Frequency-hopping sequence (FHS) sets with low-hit-zone (LHZ) have Hamming correlations maintained at a low level as long as the relative time delay between different sequences are limited in a zone around the origin, and thus can be well applied in quasi-synchronous (QS) frequency-hopping multiple-access (FHMA) systems to reduce the mutual interference between different users. Moreover, the periodic partial Hamming correlation (PPHC) properties of employed LHZ-FHS sets usually act as evaluation criterions for the performances of QS-FHMA systems in practice. In this letter, a new class of LHZ-FHS sets is constructed via interleaving techniques. Furthermore, these new LHZ-FHS sets also possess optimal PPHC properties and parameters not included in the related literature.

Automatic location identification is required for emergency calls in Japan and many other countries. However, promptly providing a reliable position is a problem in places where the global positioning system (GPS) signal is extremely weak. We propose an acquisition scheme for the assisted GPS (AGPS) architecture based on a timing-synchronized mobile network. System errors as well as code uncertainty range and frequency uncertainty range are discussed. With this method, the acquisition search range is significantly reduced, and a long-time coherent correlation is made possible. Simulations and experiments prove that the method is fast, efficient and power-saving for the user handset. Furthermore, the AGPS receiver can perform a short TTFF (Time To First Fix) start even with no ephemeris kept in the handset.

Frequency-hopping sequence (FHS) sets with low-hit-zone (LZH) can be well applied in quasi-synchronous (QS) frequency-hopping multiple-access (FHMA) systems to reduce the mutual interference among different users. On the other hand, LHZ-FHS sets with wide-gap (WG) property can effectively resist the broadband blocking interference, the single frequency narrowband interference, the multipath fading and the tracking interference. In this letter, a new family of WG-LHZ-FHS sets is constructed. Besides, these new WG-LHZ-FHS sets possess optimal average periodic Hamming correlation (APHC) properties.

In this paper, we present an image compression algorithm using two concepts, subdividing an image matrix and stratifying submatrices into FD-submatrices (feature distribution submatrices). According to the feature distribution and the view that an image can be decomposed into some feature layers, we generate a compression tree by setting up a logic process of decomposition and stratification. To get better compression ratios, the set of submatrices having one and zero as elements, including logic flag sequences is compressed by vector space theory.

For frequency hopping spread spectrum communication systems, the average Hamming correlation (AHC) among frequency hopping sequences (FHSs) is an important performance indicator. In this letter, a sufficient and necessary condition for a set of FHSs with optimal AHC is given. Based on interleaved technique, a new construction for optimal AHC FHS sets is also proposed, which generalizes the construction of Chung and Yang. Several optimal AHC FHS sets with more flexible parameters not covered in the literature are obtained by the new construction, which are summarized in Table 1.

In order to evaluate the goodness of frequency hopping (FH) sequence design, the periodic Hamming correlation function is used as an important measure. But aperiodic Hamming correlation of FH sequences matters in real applications, while it received little attraction in the literature compared with periodic Hamming correlation. In this paper, the new aperiodic Hamming correlation lower bounds for FH sequences, with respect to the size of the frequency slot set, the sequence length, the family size, the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation are established. The new aperiodic bounds are tighter than the Peng-Fan bounds. In addition, the new bounds include the second powers of the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation but the Peng-Fan bounds do not include them. For the given sequence length, the family size and the frequency slot set size, the values of the maximum aperiodic Hamming autocorrelation and the maximum aperiodic Hamming crosscorrelation are inside of an ellipse which is given by the new aperiodic bounds.

Traffic sign detection is a valuable part of future driver support system. In this paper, we present a novel framework to accurately detect traffic signs from a single color image by analyzing geometrical, physical and text/symbol features of traffic signs. First, we utilize an elaborate edge detection algorithm to extract edge map and accurate edge pixel gradient information. Then, we extract 2-D geometric primitives (circles, ellipses, rectangles and triangles) efficiently from image edge map. Third, the candidate traffic sign regions are selected by analyzing the intrinsic color features, which are invariant to different illumination conditions, of each region circumvented by geometric primitives. Finally, a text and symbol detection algorithm is introduced to classify true traffic signs. Experimental results demonstrated the capabilities of our algorithm to detect traffic signs with respect to different size, shape, color and illumination conditions.

The construction of frequency hopping sequences with good Hamming correlation is the foundation of research in frequency hopping communication. In this letter, classes of optimal low hit zone frequency hopping sequence set are constructed based on the interleaving technology. The results of the study show that the sequence set with large family size is optimal for the Peng-Fan-Lee bound. And all the sequences in the set are inequivalent.

A partitioning parallelization of the multi-objective evolutionary algorithm based on decomposition, pMOEA/D, is proposed in this letter to achieve significant time reductions for expensive bi-objective optimization problems (BOPs) on message-passing clusters. Each sub-population of pMOEA/D resides on a separate processor in a cluster and consists of a non-overlapping partition and some extra overlapping individuals for updating neighbors. Additionally, sub-populations cooperate across separate processors by the hybrid migration of elitist individuals and utopian points. Experimental results on two benchmark BOPs and the wireless sensor network layout problem indicate that pMOEA/D achieves satisfactory performance in terms of speedup and quality of solutions on message-passing clusters.

In order to evaluate the goodness of frequency hopping sequence design, the aperiodic Hamming correlation function is used as an important measure. In this letter, the aperiodic Hamming correlation lower bounds for frequency hopping sequences with low hit zone which have not yet been reported previously are established.

In order to evaluate the goodness of frequency hopping sequence design, the periodic Hamming correlation function is used as an important measure. Usually, the length of correlation window is shorter than the period of the chosen frequency hopping sequence, so the study of the partial Hamming correlation of frequency hopping sequence is particularly important. In this paper, the maximum partial Hamming correlation lower bounds of frequency hopping sequences with low hit zone, with respect to the size of the frequency slot set, the length of correlation window, the family size, the low hit zone, the maximum partial Hamming autocorrelation and the maximum partial Hamming crosscorrelation are established. It is shown that the new bounds include the known Lempel-Greenberger bound, Peng-Fan bounds, Eun-Jin-Hong-Song bound and Peng-Fan-Lee bounds as special cases.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.