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When adopting transmit diversity in the Chinese digital television terrestrial broadcasting (DTTB) system, there exists two kinds of interference: training sequences (TSs) from different transmit antennas are overlapped; and the TSs are interfered with OFDM data due to multipath fading. In order to distinguish different TSs, this paper proposes a method to design orthogonal TS set, which also have good correlation characteristic. Subsequently, an iterative algorithm to eliminate the interference between TSs and data is proposed and theoretically analyzed. Finally, simulations are performed and the results show that the system with the proposed scheme can achieve great diversity gain under acceptable computational complexity. [ABSTRACT FROM PUBLISHER]

Automatic dialect classification has emerged as an important area in the speech research field. Effective dialect classification is useful in developing robust speech systems, such as speech recognition and speaker identification. In this paper, two novel algorithms are proposed to improve dialect classification for text-independent spontaneous speech in Arabic and Spanish languages, along with probe results for Chinese. The problem considers the case where no transcripts but dialect labels are available for training and test data, and speakers are speaking spontaneously, which is defined as text-independent dialect classification. The Gaussian mixture model (GMM) is used as the baseline system for text-independent dialect classification. The major motivation is to suppress confused/distractive regions from the dialect language space and emphasize discriminative/sensitive information of the available dialects. In the training phase, a symmetric version of the Kullback–Leibler divergence is used to find the most discriminative GMM mixtures (KLD-GMM), where the confused acoustic GMM region is suppressed. For testing, the more discriminative frames are detected and used via the location of where the frames are in the GMM mixture feature space, which is termed frame selection decoding (FSD-GMM). The first KLD-GMM and second FSD-GMM techniques, are shown to improve dialect classification performance for three-way dialect tasks. The two algorithms and their combination are evaluated on dialects of Arabic and Spanish corpora. Measurable improvement is achieved in both two cases, over a generalized maximum-likelihood estimation GMM baseline (MLE-GMM). [ABSTRACT FROM PUBLISHER]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]