High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the intra prediction accuracy, 35 intra prediction modes were used in the prediction units (PUs), with partition sizes ranging from 4 × 4 to 64 × 64 in HEVC. However, the manifold prediction modes dramatically increase the encoding complexity. This paper proposes a fast mode- and depth-decision algorithm based on edge detection and reconfiguration to alleviate the large computational complexity in intra prediction with trivial degradation in accuracy. For mode decision, we propose pixel gradient statistics (PGS) and mode refinement (MR). PGS uses pixel gradient information to assist in selecting the prediction mode after rough mode decision (RMD). MR uses the neighboring mode information to select the best PU mode (BPM). For depth decision, we propose a partition reconfiguration algorithm to replace the original partitioning order with a more reasonable structure, by using the smoothness of the coding unit as a criterion in deciding the prediction depth. Smoothness detection is based on the PGS result. Experiment results show that the proposed method saves about 41.50% of the original processing time with little degradation (BD bitrate increased by 0.66% and BDPSNR decreased by 0.060dB) in the coding gain.

Hands-free communications between cellular phones must be robust enough to withstand echo-path variation, and highly nonlinear echoes must be suppressed at low cost, when acoustic echo cancellation or suppression is applied to them. This paper proposes a spectrum-selective nonlinear echo suppression (SS-ES) approach as a solution to these issues. SS-ES is characterized by the selection of either a spectrum of the residual signal from an adaptive filter or a spectrum of the sending input signal depending on the amount of linear echo cancellation in an adaptive filter. Compared to conventional methods, the objective evaluation results of the SS-ES approach show an improvement of approximately 0.8-2.2dB, 0.23-2.39dB, and 0.26-0.50 in average echo return loss enhancement (ERLE), average root-mean-square log-spectral distortion (RMS-LSD), and the perceptual evaluation of speech quality (PESQ) value, respectively, under echo-path variation and double-talk conditions.

In this paper, a compression-friendly copyright- and privacy-protected image trading system is proposed. In the image trading system, the copyright of the image and the consumer's privacy is important. In addition, it should preserve existing image compression standards. In the proposed method, for privacy protection, the content provider (CP) multiplies random signs to the discrete wavelet transformed (DWTed) coefficients of an image to generate the visually encrypted image. The proposed visually protected images can be efficiently compressed by using JPEG 2000 which compresses the image in the DWTed domain as well. For copyright protection, the trusted third party (TTP) applies digital fingerprinting to the image in the encrypted domain. While in the conventional system, the amplitude-only image (AOI) which is the inversely transformed amplitude spectra of an image is used for privacy protection. Since, the AOI consists of real numbers, to store and transmit the AOI, it has to be quantized before compression. Therefore, quantization errors cannot be avoided in the conventional system. On the other hand, the proposed method applies the digital fingerprint in the DWTed domain, so clipping errors in decoding the image by the TTP is avoided. In addition, only a seed number which is input to a pseudo random number generator is shared between the CP and the consumer, whereas an extra image is shared in the conventional systems. Experimental results show that the proposed system is efficient in terms of privacy protection, compression performance, quality of fingerprinted images, and correct fingerprint extracting performance.

Hardware Trojan or any other kind of unwanted hardware modifications has been thought as a major challenge in many commercial and secure applications. Currently, detection and prevention of hardware Trojans appeared as an important requirement in such systems. In this paper, a new concept, Trojan Vulnerability Map, is introduced to model the immunity of various regions of hardware against hardware attacks. Then, placement and routing algorithms are proposed to improve the immunity of hardware using the Trojan Vulnerability Map. Experimental results show that the proposed placement and routing algorithm reduces the hardware vulnerability by 25.65% and 4.08%, respectively. These benefits are earned in cost of negligible total wire length and delay overhead.

The timing synchronization scheme for massive MIMO systems is proposed with CCK (Complementary Code Keying) codes in this letter. Our proposed scheme is specifically designed to generate synchronization sequence with large family size. The optimal sequences can be constructed with ZCZ or LCZ based on the number of antennas. Furthermore Monte Carlo simulation results confirmed the expected detection probability for massive MIMO systems.

In this paper, we present update rules for convolutive nonnegative matrix factorization (NMF) in which cost functions are based on the squared Euclidean distance, the Kullback-Leibler (KL) divergence and the Itakura-Saito (IS) divergence. We define an auxiliary function for each cost function and derive the update rule. We also apply this method to the single-channel signal separation in speech signals. Experimental results showed that the convergence of our KL divergence-based method was better than that in the conventional method, and our method achieved single-channel signal separation successfully.

Self-aligned four-terminal (4T) planar metal double-gate (DG) polycrystalline-silicon (poly-Si) thin-film transistors (TFTs) were fabricated on a glass substrate at a low temperature (LT), which is below $550^{circ}$C, to realize high performance and low power dissipation system-on-glass (SoG). The top gate (TG) and bottom gate (BG) were formed from tungsten (W); the BG was embedded in the glass substrate and the TG was fabricated by a self-alignment process using the BG as a photomask. This structure is called embedded metal double-gate (E-MeDG) in this paper. The poly-Si channel with lateral large grains was fabricated using a continuous-wave laser lateral crystallization (CLC). The self-aligned 4T E-MeDG LT poly-Si TFT, with a gate length of 5,$mu $m and TG and BG SiO$_2$ thicknesses of 50 and 100,nm, respectively, exhibited a subthreshold swing of 120,mV/dec and a threshold voltage ($mathrm{V}_{mathrm{th}}$) of $-$0.5,V in the connecting DG mode; i.e. when TG is connected to BG. In the TG operation at various BG control voltage, a threshold voltage modulation factor $(gamma = Delta mathrm{V}_{mathrm{th}}/Delta mathrm{V}_{mathrm{BG}})$ of 0.47 at negative BG control voltage and 0.60 at positive BG control voltage are demonstrated, which values are nearly equal to theoretical prediction of 0.40 and 0.75. Trend of subthreshold swing (s.s.) of TG operation under different BG control voltage are also consistent with theoretical prediction. In addition to TG operation, successful BG operation under various TG control voltages was confirmed. Field-effect mobility derived from g$_{mathrm{m}}$ also varied depending on control gate voltage. The high controllability of device parameter of individual LT poly-Si TFTs is caused by excellent crystalline quality of CLC poly-Si film and will enable us to the fabrication of high-speed and low power-dissipation SoG.

This paper studies mapping techniques of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper are static which means the mapping is decided at design time. The mapping techniques take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Additionally, the proposed static mapping supports dynamic application switching, which means the applications mapped onto the same cores are switched to each other at runtime. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experimental results show the effectiveness of the proposed techniques.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.

In 3GPP (3-rd Generation Partnership Project) LTE (Long Term Evolution) systems, D2D (Device-to-Device) communication has been selected as a next generation study item. In uplink D2D communication that underlies LTE systems, uplink interference signals generated by CUE (Cellular User Equipment) have a profound impact on the throughput of DUE (D2D User Equipment). For that reason, various resource allocation algorithms which consider interference channels have been studied; however, these algorithms assume accurate channel estimation and feedback of D2D related links. Therefore, in order to estimate uplink channels of D2D communication, SRS (Sounding Reference Signal) defined in LTE uplink channel structure can be considered. However, when the number of interferes increases, the SRS based method incurs significant overheads such as side information, operational complexity, channel estimation and feedback to UE. Therefore, in this paper, we propose an efficient channel estimation and CSI (Channel State Information) feedback method for D2D communication, and its application in LTE systems. We verify that the proposed method can achieve a similar performance to SRS based method with lower operational complexity and overhead.

The MR image segmentation is always a challenging problem because of the intensity inhomogeneity. Many existing methods don't reach their expected segmentations; besides their implementations are usually complicated. Therefore, we originally interleave the extended Otsu segmentation with bias field estimation in an energy minimization. Via our proposed method, the optimal segmentation and bias field estimation are achieved simultaneously throughout the reciprocal iteration. The results of our method not only satisfy the required classification via its applications in the synthetic and the real images, but also demonstrate that our method is superior to the baseline methods in accordance with the performance analysis of JS metrics.

In this paper, an adaptive expansion strategy (AES) is proposed for multiple-input/multiple-output (MIMO) detection in the presence of circular signals. By exploiting channel properties, the AES classifies MIMO channels into three types: excellent, average and deep fading. To avoid unnecessary branch-searching, the AES adopts single expansion (SE), partial expansion (PE) and full expansion (FE) for excellent channels, average channels and deep fading channels, respectively. In the PE, the non-circularity of signal is exploited, and the widely linear processing is extended from non-circular signals to circular signals by I (or Q) component cancellation. An analytical performance analysis is given to quantify the performance improvement. Simulation results show that the proposed algorithm can achieve quasi-optimal performance with much less complexity (hundreds of flops/symbol are saved) compared with the fixed-complexity sphere decoder (FSD) and the sphere decoder (SD).

In this paper, we present an efficient time-of-arrival (TOA)-based localization method for wireless sensor networks. The goal of a localization system is to accurately estimate the geographic location of a wireless device. In real wireless sensor networks, accurately estimating mobile device location is difficult because of the presence of various errors. Therefore, localization methods have been studied in recent years. In indoor environments, the accuracy of wireless localization systems is affected by non-line-of-sight (NLOS) errors. The presence of NLOS errors degrades the performance of wireless localization systems. In order to effectively estimate the location of the mobile device, NLOS errors should be recognized and mitigated in indoor environments. In the TOA-based ranging method, the distance between the two wireless devices can be computed by multiplying a signal's propagation delay time by the speed of light. TOA-based localization measures the distance between the mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors of the measured distance between the i-th BS and the MS is different due to dissimilar obstacles in the direct signal path between the two nodes. In order to accurately estimate the location in a TOA-based localization system, an optimized localization algorithm that selects three measured distances with fewer NLOS errors is necessary. We present an efficient TOA-based localization scheme that combines three selected BSs in wireless sensor networks. This localization scheme yields improved localization performance in wireless sensor networks. In this paper, performance tests are performed, and the simulation results are verified through comparisons between various localization methods and the proposed method. As a result, proposed localization scheme using BS selection achieves remarkably better localization performance than the conventional methods. This is verified by experiments in real environments, and demonstrates a performance analysis in NLOS environments. By using BS selection, we will show an efficient and effective TOA-based localization scheme in wireless sensor networks.

In typical end-to-end recovery protocols, an ACK segment is delivered to a source node over a single path. ACK loss requires the source to retransmit the corresponding data packet. However, in underwater wireless sensor networks which prefer flooding-based routing protocols, the source node has redundant chances to receive the ACK segment since multiple copies of the ACK segment can arrive at the source node along multiple paths. Since existing RTO calculation algorithms do not consider inherent features of underlying routing protocols, spurious packet retransmissions are unavoidable. Hence, in this letter, we propose a new ACK loss-aware RTO calculation algorithm, which utilizes statistical ACK arrival times and ACK loss rate, in order to reduce such retransmissions.

Re-identifying the same person in different images is a distinct challenge for visual surveillance systems. Building an accurate correspondence between highly variable images requires a suitable dissimilarity measure. To date, most existing measures have used adapted distance based on a learned metric. Unfortunately, real-world human image data, which tends to show large intra-class variations and small inter-class differences, continues to prevent these measures from achieving satisfactory re-identification performance. Recognizing neighboring distribution can provide additional useful information to help tackle the deviation of the to-be-measured samples, we propose a novel dissimilarity measure from the neighborhood-wise relative information perspective, which can deliver the effectiveness of those well-distributed samples to the badly-distributed samples to make intra-class dissimilarities smaller than inter-class dissimilarities, in a learned discriminative space. The effectiveness of this method is demonstrated by explanation and experimentation.

The dynamic time warping (DTW) algorithm is widely used to determine time series similarity search. As DTW has quadratic time complexity, the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. In this paper, we present a parallel approach for DTW on a heterogeneous platform with a graphics processing unit (GPU). In order to exploit fine-grained data-level parallelism, we propose a specific parallel decomposition in DTW. Furthermore, we introduce an optimization technique called diamond tiling to improve the utilization of threads. Results show that our approach substantially reduces computational time.

Accurate channel estimation is necessary before we can demodulate orthogonal frequency division multiplexing (OFDM) signals since the radio channel is frequency-selective and time-varying for wideband mobile communication systems. For pilot-symbol-aided channel estimation, pilot sequences are inserted periodically into the data stream enabling coherent detection at receiver. The control signal information can be embedded in pilot sequences and transmitted implicitly in OFDM systems to save the bandwidth. In order to estimate the channel and control signal jointly at the receiver, we propose a novel noise subspace based method in this paper. The proposed method is developed from the DFT-based channel estimator. If the hypothesized sequence coincides with the transmitted pilot sequence, the last part of the channel impulse response (CIR) estimate is only contributed by Gaussian noise and its average power is expected to be the minimum among all possible hypothesized sequences. Simulation results show that the proposed method works well in any of the channels even if integer carrier frequency offset (CFO) is considered.

A method to synthesize facial caricatures with non-planar expression is proposed. Several methods have been already proposed to synthesize facial caricatures automatically, but they mainly synthesize plane facial caricatures which look somewhat monotonous. In order to generate expressive facial caricature, the image should be expressed in non-planar style, expressing the depth of the face by shading and highlighting. In this paper, a new method to express such non-planar effect in facial caricatures is proposed by blending the grayscale information of the real face image into the plane caricature. Some methods also have been proposed to generate non-planar facial caricature, but the proposed method can adjust the degree of non-planar expression by interactive evolutionary computing, so that the obtained expression is satisfied by the user based on his/her subjective criteria. Since the color of the face looks changed, when the grayscale information of the natural face image is mixed, the color information of the skin area are also set by interactive evolutionary computing. Experimental results show the high performance of the proposed method.

In this paper, we propose an access control protocol method that maintains the communication quality of various applications and reduces packet loss of multicasts in wireless local area networks. Multicast transmission may facilitate effective bandwidth use because packets are simultaneously delivered to more than one mobile station by a single transmission. However, because multicast transmissions does not have a retransmission function, communication quality deteriorates because of packet collisions and interference waves from other systems. Moreover, although multicasts are not considered, the communication quality of each application is guaranteed by a priority control method known as enhanced distributed channel access in IEEE802.11e. The proposed method avoids both these issues. Specifically, because the proposed method first transmits the clear-to-send-to-self frame, the multicast packet avoids collision with the unicast packet. We validate the proposed method by computer simulation in an environment with traffic congestion and interference waves. The results show a reduction in multicast packet loss of approximately 20% and a higher multicast throughput improvement compared to conventional methods. Moreover, the proposed method can assure improve multicast communication quality without affecting other applications.