With the wide usage of multispectral images, a fast efficient multidimensional clustering method becomes not only meaningful but also necessary. In general, to speed up the multidimensional images' analysis, a multidimensional feature vector should be transformed into a lower dimensional space. The Hilbert curve is a continuous one-to-one mapping from N-dimensional space to one-dimensional space, and can preserves neighborhood as much as possible. However, because the Hilbert curve is generated by a recurve division process, 'Boundary Effects' will happen, which means data that are close in N-dimensional space may not be close in one-dimensional Hilbert order. In this paper, a new efficient approach based on the space-filling curves is proposed for classifying multispectral satellite images. In order to remove 'Boundary Effects' of the Hilbert curve, multiple Hilbert curves, z curves, and the Pseudo-Hilbert curve are used jointly. The proposed method extracts category clusters from one-dimensional data without computing any distance in N-dimensional space. Furthermore, multispectral images can be analyzed hierarchically from coarse data distribution to fine data distribution in accordance with different application. The experimental results performed on LANDSAT data have demonstrated that the proposed method is efficient to manage the multispectral images and can be applied easily.

This letter presents the architecture of multi-gigabit parallel demodulator suitable for demodulating high order QAM modulated signal and easy to implement on FPGA platform. The parallel architecture is based on frequency domain implementation of matched filter and timing phase correction. Parallel FIFO based delete-keep algorithm is proposed for timing synchronization, while a kind of reduced constellation phase-frequency detector based parallel decision feedback PLL is designed for carrier synchronization. A fully pipelined parallel adaptive blind equalization algorithm is also proposed. Their parallel implementation structures suitable for FPGA platform are investigated. Besides, in the demonstration of 2 Gbps demodulator for 16QAM modulation, the architecture is implemented and validated on a Xilinx V6 FPGA platform with performance loss less than 2 dB.

This paper describes an improved multiresolution telescopic search algorithm (MRTlcSA) for block-matching motion estimation. The algorithm uses images with full and reduced bit resolution, and uses motion-track and adaptive-search-window strategies. Simulation results show that the proposed algorithm has low computational complexity and achieves good image quality. We have developed a systolic-architecture-based search engine that has split data paths. In the case of low bit-resolution, the throughput is increased by enhancing the operating parallelism. The new motion estimator works at a low clock frequency and a low supply voltage, and therefore has low power consumption.

The 2-dimensional (2-D) Hilbert curve is a one-to-one mapping between 2-D space and one-dimensional (1-D) space. It is studied actively in the area of digital image processing as a scan technique (Hilbert scan) because of its property of preserving the spacial relationship of the 2-D patterns. Currently there exist several Hilbert scan algorithms. However, these algorithms have two strict restrictions in implementation. First, recursive functions are used to generate a Hilbert curve, which makes the algorithms complex and computationally expensive. Second, both sides of the scanned rectangle must have same size and each size must be a power of two, which limits the application of the Hilbert scan greatly. In this paper, a Pseudo-Hilbert scan algorithm based on two look-up tables is proposed. The proposed method improves the Hilbert scan to be suitable for real-time processing and general application. The simulation indicates that the Pseudo-Hilbert scan can preserve point neighborhoods as much as possible and take advantage of the high correlation between neighboring lattice points. It also shows competitive performance of the Pseudo-Hilbert scan in comparison with other scan techniques.

In this paper, a novel trench MOS barrier Schottky contact super barrier rectifier (TMB-SSBR) is proposed by combining the advantages of vertical SSBR and conventional TMBS. The operation mechanism and simulation verification are presented. TMB-SSBR consists of MOS trenches with a vertical SSBR grid which replaces the Schottky diode in the mesa of a TMBS. Due to the presence of top p-n junction in the proposed TMB-SSBR, the image force barrier lowering effect is eliminated, the pinching off electric field effect by MOS trenches is weakened, so that the mesa surface electric field is much larger than that in conventional TMBS. Therefore, the mesa width is enlarged and the n-drift concentration is slightly increased, which results in a low specific on-resistance and a good tradeoff between reverse leakage currents and forward voltages. Compared to a conventional TMBS, simulation results show that, with the same breakdown voltage of 124V and the same reverse leakage current at room temperature, TMB-SSBR increases the figure of merit (FOM, equates to VB2/Ron, sp) by 25.5%, and decreases the reverse leakage by 33.3% at the temperature of 423K. Just like the development from SBD to TMBS, from TMBS to TMB-SSBR also brings obvious improvement of performance.

In this letter, we propose a novel robust transferable subspace learning (RTSL) method for cross-corpus facial expression recognition. In this method, on one hand, we present a novel distance metric algorithm, which jointly considers the local and global distance distribution measure, to reduce the cross-corpus mismatch. On the other hand, we design a label guidance strategy to improve the discriminate ability of subspace. Thus, the RTSL is much more robust to the cross-corpus recognition problem than traditional transfer learning methods. We conduct extensive experiments on several facial expression corpora to evaluate the recognition performance of RTSL. The results demonstrate the superiority of the proposed method over some state-of-the-art methods.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

Heterogeneous networks (HetNets) can provide higher capacity and user throughput than homogeneous networks in Long Term Evolution (LTE)-Advanced systems. However, because of increased interference from neighboring cells and the characteristics of the embedded small cells, handover performance is impacted adversely, especially when the user equipment (UE) moves at medium or high speeds. In this paper, to improve mobility performance, we propose two schemes, i.e., 1) using wideband signal-to-interference noise ratio (SINR) as the handover metric and 2) emergency attaching. The schemes can enhance mobility performance since handovers are performed based on the quality of the radio link. Importantly, the two schemes compliment rather than contradict each other. System-level simulations show that both the individual proposed schemes and the joint schemes can improve mobility performance significantly.

The ring-like systolic array architecture described in this paper, based on a conventional one-dimensional systolic array architecture, was created through operator rescheduling based on the symmetry of data flow. This eliminated high-latency delay due to the stuffing of the array pipeline in the conventional architecture. The new architecture requires a memory bandwidth no greater than the conventional architecture does, but increases throughput and processor utilization while reducing power consumption.

The N-dimensional (N-D) Hilbert curve is a one-to-one mapping between N-D space and one-dimensional (1-D) space. It is studied actively in the area of digital image processing as a scan technique (Hilbert scan) because of its property of preserving the spatial relationship of the N-D patterns. Currently there exist several Hilbert scan algorithms. However, these algorithms have two strict restrictions in implementation. First, recursive functions are used to generate a Hilbert curve, which makes the algorithms complex and computationally expensive. Second, all the sides of the scanned region must have the same size and the length must be a power of two, which limits the application of the Hilbert scan greatly. Thus in order to remove these constraints and improve the Hilbert scan for general application, a nonrecursive N-D Pseudo-Hilbert scan algorithm based on two look-up tables is proposed in this paper. The merit of the proposed algorithm is that implementation is much easier than the original one while preserving the original characteristics. The experimental results indicate that the Pseudo-Hilbert scan can preserve point neighborhoods as much as possible and take advantage of the high correlation between neighboring lattice points, and it also shows the competitive performance of the Pseudo-Hilbert scan in comparison with other common scan techniques. We believe that this novel scan technique undoubtedly leads to many new applications in those areas can benefit from reducing the dimensionality of the problem.