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Increasing the Educational Weather of an R1 STEM Section: Quantified Optimistic Adjustments within Notion.
Improved accuracy of the resulting estimate is achieved through the joint inter-fragment estimation of the APE gradient. The proposed algorithm, based on the mentioned scheme, was validated via computer simulations. The conducted experiments confirmed its preference against the existing techniques. The preference is particularly distinct for low SNR imagery.This paper presents a novel optimized quantization constraint set, acting as an add-on to existing DCT-based image restoration algorithms. The constraint set is created based on generalized Gaussian distribution which is more accurate than the commonly used uniform, Gaussian or Laplacian distributions when modeling DCT coefficients. More importantly, the proposed constraint set is optimized for individual input images and thus it is able to enhance image quality significantly in terms of signal-to-noise ratio. Experimental results indicate that the signal-to-noise ratio is improved by at least 6.78% on top of the existing state-of-the-art methods, with a corresponding expense of only 0.38% in processing time. The proposed algorithm has also been implemented in GPU, and the processing speed increases further by 20 times over that of CPU implementation. This makes the algorithm well suited for fast image retrieval in security and quality monitoring system.A key for person re-identification is achieving consistent local details for discriminative representation across variable environments. Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment. This paper proposes a receptive multi-granularity learning approach to facilitate stripe-based feature learning. This approach performs local partition on the intermediate representations to operate receptive region ranges, rather than current approaches on input images or output features, thus can enhance the representation of locality while remaining proper local association. Toward this end, the local partitions are adaptively pooled by using significance-balanced activations for uniform stripes. E-64 cost Random shifting augmentation is further introduced for a higher variance of person appearing regions within bounding boxes to ease misalignment. By twobranch network architecture, different scales of discriminative identity representation can be learned. In this way, our model can provide a more comprehensive and efficient feature representation without larger model storage costs. Extensive experiments on intra-dataset and cross-dataset evaluations demonstrate the effectiveness of the proposed approach. Especially, our approach achieves a state-of-the-art accuracy of 96.2%@Rank-1 or 90.0%@mAP on the challenging Market-1501 benchmark.We demonstrate time and frequency transfer using a White Rabbit time transfer system over millimeter-wave (mmwave) 71-76 GHz carriers. To validate the performance of our system, we present overlapping Allan deviation, time deviation, and phase statistics. Over mm-wave carriers, we report an ADEV of 71 × 10-12 at 1 second and a TDEV of less then 10 picoseconds at 10 000 seconds. Our results show that after 4 seconds of averaging we have sufficient precision to transfer a cesium atomic frequency standard. We analyze the link budget and architecture of our mm-wave link and discuss possible sources of phase error and their potential impact on the White Rabbit frequency transfer. Our data shows that White Rabbit can synchronize new network architectures, such as physically separated fiber-optic networks and support new applications such as the synchronization of intermittently connected platforms. We conclude with recommendations for future investigation including cascaded hybrid wireline and wireless architectures.This paper presents a comprehensive guide to co-design lithium niobate (LiNbO3) lateral overtone bulk acoustic resonators (LOBARs) and voltage-controlled oscillators (VCOs) using discrete components on a printed circuit board (PCB). The analysis focuses on understanding the oscillator level tradeoffs between the number of locked tones, frequency stability, tuning range, power consumption, and phase noise. Moreover, the paper focuses on understanding the relationship between the above specifications and the different LOBAR parameters such as electromechanical coupling (kt2), quality factor (Q), transducer design and the resonator size. As a result of this study, the first voltage-controlled MEMS oscillator (VCMO) based on LiNbO3 LOBAR is demonstrated. Our LOBAR excites over 30 resonant modes in the range of 100 to 800 MHz with a frequency spacing of 20 MHz. The VCMO consists of a LOBAR in a closed loop with 2 amplification stages and a varactor-embedded tunable LC tank. By adjusting the bias voltage applied to the varactor, the tank can be tuned to change the closed-loop gain and phase responses of the oscillator so that the Barkhausen conditions are satisfied for a particular resonant mode. The tank is designed to allow the proposed VCMO to lock to any of the ten overtones ranging from 300 to 500 MHz. These ten tones are characterized by average Qs of 2100, kt2 of 1.5%, figure-of-merit (FOM = Q · kt2) of 31.5 enabling low phase noise, and low power oscillators crucial for internet-of-things (IoT). Owing to the high Qs of the LiNbO3 LOBAR, the measured VCMO shows a close-in phase noise of -100 dBc/Hz at 1 kHz offset from a 300 MHz carrier and a noise floor of -153 dBc/Hz while consuming 9 mW. With further optimization, this VCMO can lead to direct radio frequency (RF) synthesis for ultra-low-power transceivers in multi-mode IoT nodes.Novel pulsed-Doppler methods for perfusion imaging are validated using dialysis cartridges as perfusion phantoms. Techniques that were demonstrated qualitatively at 24 MHz, in vivo [18], are here examined quantitatively at 5 and 12.5 MHz using phantoms with blood-mimicking fluid flow within cellulose microfibers. One goal is to explore a variety of flow states to optimize measurement sensitivity and flow accuracy. The results show that 2-3 s echo acquisitions at roughly 10 frames/s yields the highest sensitivity to flows 1-4 mL/min. A second goal is to examine methods for setting the parameters of higher-order singular value decomposition (HOSVD) clutter filters. For stationary or moving clutter, the velocity of bloodmimicking fluid in the microfibers is consistently estimated within measurement uncertainty (mean coeff of variation = 0.26). Power Doppler signals were equivalent for stationary and moving clutter after clutter filtering, increasing approximately 3 dB per mL/min of blood-mimicking fluid flow for 0≤ q≤ 4 mL/min. Comparisons between phantom and preclinical images show that peripheral perfusion imaging can be reliably achieved without contrast enhancement.This paper aims to develop a semi-noncontact stress-sensing system employing a laser-generated ultrasound wave assisted by candle soot nanoparticle (CSNP) composite. While the acoustoelastic effect is commonly targeted to measure stress level, efforts to combine it with the laser-generated ultrasound wave signal have been lacking due to weak signal intensity. In this study, the CSNP-based transducer is designed to potentiate the photoacoustic energy conversion. To demonstrate the wave propagation with the designed parameters, a numerical simulation was first conducted. Experiment results showed that a laser intensity of 6.5 mJ/cm2 was enough to generate the SSL wave from the CSNP composite transducer. The normal beam projection is the most effective wave-generation method, exhibiting the highest signal magnitude compared with inclined projection cases. Finally, the laser-assisted stress-sensing system was assessed by increasing the internal pressure of an air tank. The sensitivity of the developed sensor system was estimated to be 0.296 ns/MPa, showing a correlation of 0.983 with the theoretical prediction. The proposed sensing system can be used to monitor the structural integrity of nuclear power plants.Breast arterial calcifications (BACs) are part of several benign findings present on some mammograms. Previous studies have indicated that BAC may provide evidence of general atherosclerotic vascular disease, and potentially be a useful marker of cardiovascular disease (CVD). Currently, there is no technique in use for the automatic detection of BAC in mammograms. Since a majority of women over the age of 40 already undergo breast cancer screening with mammography, detecting BAC may offer a method to screen women for CVD in a way that is effective, efficient, and broad reaching, at no additional cost or radiation. In this paper, we present a deep learning approach for detecting BACs in mammograms. Inspired by the promising results achieved using the U-Net model in many biomedical segmentation problems and the DenseNet in semantic segmentation, we extend the U-Net model with dense connectivity to automatically detect BACs in mammograms. The presented model helps to facilitate the reuse of computation and improve the flow of gradients, leading to better accuracy and easier training of the model. We evaluate the performance using a set of full-field digital mammograms collected and prepared for this task from a publicly available dataset. Experimental results demonstrate that the presented model outperforms human experts as well as the other related deep learning models. This confirms the effectiveness of our model in the BACs detection task, which is a promising step in providing a cost-effective risk assessment tool for CVD.The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems.Transcranial MRI-guided focused ultrasound (TcMRgFUS) thermal ablation is a noninvasive functional neurosurgery technique. Previous reports have shown that damage in the skull bone marrow can occur at high acoustic energies. While this damage is asymptomatic, it would be desirable to avoid it. Here we examined whether acoustic and thermal simulations can predict where the thermal lesions in the marrow occurred. Post-treatment imaging was obtained at 3-15 months after 40 clinical TcMRgFUS procedures, and bone marrow lesions were observed after 16 treatments. The presence of lesions was predicted by the acoustic energy with a threshold of 18.1-21.1 kJ (maximum acoustic energy used) and 97-112 kJ (total acoustic energy applied over the whole treatment). The size of the lesions was not always predicted by the acoustic energy used during treatment alone. In contrast, the locations, sizes, and shapes of the heated regions estimated by the acoustic and thermal simulations were qualitatively similar to those of the lesions.
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