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This study introduces culture relatedness of words as a novel variable and explores its impact on emotional responses of English-Mandarin bilinguals living in the UK, where their second language (L2) is dominant. First, we conducted a norming study to identify emotive words related to participants' native (e.g., bamboo) and residential (e.g., scones) cultures. We then used event-related potentials (ERPs) to examine whether culture relatedness affects emotional responses to words presented in L1 and L2. We were particularly interested in investigating whether the well-established emotional distance from L2 may be due to cultural distance, and whether concepts related to one's native culture in L2 may enhance affective responses. Initial evidence from ongoing data analyses seems to suggest an interaction of culture relatedness and emotional valance on affective responses. This research offers new insights into the interplay of language, culture, and emotion in bilingual contexts, examining how cultural salience modulates emotional responses.

Airborne aircrafts are dependent on the Global Navigation Satellite System (GNSS), which is susceptible to interference due to the satellite base-station and cooperative communication. Synthetic aperture radar altimeter (SARAL) provides the ability to measure the topographic terrain for matching with Digital Elevation Model (DEM) to achieve positioning without relying on GNSS. However, due to the near-vertical coupling in the delay-Doppler map (DDM), the similarity of DDMs of adjacent apertures is high, and the probability of successful matching is low. To this end, a novel neural network of terrain matching and aircraft positioning is proposed based on the airborne SARAL imagery. The model-driven terrain matching and aircraft positioning network (TMP-Net) is capable of realizing aircraft positioning by utilizing the real-time DDMs to match with the DEM-based DDM references, which are generated by a point-by-point coupling mechanism between the airborne routine and ground terrain DEM. Specifically, the training dataset is established by a numerical simulation method based on a semi-analytical model. Therefore, DEM-based DDM references can be generated by forward deduction when only regional DEM can be obtained. In addition to the model-based DDM generation, feature extraction, and similarity measurement, an aircraft positioning module is added. Three different positioning methods are designed to achieve the aircraft positioning, where three-point weighting exhibits the best performance in terms of positioning accuracy. Due to the fact that both the weighted triplet loss and softmax loss are employed in a cooperative manner, the matching accuracy can be improved and the positioning error can be reduced. Finally, both simulated and measured airborne datasets are used to validate the effectiveness of the proposed algorithm. Quantitative and qualitative evaluations show the superiority. [ABSTRACT FROM AUTHOR]

To address the problem of target detection in distributed MIMO radar, three styles of distributed MIMO radar systems are given in this paper. With respect to the geometric distribution relations of distributed MIMO radar, the styles are distributed coherent MIMO radar, random phase MIMO radar, and random amplitude-phase MIMO radar. Next, the square law detector structures of random phase and random amplitude-phase MIMO radar are derived in the paper when there is a low signal-to-noise ratio, and the performance of the two detectors are analyzed as well. Finally, simulation results demonstrate the theoretical analysis of this paper are of guiding significance for the actual engineering.

Only a subset of transmitters and receivers in a distributed Multi-Input Multi-Output (MIMO) radar network is allowed to actively track a target at a particular instance due to the limited time and energy resource of a MIMO radar network. It is therefore desirable to obtain an efficient method to overcome the resource constraints while optimizing the tracking performance. In this study, posterior Cramer-Rao lower bound is used as the performance metric and the selection problem is formulated as a Boolean programming problem aiming at optimizing the worst tracking performance of multiple targets. It is later relaxed to a semidefinite programming and solved by the block coordinate descend method. Numerical results show that proposed method superior to the fixed selection method. In addition, with less computation complexity, the proposed method obtains nearly equivalent performance compared with exhaustive search method.

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel accurate array response control algorithm, abbreviated as \text A^2\text RC, and its application to array pattern synthesis. The proposed \text A^2\text RC algorithm deals with the problem of how to accurately control the array response at a given direction. Starting from the adaptive array theory, a deep analysis of the optimal weight vector is carried out. It is found that the normalized response at a given direction can be accurately adjusted to an arbitrary level, by means of making some simple modification to the initial weight vector. On this basis, all possible weight vectors, which have a specific form and can make the normalized response at the given direction equal to the prescribed value, are first figured out. Then, an effective approach to selecting the most appropriate one, which would cause the least pattern distortion, is devised. By applying the \text A^2\text RC algorithm, a new pattern synthesis approach for arbitrary arrays is developed. In this approach, the array pattern is adjusted in a point-by-point manner by successively modifying the weight vector. Contrary to the conventional approaches that assign artificial interferences in an ad hoc way, our approach is able to obtain the weight vector without iteratively determining the powers of the artificial interferences. Extensive simulation results are provided to demonstrate the performance of the \text A^2\text RC algorithm in array response control and the effectiveness of this algorithm in pattern synthesis under various situations. [ABSTRACT FROM PUBLISHER]

BackgroundCrisis situations bring about many challenges to researchers, public institutions, and governments in collecting data and conducting research in affected individuals. Recent developments in Web-based and smartphone technologies have offered government and nongovernment organizations a new system to disseminate and acquire information. However, research into this area is still lacking. The current study focuses largely on how new social networking websites and, in particular, smartphone technologies could have helped in the acquisition of crucial research data from the general population during the recent 2013 Southeast Asian Haze. This crisis lasted only for 1 week, and is unlike other crisis where there are large-scale consequential after-effects. ObjectiveTo determine whether respondents will make use of Internet, social media, and smartphone technologies to provide feedback regarding their physical and psychological wellbeing during a crisis, and if so, will these new mechanisms be as effective as conventional, technological, Internet-based website technologies. MethodsA Web-based database and a smartphone application were developed. Participants were recruited by snowball sampling. The participants were recruited either via a self-sponsored Facebook post featuring a direct link to the questionnaire on physical and psychological wellbeing and also a smartphone Web-based application; or via dissemination of the questionnaire link by emails, directed to the same group of participants. Information pertaining to physical and psychological wellbeing was collated. ResultsA total of 298 respondents took part in the survey. Most of them were between the ages of 20 to 29 years and had a university education. More individuals preferred the option of accessing and providing feedback to a survey on physical and psychological wellbeing via direct access to a Web-based questionnaire. Statistical analysis showed that demographic variables like age, gender, and educational levels did not influence the mechanism of access. In addition, the participants reported a mean number of 4.03 physical symptoms (SD 2.6). The total Impact of Event Scale–Revised (IES-R) score was 18.47 (SD 11.69), which indicated that the study population did experience psychological stress but not post-traumatic stress disorder. The perceived dangerous Pollutant Standards Index (PSI) level and the number of physical symptoms were associated with higher IES-R Score (P