An improved iterative minimum mean-squared error (MMSE) channel estimation method is proposed for joint coding and precoding OFDM systems. Compared with the traditional simplified estimator, the proposed scheme provides higher estimation quality with slight complexity increment at low signal-to-noise ratio (SNR) values. The performance of the iterative receiver including the proposed estimator approaches that of the perfect MMSE estimator without any simplification.

Ultra-wideband (UWB) beamforming is now attracting significant research attention for attaining spatial gain from array antennas. It is commonly believed that directional antenna based communication could improve the system performance. In order to further make clear the relationship between system performance and the antenna array beamforming, UWB indoor channels are extracted from practical measurements and circular horn antenna is used to characterize the channel properties and to evaluate the system performance. Using a single beam directional antenna with a certain half power beamwidth (HPBW), the channel capacity and the bit-error-rate (BER) performance of a UWB RAKE receiver are evaluated. In the line-of-sight (LOS) environments, the channel capacity and BER performance are improved with the beamwidth becoming smaller. However in the non-line-of-sight (NLOS) environments, the capacity and BER performance are not always better with directional antennas. And the variation trend between the system performance and the antenna beamwidth disappears. This is mainly because that there exist no dominant strong path components like those seen in LOS environments. Then beam combining is introduced to further improve the system performance. Simulation results show that the channel capacity and BER performance cloud be greatly improved by multiple beam combining, especially for the NLOS environments. This reminds us that when antenna beamforming is used to obtain array gain, the beamwidth should be carefully designed and beam combining is necessary to obtain optimal performance, especially in NLOS environments.

Class imbalance has drawn much attention of researchers in software defect prediction. In practice, the performance of defect prediction models may be affected by the class imbalance problem. In this paper, we present an approach to evaluating the performance stability of defect prediction models on imbalanced datasets. First, random sampling is applied to convert the original imbalanced dataset into a set of new datasets with different levels of imbalance ratio. Second, typical prediction models are selected to make predictions on these new constructed datasets, and Coefficient of Variation (C·V) is used to evaluate the performance stability of different models. Finally, an empirical study is designed to evaluate the performance stability of six prediction models, which are widely used in software defect prediction. The results show that the performance of C4.5 is unstable on imbalanced datasets, and the performance of Naive Bayes and Random Forest are more stable than other models.

An improved soft-input soft-output (SISO) minimum mean-squared error (MMSE) detection method is proposed for joint coding and precoding OFDM systems under imperfect channel estimation. Compared with the traditional mismatched detection which uses the channel estimate as its exact value, the signal model of the proposed detector is more accurate and the influence of channel estimation error (CEE) can be effectively mitigated. Simulations indicate that the proposed scheme can improve the bit error rate (BER) performance with fewer pilot symbols.

A new type of compact one dimension (1-D) microstrip photonic bandgap (PBG) structure for filter is presented. A miniature semiconductor-based structure band-stop filter with four cells is simulated, fabricated, and measured. Agreement between the experimental and simulation results has been achieved. The filter with four proposed PBG structure exhibits deep (about -60 dB) and steep (about 40 dB/GHz) stop-band characteristics. It also has less loss and ripples in the pass-band. The period of the PBG lattice is about 0.2 λe (λe, guiding wavelength at the center frequency of stop-band), or 0.068 λ0 (λ0 wavelength in air), and the filter is very compact and much easier for fabrication and realization in MIC and MMIC.

Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted superpixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.

In this letter, a simple dispersion matrix design method for generalized space-time shift keying is presented, in which the dispersion matrices are systematically constructed with cyclic identity matrix, without the need of computer search. The proposed scheme is suitable for any number of transmit antennas greater than two, and can achieve the transmit diversity order of two except two special cases. Simulation results are presented to verify our theoretical analysis and the performance of the proposed scheme.

Fault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

Personalized news recommendation is becoming increasingly important for online news platforms to help users alleviate information overload and improve news reading experience. A key problem in news recommendation is learning accurate user representations to capture their interest. However, most existing news recommendation methods usually learn user representation only from their interacted historical news, while ignoring the clustering features among users. Here we proposed a hierarchical user preference hash network to enhance the representation of users' interest. In the hash part, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically. We also learn representations of users from their browsed news in history part. And then, a Route Attention is adopted to combine these two parts (history vector and hash vector) and get the more informative user preference vector. As for news representation, a modified transformer with category embedding is exploited to build news semantic representation. By comparing the hierarchical hash network with multiple news recommendation methods and conducting various experiments on the Microsoft News Dataset (MIND) validate the effectiveness of our approach on news recommendation.

Wearing a mask correctly is an effective method to prevent respiratory infectious diseases. Correct mask use is a reliable approach for preventing contagious respiratory infections. However, when dealing with mask-wearing in some complex settings, the detection accuracy still needs to be enhanced. The technique for mask-wearing detection based on YOLOv7-Tiny is enhanced in this research. Distribution Shifting Convolutions (DSConv) based on YOLOv7-tiny are used instead of the 3×3 convolution in the original model to simplify computation and increase detection precision. To decrease the loss of coordinate regression and enhance the detection performance, we adopt the loss function Intersection over Union with Minimum Points Distance (MPDIoU) instead of Complete Intersection over Union (CIoU) in the original model. The model is introduced with the GSConv and VoVGSCSP modules, recognizing the model’s mobility. The P6 detection layer has been designed to increase detection precision for tiny targets in challenging environments and decrease missed and false positive detection rates. The robustness of the model is increased further by creating and marking a mask-wearing data set in a multi environment that uses Mixup and Mosaic technologies for data augmentation. The efficiency of the model is validated in this research using comparison and ablation experiments on the mask dataset. The results demonstrate that when compared to YOLOv7-tiny, the precision of the enhanced detection algorithm is improved by 5.4%, Recall by 1.8%, mAP@.5 by 3%, mAP@.5:.95 by 1.7%, while the FLOPs is decreased by 8.5G. Therefore, the improved detection algorithm realizes more real-time and accurate mask-wearing detection tasks.

With the widespread popularity of a large number of social platforms, an increasing number of new words gradually appear. However, such new words have made some NLP tasks like word segmentation more challenging. Therefore, new word detection is always an important and tough task in NLP. This paper aims to extract new words using the BiLSTM+CRF model which added some features selected by us. These features include word length, part of speech (POS), contextual entropy and degree of word coagulation. Comparing to the traditional new word detection methods, our method can use both the features extracted by the model and the features we select to find new words. Experimental results demonstrate that our model can perform better compared to the benchmark models.

Based on the periodical-loaded principle, a new wider stop-band filter is presented. The design equations are provided, the validity of which is proved by the measured results. Compared with loaded stub of length 1/4λg, the improved T-shape stub can change admittance paralleled with microstrip line and widen the band width of the band-stop filter. The size of the filter loaded by one side can be reduced by 2/3. The stop-band filter loaded by one side and two sides are simulated and realized. The filter loaded by two sides can achieve very wide stop-band. In addition, the stop-band of the new type of filter is deep and steep.