Enhanced forward stimulated Brillouin scattering in silicon photonic slot waveguide Bragg grating

We study the forward stimulated Brillouin scattering process in a suspended silicon slot waveguide Bragg grating. Full-vectorial formalism is applied to analyze the interplay of electrostriction and radiation pressure. We show that radiation pressure is the dominant factor in the proposed waveguide. The Brillouin gain strongly depends on the structural parameters and the maximum value in the order of 106 W−1 m−1 is obtained in the slow light regime, which is more than two orders larger than that of the stand-alone strip and slot waveguides

Non-volatile Optical Switch Based on a GST-Loaded Directional Coupler

We present a non-volatile optical switch based on a directional coupler comprising a silicon-Ge2Sb2Te5 (GST) hybrid waveguide. The non-volatility of GST makes it attractive for reducing static power consumption in optical switching. Experimental results show that the optical switch has an extinction ratio of >20 dB in the bar state and >25 dB in the cross state around 1578 nm wavelength. The insertion loss is 2 dB and 7 dB for the bar and cross states, respectively

All-optical non-volatile tuning of an AMZI-coupled ring resonator with GST phase-change material

We present a Ge2Sb2Te5 (GST)-integrated ring resonator with the tuning enabled by an all-optical phase change of GST using a sequence of optical pulses. The tuning is non-volatile and repeatable, with no static power consumption due to the “self-holding” feature of the GST phase-change material. The 2 μm long GST can be partially crystallized by controlling the number of pulses, increasing the tuning freedom. The coupling between the ring resonator and the bus waveguide is based on an asymmetric Mach–Zehnder interferometer. The coupling strength is wavelength-dependent, so that an optimal wavelength can be selected for the probe light to get more than 20 dB transmission contrast between the amorphous and crystalline GST states

Miniature Silicon Nanobeam Resonator Tuned by GST Phase Change Material

We report a silicon optical nanobeam resonator with central hole infiltrated with a thin layer of Ge2Sb2Te5 (GST) material. The resonances can be tuned when the GST changes its phases between the amorphous and crystalline states

A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning

Owing to the recent developments in Generative Artificial Intelligence (GenAI) and Large Language Models (LLM), conversational agents are becoming increasingly popular and accepted. They provide a human touch by interacting in ways familiar to us and by providing support as virtual companions. Therefore, it is important to understand the user's emotions in order to respond considerately. Compared to the standard problem of emotion recognition, conversational agents face an additional constraint in that recognition must be real-time. Studies on model architectures using audio, visual, and textual modalities have mainly focused on emotion classification using full video sequences that do not provide online features. In this work, we present a novel paradigm for contextualized Emotion Recognition using Graph Convolutional Network with Reinforcement Learning (conER-GRL). Conversations are partitioned into smaller groups of utterances for effective extraction of contextual information. The system uses Gated Recurrent Units (GRU) to extract multimodal features from these groups of utterances. More importantly, Graph Convolutional Networks (GCN) and Reinforcement Learning (RL) agents are cascade trained to capture the complex dependencies of emotion features in interactive scenarios. Comparing the results of the conER-GRL model with other state-of-the-art models on the benchmark dataset IEMOCAP demonstrates the advantageous capabilities of the conER-GRL architecture in recognizing emotions in real-time from multimodal conversational signals.Comment: 5 pages (4 main + 1 reference), 2 figures. Submitted to IEEE FG202

Amplifying Sine Unit: An Oscillatory Activation Function for Deep Neural Networks to Recover Nonlinear Oscillations Efficiently

Many industrial and real life problems exhibit highly nonlinear periodic behaviors and the conventional methods may fall short of finding their analytical or closed form solutions. Such problems demand some cutting edge computational tools with increased functionality and reduced cost. Recently, deep neural networks have gained massive research interest due to their ability to handle large data and universality to learn complex functions. In this work, we put forward a methodology based on deep neural networks with responsive layers structure to deal nonlinear oscillations in microelectromechanical systems. We incorporated some oscillatory and non oscillatory activation functions such as growing cosine unit known as GCU, Sine, Mish and Tanh in our designed network to have a comprehensive analysis on their performance for highly nonlinear and vibrational problems. Integrating oscillatory activation functions with deep neural networks definitely outperform in predicting the periodic patterns of underlying systems. To support oscillatory actuation for nonlinear systems, we have proposed a novel oscillatory activation function called Amplifying Sine Unit denoted as ASU which is more efficient than GCU for complex vibratory systems such as microelectromechanical systems. Experimental results show that the designed network with our proposed activation function ASU is more reliable and robust to handle the challenges posed by nonlinearity and oscillations. To validate the proposed methodology, outputs of our networks are being compared with the results from Livermore solver for ordinary differential equation called LSODA. Further, graphical illustrations of incurred errors are also being presented in the work.Comment: 16 Pages and 16 figure

ASU-CNN: An Efficient Deep Architecture for Image Classification and Feature Visualizations

Activation functions play a decisive role in determining the capacity of Deep Neural Networks as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit broke the taboo for a number of applications. In this paper, a Convolutional Neural Network model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.Comment: 11 pages , 8 figure

A Simplified Finite-State Predictive Direct Torque Control for Induction Motor Drive

© 2016 IEEE. Finite-state predictive torque control (FS-PTC) is computationally expensive, since it uses all voltage vectors (VVs) available from a power converter for prediction and actuation. The computational burden is rapidly increased with the number of VVs and objectives to be controlled. Moreover, designing a cost function with more than two control objectives is a complex task. This paper proposes a simplified algorithm based on a new direct torque control (DTC) switching table to reduce the number of VVs to be predicted and objectives to be controlled. The new switching table also assists to reduce average switching frequency and its variation range. As a result, the cost function is simplified by not requiring to include the frequency term. Experimental results show that the average execution time and the average switching frequency for the proposed algorithm are greatly reduced without affecting the torque and flux performances achieved in the conventional FS-PTC

Structure-based mutagenesis of the integrase-LEDGF/p75 interface uncouples a strict correlation between in vitro protein binding and HIV-1 fitness

AbstractLEDGF/p75 binding-defective IN mutant viruses were previously characterized as replication-defective, yet RNAi did not reveal an essential role for the host factor in HIV-1 replication. Correlative analyses of protein binding and viral fitness were expanded here by targeting 12 residues at the IN-LEDGF/p75 binding interface. Whereas many of the resultant viruses were defective, the majority of the INs displayed wild-type in vitro integration activities. Though an overall trend of parallel loss of LEDGF/p75 binding and HIV-1 infectivity was observed, a strict correlation was not. His-tagged INA128Q, derived from a phenotypically wild-type virus, failed to pull-down LEDGF/p75, but INA128Q was effectively recovered in a reciprocal GST pull-down assay. Under these conditions, INH171A, also derived from a phenotypically wild-type virus, interacted less efficiently than a previously described interaction-defective mutant, INQ168A. Thus, the relative affinity of the in vitro IN-LEDGF/p75 interaction is not a universal predictor of IN mutant viral fitness