Patients understanding the actual reduction and treatments for the selected podiatry conditions

In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage Vt of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2⁸ and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. However, DAN's major drawback is that it is difficult to find the domain-invariant space by using a single feature extractor. In this article, we propose to split the feature extractor into two contrastive branches, with one branch delegating for the class-dependence in the latent space and another branch focusing on domain-invariance. The feature extractor achieves these contrastive goals by sharing the first and last hidden layers but possessing decoupled branches in the middle hidden layers. For encouraging the feature extractor to produce class-discriminative embedded features, the label predictor is adversarially trained to produce equal posterior probabilities across all of the outputs instead of producing one-hot outputs. We refer to the resulting domain adaptation network as ``contrastive adversarial domain adaptation network (CADAN). We evaluated the embedded features' domain-invariance via a series of speaker identification experiments under both clean and noisy conditions. Results demonstrate that the embedded features produced by CADAN lead to a 33% improvement in speaker identification accuracy compared with the conventional DAN.Recurrent neural networks (RNNs) can remember temporal contextual information over various time steps. The well-known gradient vanishing/explosion problem restricts the ability of RNNs to learn long-term dependencies. The gate mechanism is a well-developed method for learning long-term dependencies in long short-term memory (LSTM) models and their variants. These models usually take the multiplication terms as gates to control the input and output of RNNs during forwarding computation and to ensure a constant error flow during training. In this article, we propose the use of subtraction terms as another type of gates to learn long-term dependencies. Specifically, the multiplication gates are replaced by subtraction gates, and the activations of RNNs input and output are directly controlled by subtracting the subtrahend terms. The error flows remain constant, as the linear identity connection is retained during training. The proposed subtraction gates have more flexible options of internal activation functions than the multiplication gates of LSTM. The experimental results using the proposed Subtraction RNN (SRNN) indicate comparable performances to LSTM and gated recurrent unit in the Embedded Reber Grammar, Penn Tree Bank, and Pixel-by-Pixel MNIST experiments. To achieve these results, the SRNN requires approximate three-quarters of the parameters used by LSTM. We also show that a hybrid model combining multiplication forget gates and subtraction gates could achieve good performance.Autonomous driving is of great interest to industry and academia alike. The use of machine learning approaches for autonomous driving has long been studied, but mostly in the context of perception. In this article, we take a deeper look on the so-called end-to-end approaches for autonomous driving, where the entire driving pipeline is replaced with a single neural network. We review the learning methods, input and output modalities, network architectures, and evaluation schemes in end-to-end driving literature. Interpretability and safety are discussed separately, as they remain challenging for this approach. selleck chemicals llc Beyond providing a comprehensive overview of existing methods, we conclude the review with an architecture that combines the most promising elements of the end-to-end autonomous driving systems.To meet the increasing demand for denser integrated circuits, feedforward control plays an important role in the achievement of high servo performance of wafer stages. The preexisting feedforward control methods, however, are subject to either inflexibility to reference variations or poor robustness. In this article, these deficiencies are removed by a novel variable-gain iterative feedforward tuning (VGIFFT) method. The proposed VGIFFT method attains 1) no involvement of any parametric model through data-driven estimation; 2) high performance regardless of reference variations through feedforward parameterization; and 3) especially high robustness against stochastic disturbance as well as against model uncertainty through a variable learning gain. What is more, the tradeoff in which preexisting methods are subject to between fast convergence and high robustness is broken through by VGIFFT. Experimental results validate the proposed method and confirm its effectiveness and enhanced performance.Battery-less and ultra-low-power implantable medical devices (IMDs) with minimal invasiveness are the latest therapeutic paradigm. This work presents a 13.56-MHz inductive power receiver system-on-a-chip with an input sensitivity of -25.4 dBm (2.88 μW) and an efficiency of 46.4% while driving a light load of 30 μW. In particular, a real-time resonance compensation scheme is proposed to mitigate resonance variations commonly seen in IMDs due to different dielectric environments, loading conditions, and fabrication mismatches, etc. The power-receiving front-end incorporates a 6-bit capacitor bank that is periodically adjusted according to a successive-approximation-resonance-tuning (SART) algorithm. The compensation range is as much as 24 pF and it converges within 12 clock cycles and causes negligible power consumption overhead. The harvested voltage from 1.7 V to 3.3 V is digitized on-chip and transmitted via an ultra-wideband impulse radio (IR-UWB) back-telemetry for closed-loop regulation. The IC is fabricated in 180-nm CMOS process with an overall current dissipation of 750 nA. At a separation distance of 2 cm, the end-to-end power transfer efficiency reaches 16.1% while driving the 30-μW load, which is immune to artificially induced resonance capacitor offsets. The proposed system can be applied to various battery-less IMDs with the potential improvement of the power transfer efficiency on orders of magnitude.Due to the potential values in many areas such as e-commerce and inventory management, fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has recently become a research hotspot. It is also a challenging issue with serval obstacles variety and complexity of fabric appearance, high requirements for retrieval accuracy. link2 To address this issue, this paper proposes a novel approach for fabric image retrieval based on multi-task learning and deep hashing. According to the cognitive system of fabric, a multi-classification-task learning model with uncertainty loss and constraint is presented to learn fabric image representation. Then we adopt an unsupervised deep network to encode the extracted features into 128-bits hashing codes. Further, the hashing codes are regarded as the index of fabrics image for image retrieval. To evaluate the proposed approach, we expanded and upgraded the dataset WFID, which was built in our previous research specifically for fabric image retrieval. The experimental results show that the proposed approach outperforms the state-of-the-art.This work assessed the possible correlation between the refractive index of a SiOxNy passivation film on a surface acoustic wave (SAW) device and the temperature coefficient of frequency (TCF) of the device itself. The data demonstrate that the refractive index does correlate with the TCF as well as the frequency of the one-port resonator. SiOxNy passivation films having an optimal refractive index can potentially suppress the frequency shifts caused by the deposition of such layers, and can change the TCF from that for a Si3N4 film to that for SiO2. The results also show that the coupling coefficient of the one-port resonator increases when using a SiOxNy film with a lower refractive index, which changes the TCF such that this value approaches that for a SiO2 film. Finite-element method spectral domain analyses established that the frequency responses of the one-port resonators were affected by the velocity and temperature coefficient of velocity of the dielectric films deposited on the interdigital transducer electrodes. Thus, adjusting the refractive index of the SiOxNy film can be used to control the properties of an SAW device, including the TCF.The transducer is an essential part of all ultrasonic systems used for applications such as medical diagnostics, therapy, nondestructive evaluation, and cleaning because its health condition is vital to their proper operation. Defects within the active element, backing or other constitutive elements, and loss of adhesion between layers can significantly weaken the performance of a transducer. The objective of this work is to determine procedures to monitor the behavior of a single-element probe during its lifetime and detect degradations before they significantly affect the performance of the system. To achieve this, electromechanical admittance (EMA)-based method is envisaged numerically and experimentally. A simplified single-element transducer consisting of a piezoceramic disk, a bonding layer, and a backing is studied and the influence of bonding delamination on EMA is investigated. This study considers three different types of delaminations, which are named, respectively, "center" (circular delamination from the center of the disk toward the peripheric zone), "peripheric" (annular delamination from the peripheric zone toward the center), and "wedge" (wedge-shaped delamination with a given angle). For each case, a numerical model based on the finite-element (FE) method is developed a 2-D FE analysis is implemented for the first two types of delaminations, taking advantage of their axisymmetric structure, and "wedge" delamination is modeled in 3-D. link3 Then, transducers with different shapes of 3-D printed backings are mounted and experiments are conducted using an impedance analyzer. Finally, experimental results are found to be in good agreement with numerical solutions and it shows that changes in EMA can particularly reveal the occurrence and extent of delamination in an ultrasound probe.