAM production must be clarified. In this work, an air flow through a glottis in the vocal tract was numerically simulated using computational fluid dynamics and vocal tract shape models that are obtained by a magnetic resonance imaging (MRI) scan for whispered voice production with various strengths, i.e. strong, weak, and very weak. For a very weak whispering during the MRI scan, subjects were trained, just before the scanning, to produce the very weak whispered voice, or the NAM. The numerical results show that a weak vorticity flow occurs in the supraglottal region even during a very weak whisper production; such vorticity flow provide aeroacoustic sources for a very weak whispering, i.e. NAM, as in an ordinary whispering.

With energy shortages and global climate change leading our concerns these days, the energy consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduler for reducing energy consumption of datacenters in Cloud computing platforms. It is composed of four algorithms: prediction, ON/OFF, task scheduling, and evaluation algorithms. The prediction algorithm employs a neural predictor to predict future load demand based on historical demand. According to the prediction, the ON/OFF algorithm dynamically adjusts server allocations to minimize the number of servers running, thus minimizing the energy use at the points of consumption to benefit all other levels. The task scheduling algorithm is responsible for directing request traffic away from powered-down servers and toward active servers. The performance is monitored by the evaluation algorithm to balance the system's adaptability against stability. For evaluation, we perform simulations with two load traces. The results show that the prediction mode, with a combination of dynamic training and dynamic provisioning of 20% additional servers, can reduce energy consumption by 49.8% with a drop rate of 0.02% on one load trace, and a drop rate of 0.16% with an energy consumption reduction of 55.4% on the other. Our method is also proven to have a distinct advantage over its counterparts.

In this paper, a novel algorithm, Cross Low-dimension Pursuit, based on a new structured sparse matrix, Permuted Block Diagonal (PBD) matrix, is proposed in order to recover sparse signals from incomplete linear measurements. The main idea of the proposed method is using the PBD matrix to convert a high-dimension sparse recovery problem into two (or more) groups of highly low-dimension problems and crossly recover the entries of the original signal from them in an iterative way. By sampling a sufficiently sparse signal with a PBD matrix, the proposed algorithm can recover it efficiently. It has the following advantages over conventional algorithms: (1) low complexity, i.e., the algorithm has linear complexity, which is much lower than that of existing algorithms including greedy algorithms such as Orthogonal Matching Pursuit and (2) high recovery ability, i.e., the proposed algorithm can recover much less sparse signals than even 1-norm minimization algorithms. Moreover, we demonstrate both theoretically and empirically that the proposed algorithm can reliably recover a sparse signal from highly incomplete measurements.

Context awareness is viewed as one of the most important goals in the pervasive computing paradigm. As one kind of context awareness, danger awareness describes and detects dangerous situations around a user, and provides services such as warning to protect the user from dangers. One important problem arising in danger-aware systems is that the description/definition of dangerous situations becomes more and more complex, since many factors have to be considered in such description, which brings a big burden to the developers/users and thereby reduces the reliability of the system. It is necessary to develop a flexible reasoning method, which can ease the description/definition of dangerous situations by reasoning dangers using limited specified/predefined contexts/rules, and increase system reliability by detecting unspecified dangerous situations. Some reasoning mechanisms based on context similarity were proposed to address the above problems. However, the current mechanisms are not so accurate in some cases, since the similarity is computed from only basic knowledge, e.g. nature property, such as material, size etc, and category information, i.e. they may cause false positive and false negative problems. To solve the above problems, in this paper we propose a new flexible and accurate method from feature point of view. Firstly, a new ontology explicitly integrating basic knowledge and danger feature is designed for computing similarity in danger-aware systems. Then a new method is proposed to compute object similarity from both basic knowledge and danger feature point of views when calculating context similarity. The method is implemented in an indoor ubiquitous test bed and evaluated through experiments. The experiment result shows that the accuracy of system can be effectively increased based on the comparison between system decision and estimation of human observers, comparing with the existing methods. And the burden of defining dangerous situations can be decreased by evaluating trade-off between the system's accuracy and burden of defining dangerous situations.

This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.

In this paper, the joint optimization issue of the cooperative precoder design is investigated for the transmission from the cooperative multi-point system to one mobile terminal. Based on the mean squared error minimization criterion, the problem is established for the cooperative precoder design. Unfortunately, this problem cannot be solved due to the block diagonal structure of the whole precoding matrix resulting from the fact that there is no data exchange among multiple base stations. In order to tackle this difficulty, the original problem is converted into an equivalent problem by stacking all of the nonzero entries in the block diagonal matrix into a long column vector. With the equivalent problem, the optimum solution is obtained in a closed-form expression by using the Lagrangian multiplier method. Numerical simulations illustrate the effectiveness of the proposed scheme in terms of bit error rate and spectral efficiency.

Complex-valued Associative Memory (CAM) is an advanced model of Hopfield Associative Memory. The CAM is based on multi-state neurons and has the high ability of representation. Lee proposed gradient descent learning for the CAM to improve the storage capacity. It is based on only the phases of input signals. In this paper, we propose another type of gradient descent learning based on both the phases and the amplitude. The proposed learning method improves the noise robustness and accelerates the learning speed.

A 0.8-V CMOS Phase-Locked Loop (PLL) has been designed and fabricated by using a 0.13-µm 1p8m CMOS process. In the proposed PLL, the double-positive-feedbacks voltage-controlled oscillator (DPF-VCO) is used to generate current signals for the coupling current-mode injection-locked frequency divider (CCMILFD) and current-injection current-mode logic (CICML) divider. A short-pulsed-reset phase frequency detector (SPR-PFD) with the reduced pulse width of reset signal to improve the linear range of the PFD and a complementary-type charge pump to eliminate the current path delay are also adopted in the proposed PLL. The measured in-band phase noise of the fabricated PLL is -98 dBc/Hz. The locking range of the PLL is from 22.6 GHz to 23.3 GHz and the reference spur level is -69 dBm that is 54 dB bellow the carrier. The power consumption is 9.2 mW under a 0.8-V power supply. The proposed PLL has the advantages of low phase noise, low reference spur, and low power dissipation at low voltage operation.

We analyze subjective assessments by comparative evaluations of bulk data transmission by using two psychological methods (the method of successive categories and the constant method). From the results of the first experiment, the thresholds at which participants downloading a data file began to feel dissatisfaction with service degradation are 15.7 Mbps and 11.6 Mbps obtained by the two different methods when the throughput without the service degradation is approximately 22 Mbps. In the second experiment, we investigate the threshold of user satisfaction for various network environments. The threshold is 63% of the throughput of the user's usual network environment. Moreover, from the viewpoint of download time, users feel dissatisfaction with the quality when download time becomes 1.5 times longer. These values can be used to more effectively allocate network resources and thereby achieve higher service quality.