Three-dimensional tongue shape during vowel production is analyzed using the three-mode PARAFAC (parallel factors) model. Three-dimensional MRI images of five speakers (9 vowels) are analyzed. Sixty-five virtual fleshpoints (13 segments along the rostral-caudal dimension and 5 segments along the right-left direction) are chosen based on the interpolated tongue shape images. Methods used to adjust the alignment of MRI images, to set up the fleshpoints, and to measure the position of the fleshpoints are presented. PARAFAC analysis of this 3D coordinate data results in a stable two-factor solution that explains about 70% of the variance.

In this paper, the nonlinear dynamics of a novel model based on optically pumped spin-polarized vertical-cavity surface-emitting lasers (spin-VCSELs) with optical feedback is investigated numerically. Due to optical feedback being the external disturbance component, the complex nonlinear dynamical behaviors can be enhanced and the regions of different nonlinear dynamics in size can be extended with appropriate parameters of spin-VCSELs. According to the equations of the modified spin-flip model (SFM), the comparison of bifurcation diagrams is first presented for the clear presentation of different routes to chaos. Meanwhile, numerous bifurcation diagrams in color are illustrated to demonstrate the rich dynamical regimes intuitively, and the crucial effects of optical feedback strength, feedback delay, linewidth enhancement factor, and spin-flip relaxation rate on the region evolvement of complex dynamics of the proposed model are revealed to investigate the dependence of dynamical behaviors on external and internal parameters when the optical feedback scheme is introduced. These parameters play a remarkable role in enhancing the mechanism of complex dynamic oscillations. Furthermore, utilizing combination with time series, power spectra, and phase portraits, the various dynamical behaviors observed in the bifurcation diagram are simulated numerically. Correspondingly, the powerful measure 0–1 test is employed to distinguish between chaos and non-chaos.

The photoelectric hybrid network has been proposed to achieve the ultrahigh bandwidth, lower delay, and less power consumption for chip multiprocessor (CMP) systems. However, a large number of optical elements used in optical networks-on-chip (ONoCs) generate high transmission loss which will influence network performance severely and increase power consumption. In this paper, the Dijkstra algorithm is adopted to realize adaptive routing with minimum transmission loss of link and reduce the output power of the link transmitter in mesh-based ONoCs. The numerical simulation results demonstrate that the transmission loss of a link in optimized power control based on the Dijkstra algorithm could be maximally reduced compared with traditional power control based on the dimensional routing algorithm. Additionally, it has a greater advantage in saving the average output power of optical transmitter compared to the adaptive power control in previous studies, while the network size expands. With the aid of simulation software OPNET, the network performance simulations in an optimized network revealed that the end-to-end (ETE) latency and throughput are not vastly reduced in regard to a traditional network. Hence, the optimized power control proposed in this paper can greatly reduce the power consumption of s network without having a big impact on network performance.

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Vehicle Re-identification (Re-ID) refers to finding the same vehicle shot by other cameras from a given vehicle image library, which can also be regarded as a sub-problem of image retrieval. It plays an important role in intelligent transportation and smart cities. The key of vehicle Re-ID is to extract discriminative vehicle features. To better extract such features from the vehicle image to improve the recognition accuracy, we propose a three-branch adaptive attention network-Global Relational Attention and Multi-granularity Feature Learning (GRMF) to improve feature representation and discrimination. First, we divide the network into three branches, extracting different and useful features from three perspectives: spatial location, channel information, and local information. Second, we propose two effective global relational attention modules, which capture the global structural information for better attention learning. Specifically, to determine the importance level of a node, we use the global relationship between the node and all other nodes to infer the attention weight of the node directly. Third, according to the characteristics of the vehicle re-identification task, we introduce a suitable local partition strategy. It not only can simply capture subtle local information but also solve the problem of misalignment and within-part consistency disruption to a great extent. Extensive experiments demonstrate the effectiveness of our approach, and we achieve state-of-the-art results on two challenging datasets, including VeRi776 and VehicleID.

We present a novel hardware device that combines a regular microphone with a bone-conductive microphone. The device looks like a regular headset and it can be plugged into any machine with a USB port. The bone-conductive microphone has an interesting property: it is insensitive to ambient noise and captures the low frequency portion of the speech signals. Thanks to the signals from the boneconductive microphone, we are able to detect very robustly whether the speaker is talking, eliminating more than 90% of background speech. Furthermore, by combining both channels, we are able to significantly remove background speech even when the background speaker speaks at the same time as the speaker wearing the headset.

Optical networks-on-chips (ONoCs) is an effective and extensible on-chip communication technology, which has the characteristics of high bandwidth, low consumption, and low delay. In the design process of ONoCs, power loss is an important factor for limiting the scalability of ONoCs. Additionally, the optical signal-to-noise ratio (OSNR) is an index to measure the quality of ONoCs. Nowadays, the routing algorithm commonly used in ONoCs is the dimension-order routing algorithm, but the routing paths selected by the algorithm have high power loss and crosstalk noise. In this paper, we propose a 5×5 all-pass optical router model for two-dimensional (2-D) mesh-based ONoCs. Based on the general optical router model and the calculation models of power loss and crosstalk noise, a novel algorithm is proposed in ordder to select the routing paths with the minimum power loss. At the same time, it can ensure that the routing paths have the approximately optimal OSNR. Finally, we employ the Cygnus optical router to verify the proposed routing algorithm. The results show that the algorithm can effectively reduce the power loss and improve the OSNR in the case of network sizes of 5×5 and 6×6. With the increase of the optical network scale, the algorithm can perform better in reducing the power loss and raising the OSNR.

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