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Quick Report: The Benefits of a good Employer-Based Work-Experience Program regarding Members along with ASD.
Experimental results demonstrate our network achieves peak signal-to-noise ratio improvement, 0.55 - 0.69 dB compared with the compressed videos at different quantization parameters, outperforming state-of-the-art approach.Aerial scene recognition is challenging due to the complicated object distribution and spatial arrangement in a large-scale aerial image. Recent studies attempt to explore the local semantic representation capability of deep learning models, but how to exactly perceive the key local regions remains to be handled. In this paper, we present a local semantic enhanced ConvNet (LSE-Net) for aerial scene recognition, which mimics the human visual perception of key local regions in aerial scenes, in the hope of building a discriminative local semantic representation. Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer. Firstly, we design a multi-scale dilated convolution operators to fuse multi-level and multi-scale convolutional features in a trainable manner in order to fully receive the local feature responses in an aerial scene. Then, these features are fed into our two-branch local semantic perception module. In this module, we design a context-aware class peak response (CACPR) measurement to precisely depict the visual impulse of key local regions and the corresponding context information. Also, a spatial attention weight matrix is extracted to describe the importance of each key local region for the aerial scene. Finally, the refined class confidence maps are fed into the classification layer. Exhaustive experiments on three aerial scene classification benchmarks indicate that our LSE-Net achieves the state-of-the-art performance, which validates the effectiveness of our local semantic perception module and CACPR measurement.In the contemporary era of Internet-of-Things, there is an extensive search for competent devices which can operate at ultra-low voltage supply. Due to the restriction of power dissipation, a reduced sub-threshold swing based device appears to be the perfect solution for efficient computation. To counteract this issue, Negative Capacitance Fin field-effect transistors (NC-FinFETs) came up as the next generation platform to withstand the aggressive scaling of transistors. The ease of fabrication, process-integration, higher current driving capability and ability to tailor the short channel effects (SCEs), are some of the potential advantages offered by NC-FinFETs, that attracted the attention of the researchers worldwide. The following review emphasizes about how this new state-of-art technology, supports the persistence of Moore's law and addresses the ultimate limitation of Boltzmann tyranny, by offering a sub-threshold slope (SS) below 60 mV/decade. The article primarily focuses on two parts-i) the theoretical background of negative capacitance effect and FinFET devices and ii) the recent progress done in the field of NC-FinFETs. It also highlights about the crucial areas that need to be upgraded, to mitigate the challenges faced by this technology and the future prospects of such devices.Acoustic radiation force impulse (ARFI) has been widely used in transient shear wave elasticity imaging (SWEI). For SWEI based on focused ARFI, the highest image quality exists inside the focal zone due to the limitation of depth of focus and diffraction. Consequently, the areas outside the focal zone and in the near field present poor image quality. To address the limitations of focused beam, we introduce Bessel apodized ARFI which enhances image quality and improves depth of focus. The objective of this study is to evaluate the feasibility of SWEI based on Bessel ARF in simulation and experiment. We report measurements of elastogram image quality and depth of field in tissue-mimicking phantoms and ex-vivo liver tissue. Our results demonstrate improved depth of field, image quality, and shear wave speed (SWS) estimation accuracy using Bessel push beams. As a result, Bessel ARF enlarges the field of view of elastograms. The signal-to-noise ratio (SNR) of Bessel SWEI is improved 26% compared with focused SWEI in homogeneous phantom. The estimated SWS by Bessel SWEI is closer to the measured SWS from a clinical scanner with an error of 0.3% compared to 2.4% with focused beam. In heterogeneous phantoms, the contrast-to-noise ratios (CNR) of shallow and deep inclusions are improved by 8.79 dB and 3.33 dB, respectively, under Bessel ARF. We also compare the results between Bessel SWEI and supersonic shear imaging (SSI), the SNR of Bessel SWEI is improved by 8.1%. check details Compared with SSI, Bessel SWEI shows more accurate SWS estimates in high stiffness inclusions. Lastly, Bessel SWEI can generate higher quality elastograms with less energy than conventional SSI.
This paper presents a Proof-of-Concept (POC) design and implementation of a biosensing and communication system that can be used for biotelemetry in neural and Gastrointestinal (GI) applications.

Our proposed system is based on backscattering from a semi-passive Radio-Frequency-Identification-Device (RFID) implemented using an Application Specific Integrated Circuit (ASIC) in which electronic switching between transistor gates in high and low states create an impedance difference, thereby effectively changing the ASIC's Radar Cross Section (RCS) and thus modulating its backscat-tered field. The ASIC is used in conjunction with a biosensor to measure and transmit vital signs from within the body. With this system, we conducted backscatter propagation experiments through different biological and phantom tissues (in ex-vivo and in-vitro) in the GI and neural environments.

Our results show that the backscattered waveforms can penetrate tissues of various compositions and thicknesses with power received at distances of up to 55 cm away from the RFID ASIC. Furthermore, results from single-and multi-bit biotelemetry measurements showed a high signal fidelity with low Bit-Error-Rate (BER) while being able to resolve varying tissue temperatures measured by the biosensor in our system.

We realized a POC system in which on-chip transistor switching in an ASIC can be used to achieve backscatter communication and biosensing. This system is deployable in neural and GI applications.

Our findings in this work will provide an important practical basis for the design and development of RFID ASIC for biosensing and biotelemetry in medical applications.
Our findings in this work will provide an important practical basis for the design and development of RFID ASIC for biosensing and biotelemetry in medical applications.
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