Bowenblevins5514

For portable devices with limited resources, it is often difficult to deploy deep networks due to the prohibitive computational overhead. Numerous approaches have been proposed to quantize weights and/or activations to speed up the inference. Loss-aware quantization has been proposed to directly formulate the impact of weight quantization on the model's final loss. However, we discover that, under certain circumstances, such a method may not converge and end up oscillating. To tackle this issue, we introduce a novel loss-aware quantization algorithm to efficiently compress deep networks with low bit-width model weights. We provide a more accurate estimation of gradients by leveraging the Taylor expansion to compensate for the quantization error, which leads to better convergence behavior. Our theoretical analysis indicates that the gradient mismatch issue can be fixed by the newly introduced quantization error compensation term. Experimental results for both linear models and convolutional networks verify the effectiveness of our proposed method.In recent years, multivariate synchronization index (MSI) algorithm, as a novel frequency detection method, has attracted increasing attentions in the study of brain-computer interfaces (BCIs) based on steady state visual evoked potential (SSVEP). However, MSI algorithm is hard to fully exploit SSVEP-related harmonic components in the electroencephalogram (EEG), which limits the application of MSI algorithm in BCI systems. In this paper, we propose a novel filter bank-driven MSI algorithm (FBMSI) to overcome the limitation and further improve the accuracy of SSVEP recognition. We evaluate the efficacy of the FBMSI method by developing a 6-command SSVEP-NAO robot system with extensive experimental analyses. An offline experimental study is first performed with EEG collected from nine subjects to investigate the effects of varying parameters on the model performance. Offline results show that the proposed method has achieved a stable improvement effect. We further conduct an online experiment with six subjects to assess the efficacy of the developed FBMSI algorithm in a real-time BCI application. The online experimental results show that the FBMSI algorithm yields a promising average accuracy of 83.56% using a data length of even only one second, which was 12.26% higher than the standard MSI algorithm. These extensive experimental results confirmed the effectiveness of the FBMSI algorithm in SSVEP recognition and demonstrated its potential application in the development of improved BCI systems.How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.Singular value decomposition (SVD) is a valuable factorization technique used in clutter rejection filtering for power Doppler imaging. Conventionally, SVD is applied to a Casorati matrix of radiofrequency data, which enables filtering based on spatial or temporal characteristics. In this paper, we propose a clutter filtering method that uses a higher-order singular value decomposition (HOSVD) applied to a tensor of aperture data, e.g. delayed channel data. We discuss temporal, spatial, and aperture domain features that can be leveraged in filtering and demonstrate that this multidimensional approach improves sensitivity toward blood flow. Further, we show that HOSVD remains more robust to short ensemble lengths than conventional SVD filtering. Validation of this technique is shown using Field II simulations and in vivo data.Emerging ultrasound imaging modality based on optical-generated acoustic waves, such as photoacoustic (PA) imaging, has enabled novel functional imaging on biological samples. The performance of the ultrasonic transducer plays a critical role in producing higher quality photoacoustic images. UCL-TRO-1938 in vivo However, the high electrical impedance of the small piezoelectric elements in the transducer array causes an electrical mismatch with external circuitry and results in degraded sensitivity. One effective method for reducing the electrical impedance is to implement a piezoelectric multilayer configuration instead of the conventional single layer for the transducer. In this work, we introduced an ultrasonic transducer comprising a piezoelectric polymer multilayer structure produced by an innovative multi-cycle powder-based electrophoretic deposition, using a suspension of polymer nano-particles. The multi-cycle electrophoretic deposition overcomes the re-dissolution issue in solution-based methods. The ultrasonic transducer comprising the piezoelectric polymer multilayer exhibits significantly enhanced receiving sensitivity as compared to the ultrasonic transducer using a single layer. Ultrasonic transducer with multi-element array configuration is obtained using the piezoelectric polymer multilayer, and photoacoustic imaging with improved resolution is demonstrated. Theoretical analysis shows that the enhanced transducer performance is mainly attributed to the improved electrical impedance match between the piezoelectric polymer element in the transducer and external receiving circuit.