Breaking the Responsivity-Bandwidth Trade-Off Limit in GaN Photoelectrodes for High-Response and Fast-Speed Optical Communication Application

Underwater optical communication (UOC) has attracted considerable interest in the continuous expansion of human activities in marine/ocean environments. The water-durable and self-powered photoelectrodes that act as a battery-free light receiver in UOC are particularly crucial, as they may directly face complex underwater conditions. Emerging photoelectrochemical (PEC)-type photodetectors are appealing owing to their intrinsic aqueous operation characteristics with versatile tunability of photoresponses. Herein, a self-powered PEC photodetector employing n-type gallium nitride (GaN) nanowires as a photoelectrode, which is decorated with an iridium oxide (IrO_x) layer to optimize charge transfer dynamics at the GaN/electrolyte interface, is reported. Strikingly, the constructed n-GaN/IrO_x photoelectrode breaks the responsivity-bandwidth trade-off limit by simultaneously improving the response speed and responsivity, delivering an ultrafast response speed with response/recovery times of only 2 µs/4 µs while achieving a high responsivity of 110.1 mA W⁻¹. Importantly, the device exhibits a large bandwidth with 3 dB cutoff frequency exceeding 100 kHz in UOC tests, which is one of the highest values among self-powered photodetectors employed in optical communication system.     

Piezoelectric Sensors Operating at Very High Temperatures and in Extreme Environments Made of Flexible Ultrawide-Bandgap Single-Crystalline AlN Thin Films

Extreme environments are often faced in energy, transportation, aerospace, and defense applications and pose a technical challenge in sensing. Piezoelectric sensor based on single-crystalline AlN transducers is developed to address this challenge. The pressure sensor shows high sensitivities of 0.4–0.5 mV per psi up to 900 °C and output voltages from 73.3 to 143.2 mV for input gas pressure range of 50 to 200 psi at 800 °C. The sensitivity and output voltage also exhibit the dependence on temperature due to two origins. A decrease in elastic modulus (Young's modulus) of the diaphragm slightly enhances the sensitivity and the generation of free carriers degrades the voltage output beyond 800 °C, which also matches with theoretical estimation. The performance characteristics of the sensor are also compared with polycrystalline AlN and single-crystalline GaN thin films to investigate the importance of single crystallinity on the piezoelectric effect and bandgap energy-related free carrier generation in piezoelectric devices for high-temperature operation. The operation of the sensor at 900 °C is amongst the highest for pressure sensors and the inherent properties of AlN including chemical and thermal stability and radiation resistance indicate this approach offers a new solution for sensing in extreme environments.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Self-generation of reward by logarithmic transformation of multiple sensor evaluations

Artificial Life and Robotics - Although the design of the reward function in reinforcement learning is important, it is difficult to design a system that can adapt to a variety of environments and...     

An interpretability security framework for intelligent decision support systems based on saliency map

International Journal of Information Security - Benefiting from the high-speed transmission and super-low latency, the Fifth Generation (5G) networks are playing an important role in contemporary...     

Observer-based leader-following consensus of one-sided Lipschitz multi-agent systems over input saturation and directed graphs

This paper investigates a local observer-based leader-following consensus control of one-sided Lipschitz (OSL) multi-agent systems (MASs) under input saturation. The proposed consensus control scheme has been formulated by using the OSL property, input saturation, directed graphs, estimated states, and quadratic inner-boundedness condition by attaining the regional stability. It is assumed that the graph always includes a (directed) spanning tree with respect to the leader root to develop matrix inequalities for investigating parameters of the proposed observer and consensus protocols. Further, a new observer-based consensus tracking method for MASs with saturation, concerning independent topologies for communicating outputs and estimates over the network, is explored to deal with a more perplexing and realistic situation. In contrast to the traditional methods, the proposed consensus approach considers output feedback and deals with the input saturation for a generalized class of nonlinear systems. The efficiency of the obtained results is illustrated via application to a group of five moving agents in the Cartesian coordinates.     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI

Journal of Signal Processing Systems - Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful...