This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.

Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification tasks. However, their performance may be significantly degraded in the presence of outliers. In this paper, we propose a robust SVM formulation which is shown to be less sensitive to outliers. The key idea is to employ an adaptively weighted hinge loss that explicitly incorporates outlier filtering in the SVM training, thus performing outlier filtering and classification simultaneously. The resulting robust SVM formulation is non-convex. We first relax it into a semi-definite programming which admits a global solution. To improve the efficiency, an iterative approach is developed. We have performed experiments using both synthetic and real-world data. Results show that the performance of the standard SVM degrades rapidly when more outliers are included, while the proposed robust SVM training is more stable in the presence of outliers.

A conference key establishment protocol allows a group of conferees to agree on a secret key shared among them for secure group communication. This paper proposes a three-level conference key establishment protocol based on the Universal Mobile Telecommunications System (UMTS) framework to establish a group-level key, home location register (HLR) level keys, and visitor location register (VLR) level keys simultaneously for a group of conferees. The group-level key is used to secure the communications for all conferees, the HLR-level key is for those within the same HLR domain, and the VLR-level key is for those within the same VLR domain. The group-level key can be used for securing inter-domain group-oriented applications such as commercial remote conferencing systems. The HLR- and VLR-level keys can be used for securing intra-domain subgroup applications (e.g., location-based or context-aware services) and dynamic key updating. Since our proposed protocol exploits existing UMTS security functions and the exclusive-or operation, it is compatible with UMTS architecture. This means that it is fast and easy to implement on the existing UMTS architecture. Furthermore, the proposed protocol has low computational complexities and can provide cost effectiveness, load-amortization, scalability, user authentication, key establishment, key confirmation, key updating, and lawful interception.

This paper deals with a reinforced cumulative probability distribution approach (CPDA) based method for extracting classification rules. The method includes two phases: (1) automatic generation of the membership function, and (2) use of the corresponding linguistic data to extract classification rules. The proposed method can determine suitable interval boundaries for any given dataset based on its own characteristics, and generate the fuzzy membership functions automatically. Experimental results show that the proposed method surpasses traditional methods in accuracy.

We address the compression efficiency of feedback-free and hash-check distributed video coding, which generates and transmits a hash code of a source information sequence. The hash code helps the decoder perform a motion search. A hash collision is a special case in which the hash codes of wrongly reconstructed information sequences occasionally match the hash code of the source information sequence. This deteriorates the quality of the decoded image greatly. In this paper, the statistics of hash collision are analyzed to help the codec select the optimal trade-off between the probability of hash collision and the length of the hash code, according to the principle of rate-distortion optimization. Furthermore, two novel algorithms are proposed: (1) the nonzero prefix of coefficients (NPC), which indicates the count of nonzero coefficients of each block for the second algorithm, and also saves 8.4% bitrate independently; (2) the adaptive selection of hash functions (AHF), which is based on the NPC and saves a further 2%–6% bitrate on average. The detailed optimization of the parameters of AHF is also presented.

Baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel filtering method based on a statistical weighted moving average filter. Supposed a and b are the minimum and maximum of all sample values within a moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region, denoted as [a₀, b₀] for simplicity. Third, for every sample point in the moving window, its weight is set to 1 if its value falls in [a₀, b₀]; otherwise, its weight is 0. Last, all sample points with weight 1 are averaged to estimate the baseline. The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and wavelet package translation did.

The geosynchronous circular synthetic aperture radar (GEOCSAR) is an innovative SAR system, which can produce high resolution three-dimensional (3D) images and has the potential to provide 3D deformation measurement. With an orbit altitude of approximately 36 000 km, the orbit motion and orbit disturbance effects of GEOCSAR behave differently from those of the conventional spaceborne SAR. In this paper, we analyze the effects of orbit errors on GEOCSAR imaging and interferometric processing. First, we present the GEOCSAR imaging geometry and the orbit errors model based on perturbation analysis. Then, we give the GEOCSAR signal formulation based on imaging geometry, and analyze the effect of the orbit error on the output focused signal. By interferometric processing on the 3D reconstructed images, the relationship between satellite orbit errors and the interferometric phase is deduced. Simulations demonstrate the effects of orbit errors on the GEOCSAR images, interferograms, and the deformations. The conclusions are that the required relative accuracy of orbit estimation should be at centimeter level for GEOCSAR imaging at L-band, and that millimeter-scale accuracy is needed for GEOCSAR interferometric processing.

A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system. High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset. An innovative A^* algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images. This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos. A simulated grid image was first used to analyze the effect of different heuristic functions on path planning. Three subsets of LiDAR data from Xi’an, Dunhuang, and Changyang in Northwest China were obtained. A quantitative method including pixel intensity, hue, and texture was used. With our proposed method, 9.4%, 8.7%, and 9.8% improvements were achieved in Dunhuang, Xi’an, and Changyang, respectively.

In order to simplify the threshold determination, reduce the inter-pixel cross-talk, and improve the storage density for high-density volume holographic data storage, a two-dimensional constant-weight sparse modulation code is proposed. The evaluation criteria and design rules are investigated based on the page-oriented optical data storage system. Coding parameters are optimized to achieve large channel capacities. An 8:16 modulation code is designed to reduce the raw bit error rate and its performances are experimentally evaluated. A raw bit error rate of the magnitude of 10^?4 is obtained with a single-data-page storage and 10^?3 with multiplexing.

Optical proximity correction (OPC) is a key step in modern integrated circuit (IC) manufacturing. The quality of model-based OPC (MB-OPC) is directly determined by segment offsets after OPC processing. However, in conventional MB-OPC, the intensity of a control site is adjusted only by the movement of its corresponding segment; this scheme is no longer accurate enough as the lithography process advances. On the other hand, matrix MB-OPC is too time-consuming to become practical. In this paper, we propose a new sparse matrix MB-OPC algorithm with model-based mapping between segments and control sites. We put forward the concept of ‘sensitive area’. When the Jacobian matrix used in the matrix MB-OPC is evaluated, only the elements that correspond to the segments in the sensitive area of every control site need to be calculated, while the others can be set to 0. The new algorithm can effectively improve the sparsity of the Jacobian matrix, and hence reduce the computations. Both theoretical analysis and experiments show that the sparse matrix MB-OPC with model-based mapping is more accurate than conventional MB-OPC, and much faster than matrix MB-OPC while maintaining high accuracy.