Adaptive network coded cooperation (ANCC) scheme may have excellent performance for data transmission from a large collection of terminals to a common destination in wireless networks. However, the random relay selection strategy for ANCC protocol may generate the distributed low-density parity-check (LDPC) codes with many short cycles which may cause error floor and performance degradation. In this paper, an optimized relay selection strategy for ANCC is proposed. Before data communication, by exploiting low-cost information interaction between the destination and terminals, the proposed method generates good assembles of distributed LDPC codes and its storage requirement reduces dramatically. Simulation results demonstrate that the proposed relay selection protocol significantly outperforms the random relay selection strategy.

We consider how to accurately estimate the position of targets that exist in closed areas such as a room. In the past, arranging the sensors in a straight line would trigger large position estimation errors in the same direction of the straight line. However, this arrangement is useful because of easy setting, wirings, and space limitations. In this paper, we show a novel algorithm which can reduce the error using signals from reflection objects such as walls. The algorithm uses ellipse relations among sensors, targets and reflection points. Using ellipse relations, the algorithm estimates the positions of the reflection points which are assumed to be the locations of the virtual sensors. So in spite of the straight sensor arranging, the sensors are virtually distributed such as surrounding the targets. In this paper, we show the algorithm and error reduction performances as determined from computer simulations.

This paper proposes an adaptive sequential cooperative energy detection scheme for primary user detection in cognitive radio to minimize the detection time while guaranteeing the desired detection accuracy. Simulation results are provided to show the effectiveness of the proposed scheme.

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.

Ultra-wide band (UWB) pulse radar has high range resolution, and is thus applicable to imaging sensors for a household robot. To enhance the imaging region of UWB radar, especially for multiple objects with complex shapes, an imaging algorithm based on aperture synthesis for multiple scattered waves has been proposed. However, this algorithm has difficulty realizing in real-time processing because its computation time is long. To overcome this difficulty, this letter proposes a fast accurate algorithm for shadow region imaging by incorporating the Range Points Migration (RPM) algorithm. The results of the numerical simulation show that, while the proposed algorithm affects the performance of the shadow region imaging slightly, it does not cause significant accuracy degradation and significantly decreases the computation time by a factor of 100 compared to the conventional algorithm.

Temporal and spatial (geographical) fluctuations, which are present in the traffic of wireless sensor networks (WSNs), have a significant affect on the transmission performance and power consumption of WSNs. Several medium access control (MAC) mechanisms have been proposed for IEEE802.15.4 cluster-based WSNs to counter the temporal and spatial traffic fluctuations. However, these mechanisms cannot always achieve simultaneous improvement in both transmission performance and power consumption. In this paper, we propose two enhanced 2-level active period control mechanisms, BI&CAP control and BI&SD&CAP control, to achieve higher system performance than conventional control mechanisms. Various computer simulation results demonstrate the effectiveness of the proposed mechanisms for WSNs with various traffic fluctuations.

Regularization plays a fundamental role in adaptive filtering. There are, very likely, many different ways to regularize an adaptive filter. In this letter, we propose one possible way to do it based on a condition that makes intuitively sense. From this condition, we show how to regularize the recursive least-squares (RLS) algorithm.

UWB (Ultra Wide-Band) pulse radar is promising for surveillance systems because it has an outstanding high range-resolution. To realize an accurate UWB radar imaging system, we propose a new approach that employs multipath echoes from a target in an indoor environment. Using multipath echoes, the proposed system can accurately estimate images, even for targets in a shadow region where the targets are out of sight of the antenna. We apply a simple interferometry technique using the multiple mirror image antennas generated by multipath propagation. We find that this simple method also produces many undesired false image points. To tackle this issue, we also propose an effective false image reduction algorithm to obtain a clear image. Numerical simulations verify that most of the false image points are removed and the target shape is accurately estimated.

The applicability in harsh optical environments, such as dark smog, or strong backlight of ultra-wide band (UWB) pulse radar has a definite advantage over optical ranging techniques. We have already proposed the extended Synthetic Aperture Radar (SAR) algorithm employing double scattered waves, which aimed at enhancing the reconstructible region of the target boundary including shadow region. However, it still suffers from the shadow area for the target that has a sharp inclination or deep concave boundary, because it assumes a mono-static model, whose real aperture size is, in general, small. To resolve this issue, this study proposes an extension algorithm of the double scattered SAR based on a multi-static configuration. While this extension is quite simple, the effectiveness of the proposed method is nontrivial with regard to the expansion of the imaging range. The results from numerical simulations verify that our method significantly enhances the visible range of the target surfaces without a priori knowledge of the target shapes or any preliminary observation of its surroundings.

In this paper, we present an approach of detecting speech presence for which the decision rule is based on a combination of multiple features using a sigmoid function. A minimum classification error (MCE) training is used to update the weights adjustment for the combination. The features, consisting of three parameters: the ratio of ZCR, the spectral energy, and spectral entropy, are combined linearly with weights derived from the sub-band domain. First, the Bark-scale wavelet decomposition (BSWD) is used to split the input speech into 24 critical sub-bands. Next, the feature parameters are derived from the selected frequency sub-band to form robust voice feature parameters. In order to discard the seriously corrupted frequency sub-band, a strategy of adaptive frequency sub-band extraction (AFSE) dependant on the sub-band SNR is then applied to only the frequency sub-band used. Finally, these three feature parameters, which only consider the useful sub-band, are combined through a sigmoid type function incorporating optimal weights based on MSE training to detect either a speech present frame or a speech absent frame. Experimental results show that the performance of the proposed algorithm is superior to the standard methods such as G.729B and AMR2.

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.

Different encoding modes for variable block size are available in the H.264/AVC standard in order to offer better coding quality. However, this also introduces huge computation time due to the exhaustive check for all modes. In this paper, a fast spatial DIRECT mode decision method for profiles supporting B frame encoding (main profile, high profile, etc.) in H.264/AVC is proposed. Statistical analysis on multiple video sequences is carried out, and the strong relationship of mode selection and rate-distortion (RD) cost between the current DIRECT macroblock (MB) and the co-located MBs is observed. With the check of mode condition, predicted RD cost threshold and dynamic parameter update model, the complex mode decision process can be terminated at an early stage even for small QP cases. Simulation results demonstrate the proposed method can achieve much better performance than the original exhaustive rate-distortion optimization (RDO) based mode decision algorithm by reducing up to 56.8% of encoding time for IBPBP picture group and up to 67.8% of encoding time for IBBPBBP picture group while incurring only negligible bit increment and quality degradation.

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.

A stereo video coding scheme which is compatible with monoview-processor is presented in this paper. At the same time, this paper proposes an adaptive prediction structure which can make different prediction modes to be applied to different groups of picture (GOPs) according to temporal correlations and interview correlations to improve the coding efficiency. Moreover, the most advanced video coding standard H.264 is used conveniently for maximize the coding efficiency in this paper. Finally, the effectiveness of the proposed scheme is verified by extensive experimental results.

We describe a concept and realization of distance-adaptive (DA) resource allocation in spectrum-sliced elastic optical path network (SLICE). We modify the modulation format and cross-connection bandwidth of individual fixed-bit rate optical paths to optimize performance with respect to transmission distance. The shorter paths are allocated a smaller amount of resources which allows reducing the spectrum occupied by the channel. We show in calculation a reduction in required spectral resources of more than 60% when compared to the traditional traffic allocation schemes based on ITU-T grid. The concept is verified experimentally.

The design of an energy-efficient wavelength division multiplexing (WDM) transponder is proposed and effectiveness of the proposed WDM transponder is experimentally studied. The proposed WDM transponder interworking with the link-aggregation technique possessed by a layer 2 switch can achieve power saving depending on traffic volume variations by utilizing an adaptive interface control. Monitoring methods for the link connectivity of a sleep link are also discussed.

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%.

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

Traffic adaptive 2-level active period control has been proposed to enhance system performance in cluster-based wireless sensor networks (WSNs) employing IEEE 802.15.4 medium access control (MAC) under temporal and spatial (geographical) non-uniform traffic environments. This paper proposes an adaptive method of controlling the backoff window for traffic adaptive 2-level active period control. The proposed method adjusts the size of the backoff window according to the length of the current active period, which is determined by 2-level active period control, and the time position for channel access in the active period. The results evaluated through computer simulations reveal that the proposed method can improve throughput as well as achieve high energy efficiency in cluster-based WSNs with non-uniform traffic distributions.

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.