Furthermore, we introduce a simple but very effective center prior in designing the learning cost function of the DNN by attaching high importance to the errors around the image center. We also present extensive experimental results on four commonly used public databases to demonstrate the superiority of the proposed method over classical and state-of-the-art methods on various evaluation metrics.Recent progress in vision-based fire detection is driven by convolutional neural networks. However, the existing methods fail to achieve a good tradeoff among accuracy, model size, and speed. In this paper, we propose an accurate fire detection method that achieves a better balance in the abovementioned aspects. Specifically, a multiscale feature extraction mechanism is employed to capture richer spatial details, which can enhance the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism is utilized to enhance the interaction among information flows through dense skip connections. Finally, a channel attention mechanism is employed to selectively emphasize the contribution between different feature maps. Experimental results demonstrate that our method achieves 95.3% accuracy, which outperforms the suboptimal method by 2.5%. Moreover, the speed and model size of our method are 3.76% faster on the GPU and 63.64% smaller than the suboptimal method, respectively.The goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical colocalization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pretrained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. https://www.selleckchem.com/products/lixisenatide.html Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on finegrained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly.The recent development of high-frame-rate (HFR) imaging/Doppler methods based on the transmission of plane or diverging waves, has proposed new challenges to echographic data management and display. Due to the huge amount of data that need to be processed at very high speed, the pulse repetition frequency (PRF) is typically limited to hundreds Hz or few kHz. In Doppler applications, a PRF limitation may result unacceptable since it inherently translates to a corresponding limitation in the maximum detectable velocity. In this paper, the ULA-OP 256 implementation of a novel ultrasound modality, called virtual real-time (VRT), is described. First, for a given HFR real-time modality, the scanner displays the processed results while saving channel data into an internal buffer. Then, ULA-OP 256 switches to VRT mode, according to which the raw data stored in the buffer are immediately re-processed by the same hardware used in real-time. In the two phases, the ULA-OP 256 calculation power can be differently distributed to increase the acquisition frame rate or the quality of processing results. VRT was here used to extend the PRF limit in a multi-line vector Doppler application. In real-time, the PRF was maximized at the expense of the display quality; in VRT, data were reprocessed at a lower rate in a high-quality display format, which provides more detailed flow information. Experiments are reported in which the multi-line vector Doppler technique is shown capable of working at 16 kHz PRF, so that flow jet velocities higher up to 3 m/s can be detected.In an adhesively bonded structure, utilizing the adhesive itself for monitoring the joint integrity can be beneficial in reduction of labor, time and potential human errors while avoiding problems associated with introduction of a foreign sensor component. This work started from the examination of effective piezoelectricity of commercial structural adhesives/sealants, and five of them were found to possess effective piezoelectric property, with effective piezoelectric coefficient d33 from -0.11 to -1.77 pm/V depending on frequency under substrate clamping condition. With stable piezoelectric response at least up to MHz, an epoxy adhesive with inorganic filler was selected for SHM feasibility demonstration via generating or sensing guided ultrasonic Lamb waves. The presence of disbond in the adhesive joint is detectable by comparing the Lamb waves signal with a reference baseline signal associated with an intact structure. The results show that the selected adhesive with piezoelectric response can perform the dual roles of structural bonding and ultrasonic joint integrity monitoring.Ultrasonography and photoacoustic tomography provide complementary contrasts in preclinical studies, disease diagnoses, and imaging-guided interventional procedures. Here, we present a video-rate (20 Hz) dual-modality ultrasound and photoacoustic tomographic platform that has a high resolution, rich contrasts, deep penetration, and wide field of view. A three-quarter ring-array ultrasonic transducer is used for both ultrasound and photoacoustic imaging. Plane-wave transmission/receiving approach is used for ultrasound imaging, which improves the imaging speed by nearly two folds and reduces the RF data size compared with the sequential single-channel scanning approach. GPU-based image reconstruction is developed to advance computational speed. We demonstrate fast dual-modality imaging in phantom, mouse, and human finger joint experiments. The results show respiration motion, heart beating, and detailed features in the mouse internal organs. To our knowledge, this is the first report on fast plane-wave ultrasound imaging and single-shot photoacoustic computed tomography in a ring-array system.