The past decade has seen a surge of research activities in the fields of mobile computing and wireless communication. In particular, recent technological advances have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. To effectively operate portable devices, energy efficiency and Quality of Service (QoS) provisioning are two primary concerns. Dynamic Voltage Scaling (DVS) is a common method for energy conservation for portable devices. However, due to the amount of data that needs to be dynamically handled in varying time periods, it is difficult to apply conventional DVS techniques to QoS sensitive multimedia applications. In this paper, a new adaptive DVS algorithm is proposed for QoS assurance and energy efficiency. Based on the repeated learning model, the proposed algorithm dynamically schedules multimedia service requests to strike the appropriate performance balance between contradictory requirements. Experimental results clearly indicate the performance of the proposed algorithm over that of existing schemes.

Change history in project revisions provides helpful information on handling bugs. Existing studies on predicting bugs mainly focus on resulting bug patterns, not these change patterns. When a code hunk is copied onto several files, the set of original and copied hunks often need to be consistently maintained. We assume that it is a normal state when all of hunks survive or die in a specific revision. When partial change occurs on some duplicated hunks, they are regarded as suspicious hunks. Based on these assumptions, suspicious cases can be predicted and the project's developers can be alerted. In this paper, we propose a practical approach to detect various change smells based on revision history and code hunk tracking. The change smells are suspicious change patterns that can result in potential bugs, such as partial death of hunks, missed refactoring or fix, backward or late change. To detect these change smells, three kinds of hunks – add, delete, and modify – are tracked and analyzed by an automated tool. Several visualized graphs for each type have been suggested to improve the applicability of the proposed technique. We also conducted experiments on large-scale open projects. The case study results show the applicability of the proposed approach.

Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori-information on both noise spectrum and pitch. It works in the presence of noise with high amplitude and unknown direction of arrival. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.

Wireless Sensor Networks (WSNs) are gradually moving toward the adoption of clustered heterogeneous designs, incorporating a mixture of variety kinds of sensor nodes with different radio coverage and battery capacity. Compared with homogeneous networks, heterogeneous networks are able to reduce the initial cost of the network or prolong the network lifetime. The architecture and routing protocol for this type of heterogeneous WSN should be energy aware in order to prolong the lifetime of the network. However, most of the existing clustering methods consider only initial energy of the sensor nodes and ignore the non-uniform energy drainage caused by many-to-one traffic near sink and/or cluster heads in heterogeneous network environment. In this paper, we propose a new clustering method for WSN with heterogeneous node types which selects cluster heads considering not only the transmission power and residual energy of each node but also those of its adjacent nodes. Simulation experiments show that the proposed method increases network lifetime by 80% and 60% more than that of the CC and HEED, respectively.

Locally linear embedding (LLE) is a well-known method for nonlinear dimensionality reduction. The mathematical proof and experimental results presented in this paper show that the neighborhood sizes in LLE must be smaller than the dimensions of input data spaces, otherwise LLE would degenerate from a nonlinear method for dimensionality reduction into a linear method for dimensionality reduction. Furthermore, when the neighborhood sizes are larger than the dimensions of input data spaces, the solutions to LLE are not unique. In these cases, the addition of some regularization method is often proposed. The experimental results presented in this paper show that the regularization method is not robust. Too large or too small regularization parameters cannot unwrap S-curve. Although a moderate regularization parameters can unwrap S-curve, the relative distance in the input data will be distorted in unwrapping. Therefore, in order to make LLE play fully its advantage in nonlinear dimensionality reduction and avoid multiple solutions happening, the best way is to make sure that the neighborhood sizes are smaller than the dimensions of input data spaces.

Detecting spreaders, or scan sources, helps intrusion detection systems (IDS) identify potential attackers. The existing work can only detect aggressive spreaders that scan a large number of distinct destinations in a short period of time. However, stealthy spreaders may perform scanning deliberately at a low rate. We observe that these spreaders can easily evade the detection because current IDS's have serious limitations. Being lightweight, the proposed scheme can detect scan sources in high speed networking while residing in SRAM. By theoretical analysis and experiments on real Internet traffic traces, we demonstrate that the proposed scheme detects stealthy spreaders successfully.

We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.

This paper proposes a low-complexity signal detection algorithm for spatially correlated multiple-input multiple-output (MIMO) channels. The proposed algorithm sets a minimum mean-square error (MMSE) detection result to the starting point, and searches for signal candidates in multi-dimensions of the noise enhancement from which the MMSE detection suffers. The multi-dimensional search is needed because the number of dominant directions of the noise enhancement is likely to be more than one over the correlated MIMO channels. To reduce the computational complexity of the multi-dimensional search, the proposed algorithm limits the number of signal candidates to O(NT) where NT is the number of transmit antennas and O( ) is big O notation. Specifically, the signal candidates, which are unquantized, are obtained as the solution of a minimization problem under a constraint that a stream of the candidates should be equal to a constellation point. Finally, the detected signal is selected from hard decisions of both the MMSE detection result and unquantized signal candidates on the basis of the log likelihood function. For reducing the complexity of this process, the proposed algorithm decreases the number of calculations of the log likelihood functions for the quantized signal candidates. Computer simulations under a correlated MIMO channel condition demonstrate that the proposed scheme provides an excellent trade-off between BER performance and complexity, and that it is superior to conventional one-dimensional search algorithms in BER performance while requiring less complexity than the conventional algorithms.

We propose a system that, using video information, segments the mouth region from a face image and then detects the protrusion of the tongue from inside the oral cavity. Initially, under the assumption that the mouth is closed, we detect both mouth corners. We use a set of specifically oriented Gabor filters for enhancing horizontal features corresponding to the shadow existing between the upper and lower lips. After applying the Hough line detector, the extremes of the line that was found are regarded as the mouth corners. Detection rate for mouth corner localization is 85.33%. These points are then input to a mouth appearance model which fits a mouth contour to the image. By segmenting its bounding box we obtain a mouth template. Next, considering the symmetric nature of the mouth, we divide the template into right and left halves. Thus, our system makes use of three templates. We track the mouth in the following frames using normalized correlation for mouth template matching. Changes happening in the mouth region are directly described by the correlation value, i.e., the appearance of the tongue in the surface of the mouth will cause a decrease in the correlation coefficient through time. These coefficients are used for detecting the tongue protrusion. The right and left tongue protrusion positions will be detected by analyzing similarity changes between the right and left half-mouth templates and the currently tracked ones. Detection rates under the default parameters of our system are 90.20% for the tongue protrusion regardless of the position, and 84.78% for the right and left tongue protrusion positions. Our results demonstrate the feasibility of real-time tongue protrusion detection in vision-based systems and motivates further investigating the usage of this new modality in human-computer communication.

We develop an effective algorithm, based on the filtered backprojection (FBP) approach, for the imaging of vegetation. Under the FBP scheme, the reconstruction amounts at a non-trivial Fourier inversion, since the data are Fourier samples arranged on a non-Cartesian grid. The computational issue is efficiently tackled by Non-Uniform Fast Fourier Transforms (NUFFTs), whose complexity grows asymptotically as that of a standard FFT. Furthermore, significant speed-ups, as compared to fast CPU implementations, are obtained by a parallel versions of the NUFFT algorithm, purposely designed to be run on Graphic Processing Units (GPUs) by using the CUDA language. The performance of the parallel algorithm has been assessed in comparison to a CPU-multicore accelerated, Matlab implementation of the same routine, to other CPU-multicore accelerated implementations based on standard FFT and employing linear, cubic, spline and sinc interpolations and to a different, parallel algorithm exploiting a parallel linear interpolation stage. The proposed approach has resulted the most computationally convenient. Furthermore, an indoor, polarimetric experimental setup is developed, capable to isolate and introduce, one at a time, different non-idealities of a real acquisition, as the sources (wind, rain) of temporal decorrelation. Experimental far-field polarimetric measurements on a thuja plicata (western redcedar) tree point out the performance of the set up algorithm, its robustness against data truncation and temporal decorrelation as well as the possibility of discriminating scatterers with different features within the investigated scene.

To reduce the huge search space when customizing accelerators for the application specific instruction-set processor (ASIP), this paper proposes an automated customization method based on the data flow graph exploration. This method integrates the instruction identification and selection using an iterative improvement strategy, which uses a seed-growth algorithm to select the valid patterns that can bring higher performance enhancement. The search space is reduced by considering the performance factors during the identification stage. The experimental results indicate that the proposed method is feasible enough compared to the previous exhaustive algorithms.

Quadratic congruence code (QCC)-based frequency-hopping and time-spreading (FH/TS) optical orthogonal codes (OOCs), and the corresponding expanded cardinality were recently studied to improve data throughput and code capacity. In this paper, we propose a new FH/TS two-dimensional (2-D) code using the QCC and the cubic congruence code (CCC), named as the QCC/CCC 2-D code. Additionally the expanded CCC-based 2D codes are also considered. In contrast to the conventional QCC-based 1-D and QCC-based FH/TS 2-D optical codes, our analysis indicates that the code capacity of the CCC-based 1-D and CCC-based FH/TS 2-D codes can be improved with the same code weight and length, respectively.

This paper proposed a novel platform for sensor nodes to resolve the energy and latency challenges. It consists of a processor, an adaptive compressing module and several compression accelerators. We completed the proposed chip in a 0.18µm HJTC CMOS technology. Compared to the software-based solution, the hardware-assisted compression reduces over 98% energy and 212% latency. Besides, we balanced the energy and latency metric using an adaptive module. According to the scheduling algorithm, the module tunes the state of the compression accelerator, as well as the sampling frequency of the online sensor. For example, given a 9µs constraint for a 1-byte operation, it reduces 34% latency while the energy overheads are less than 5%.

A loop-back WDM-PON based on a RSOA has lots of merits, however one-level of the upstream signal has downstream information under OOK modulation. These effects make difficult to define decision threshold and estimate BER. In order to solve this, we propose a mathematical model of remodulated OOK signal and experimentally demonstrate BER performance with the near optimum decision threshold achieved by the proposed model.

In wireless networks, the mechanism to adaptively select a link transmission rate based on channel variations is referred to as RA (rate adaptation). The operation may have a critical impact on the upper-layer application, specifically video streaming which has strict QoS requirements. Thus, RA should consider the QoS requirements and radio conditions at the same time. In this paper, we present a CV-RA (cross-layer video-oriented rate adaptation) scheme for video transmission over multi-rate wireless networks. The transmission rate is switched in a cross-layer optimized way, by simultaneously considering video R-D (rate-distortion) characteristics as well as wireless conditions. At the radio link layer, transmission rate selection is made using cross-layer optimization. As a result of RA, the effective link throughput dynamically changes. At the application layer, video source rate is adaptively controlled using cross-layer adaptation. CV-RA is compared to three traditional RA schemes. It can realize the highest possible visual communications for any channel condition. For the previous schemes, the variations of visual quality is high due to dynamic packet error rates. In contrast, for CV-RA, visual quality improves with the channel condition.

In this paper, a frequency domain adaptive antenna array (FDAAA) algorithm is proposed for broadband single-carrier uplink transmissions in a cellular system. By employing AAA weight control in the frequency domain, the FDAAA receiver is able to suppress the multi-user interference (MUI) and the co-channel interference (CCI). In addition, the channel frequency selectivity can be exploited to suppress the inter-symbol interference (ISI) and to obtain frequency diversity (or the multi-path diversity). Another advantage of the FDAAA algorithm is that its performance is not affected by the spread of angles of arrival (AOA) of the received multi-path signal. In this study the structure of FDAAA receiver is discussed and the frequency domain signal-to-interference-plus-noise-ratio (SINR) after weight control is investigated. The performance of the FDAAA algorithm is confirmed by simulation results. It is shown that, the optimal FDAAA weight to obtain the best BER performance is that which fully cancels the interference when single-cell system is considered; On the other hand, when multi-cell cellular system is considered, the optimal FDAAA weight depends on both the cellular structure and the target signal to noise ratio (SNR) of transmit power control (TPC).

The tag collection algorithm in ISO/IEC 18000-7 has difficulty in collecting data from massive numbers of active RFID tags in a timely manner, so it should be improved to allow successful application in a wide variety of industrial fields. We propose two novel methods, a reduced-message method to improve the performance of data-tag collection and an efficient-sleep method to improve the performance of ID-tag collection. The reduced-message method decreases the slot size for a tag response by reducing the response size from the tag and reduces the number of commands issued from the reader. The efficient-sleep method utilizes redundant empty slots within the frame period to transmit sleep commands to the tags collected previously. We evaluated the performance improvement of tag collection by the proposed methods experimentally using an active RFID reader and 60 tags that we prepared for this study. The experimental results showed that the reduced-message method and the efficient-sleep method decreased the average tag collection time by 16.7% for data-tag collection and 9.3% for ID-tag collection compared with the standard tag collection. We also developed a simulation model for the active RFID system, reflecting the capture effect in wireless communication, and performed simulations to evaluate the proposed methods with a massive number of tags. The simulation results with up to 300 tags confirmed that the proposed methods could improve the tag collection performance, confirming the experimental results, even with larger numbers of tags.

We demonstrate high baud-rate DQPSK modulation with full-ETDM technique using a novel high-speed optical IQ modulator consisting of a ridge-type optical waveguide structure on a thin LiNbO3 substrate. Our fabrication technique achieves a drastic extension of the modulator's bandwidth and a reduction of half-wave voltage. Demonstration of 90-Gbaud NRZ-DP-DQPSK signal generation with the modulator successfully achieved a bit rate of 360-Gb/s under full-ETDM configuration.

Multi-core processors have been attracting a great deal of attention. In the domain of signal processing for communications, the current trends toward rapidly evolving standards and formats, and toward algorithms adaptive to dynamic factors in the environment, require programmable solutions that possess both algorithm flexibility and low implementation complexity. Reconfigurable architectures have demonstrated better tradeoffs between algorithm flexibility, implementation complexity, and energy efficiency. This paper presents a reconfigurable homogeneous memory-based FFT processor (MBFFT) architecture integrated in a single chip to provide hybrid SISO/MIMO OFDM wireless communication systems. For example, a reconfigurable MBFFT processor with eight processing elements (PEs) can be configured for one DVB-T/H with N=8192 and two 802.11n with N=128. The reconfigurable processors can perfectly fit the applications of Software Defined Radio (SDR) which requires more hardware flexibility.

We demonstrate an all-optical picosecond pulse duration-tunable nonreturn-to-zero (NRZ)-to-return-to-zero (RZ) data format conversion using a Raman amplifier-based compressor and a fiber-based four-wave mixing (FWM) switch. A NRZ data signal is injected into the fiber-based FWM switch (AND gate) with a compressed RZ clock by the Raman amplifier-based compressor, and convert to RZ data signal by the fiber-based FWM switch. The compressed RZ clock train acts as a pump signal in the fiber-based FWM switch to perform the NRZ-to-RZ data format conversion. By changing the Raman pump power of the Raman amplifier-based compressor, it is possible to tune the pulse duration of the converted RZ data signal from 15 ps to 2 ps. In all the tuning range, the receiver sensitivity at bit error rate (BER) of 10-9 for the converted RZ data signal was about 1.31.7 dB better than the receiver sensitivity of the input NRZ data signal. Moreover, the pulse pedestal of the converted RZ data signals is well suppressed owing to the FWM process in the fiber-based FWM switch.