scholarly journals Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Erdem Demircioglu ◽  
Ahmet Fazil Yagli ◽  
Senol Gulgonul ◽  
Haydar Ankishan ◽  
Emre Oner Tartan ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Patch Antennas ◽  
Computing Technique ◽  
Bandwidth Enhancement ◽  
Antenna Performance ◽  
Measurement Results ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Substrate Properties ◽  
Soft Computing Technique

This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.

scholarly journals Design Techniques for Elliptical Micro-Strip Patch Antenna and Their Effects on Antenna Performance

2019 ◽  
Vol 8 (12) ◽  
pp. 2317-2326
Keyword(s):  
Patch Antenna ◽  
Degrees Of Freedom ◽  
Wide Band ◽  
Research Area ◽  
Patch Antennas ◽  
Bandwidth Enhancement ◽  
Antenna Performance ◽  
Elliptical Ring ◽  
New Research ◽  
Design Techniques

Elliptical Micro-strip Patch Antenna (EMPA) has been emerged as a peculiar and significant category among the different shaped micro-strip patch antennas because of its circular polarization and dual-resonant frequency features with a single feed. Elliptical and its derived shapes such as semielliptical, half-elliptical, slotted-elliptical and elliptical ring are found to be particularly instrumental for bandwidth enhancement and these antennas find great applications in Ultra Wide Band (UWB) and Super Wide Band (SWB) communications. Compared to antennas with circular or rectangular shapes, the design of EMPA is a research area of high potential as there is higher flexibility in its design due to more degrees of freedom. The reported literature in the field of EMPA is very less and there is ample scope for new researchers to work on. This review paper is an attempt to summarize and critically assess the-state-of-the-art design techniques as reported in literature and understand their effects on performance of elliptical patch antenna for suggesting new research fronts in the field of EMPA.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals A data-driven performance dashboard for surgical dissection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amir Baghdadi ◽  
Sanju Lama ◽  
Rahul Singh ◽  
Hamidreza Hoshyarmanesh ◽  
Mohammadsaleh Razmi ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Economic Consequences ◽  
Skill Assessment ◽  
Geospatial Information ◽  
Surgical Performance ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Surgical Data ◽  
Expert Surgeon ◽  
The Rich

AbstractSurgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document