Design of fast vedic multiplier with fault diagnostic capabilities

2016 ◽  
Author(s):  
Kunjpriya Morghade ◽  
Pravin Dakhole
Keyword(s):  
Vedic Multiplier ◽  
Fault Diagnostic ◽  
Diagnostic Capabilities

Concepts for In Situ Diagnostics in Analog Microelectronic Circuits

2007 ◽  
Author(s):  
Ted Kolasa ◽  
Alfredo Mendoza
Keyword(s):  
Analog Circuits ◽  
System Architecture ◽  
Analog Circuit ◽  
Fault Localization ◽  
Diagnostic System ◽  
Diagnostic Capability ◽  
Common Signal ◽  
Diagnostic Capabilities ◽  
In Situ Diagnostics

Abstract Comprehensive in situ (designed-in) diagnostic capabilities have been incorporated into digital microelectronic systems for years, yet similar capabilities are not commonly incorporated into the design of analog microelectronics. And as feature sizes shrink and back end interconnect metallization becomes more complex, the need for effective diagnostics for analog circuits becomes ever more critical. This paper presents concepts for incorporating in situ diagnostic capability into analog circuit designs. Aspects of analog diagnostic system architecture are discussed as well as nodal measurement scenarios for common signal types. As microelectronic feature sizes continue to shrink, diagnostic capabilities such as those presented here will become essential to the process of fault localization in analog circuits.

Assessing the diagnostic capabilities of the app DemDx in an acute care setting (Preprint)

2018 ◽  
Author(s):  
Cian Murphy
Keyword(s):  
Differential Diagnosis ◽  
Care Setting ◽  
Medical Student Education ◽  
Initial Assessment ◽  
Junior Doctors ◽  
Doctor Who ◽  
Clinical Accuracy ◽  
Education Tool ◽  
Clinical Records ◽  
Diagnostic Capabilities

UNSTRUCTURED DemDx is a differential diagnosis app for students and junior doctors. Starting with a patient’s presenting complaint the app goes through a step-by-process through history, examination and investigation findings to an increasingly refined differential diagnosis list until a single most likely diagnosis is reached. The aim of this project was to assess the accuracy of DemDx in an Emergency Department (ED) setting. Anonymised clinical records for 100 patients were retrospectively obtained from the ED in Beth Israel, Boston, USA. This contained the differential diagnoses from the clerking doctor, who performed the initial assessment (D1). The discharge diagnosis was used as the gold standard diagnosis (D2). D1 agreed with D2 in 74.44% of cases while DemDx agreed with D2 in 85.56% of cases (p=0.0003716). When the first, and thus most likely, differential was taken from D1 and DemDx, they agreed with D2 in 20 and 18.8% of cases, respectively (p=0.1428). This demonstration of the clinical accuracy of the app highlights how it can be a useful medical student education tool.

scholarly journals Clinically applicable artificial intelligence system for dental diagnosis with CBCT

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matvey Ezhov ◽  
Maxim Gusarev ◽  
Maria Golitsyna ◽  
Julian M. Yates ◽  
Evgeny Kushnerev ◽  
...  
Keyword(s):  
State Of The Art ◽  
Model Development ◽  
Clinical Effectiveness ◽  
Anatomical Landmarks ◽  
Periapical Lesion ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Significant Difference ◽  
Diagnostic Capabilities ◽  
Lesion Localization

AbstractIn this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Diagnostic Capabilities of a Smartphone- Based Low-Cost Microscope

2020 ◽  
Vol 6 (3) ◽  
pp. 522-525
Author(s):  
Dorina Hasselbeck ◽  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Peter P. Pott
Keyword(s):  
Low Cost ◽  
Field Of View ◽  
Parasitic Diseases ◽  
Optical Parameters ◽  
Trade Off ◽  
Resource Limited ◽  
Wide Range ◽  
Diagnostic Applications ◽  
Diagnostic Capabilities

AbstractMicroscopy enables fast and effective diagnostics. However, in resource-limited regions microscopy is not accessible to everyone. Smartphone-based low-cost microscopes could be a powerful tool for diagnostic and educational purposes. In this paper, the imaging quality of a smartphone-based microscope with four different optical parameters is presented and a systematic overview of the resulting diagnostic applications is given. With the chosen configuration, aiming for a reasonable trade-off, an average resolution of 1.23 μm and a field of view of 1.12 mm2 was achieved. This enables a wide range of diagnostic applications such as the diagnosis of Malaria and other parasitic diseases.

scholarly journals Validation of an informatics tool to assess resident’s progress in developing reporting skills

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Facundo N. Diaz ◽  
Marina Ulla
Keyword(s):  
Communication Skills ◽  
Residency Program ◽  
Preliminary Report ◽  
Objective Assessment ◽  
Final Report ◽  
Residency Programs ◽  
Case Based Learning ◽  
Radiology Residency ◽  
Clinically Significant ◽  
Diagnostic Capabilities

Abstract Background Diagnostic radiology residency programs pursuits as main objectives of the development of diagnostic capabilities and written communication skills to answer clinicians’ questions of referring clinicians. There has been also an increasing focus on competencies, rather than just education inputs. Then, to show ongoing professional development is necessary for a system to assess and document resident’s competence in these areas. Therefore, we propose the implementation of an informatics tool to objectively assess resident’s progress in developing diagnostics and reporting skills. We expect to found decreased preliminary report-final report variability within the course of each year of the residency program. Results We analyzed 12,162 evaluations from 32 residents (8 residents per year in a 4-year residency program) in a 7-month period. 73.96% of these evaluations belong to 2nd-year residents. We chose two indicators to study the evolution of evaluations: the total of discrepancies over the total of preliminary reports (excluding score 0) and the total of likely to be clinically significant discrepancies (scores 2b, 3b, and 4b) over the total of preliminary reports (excluding score 0). With the analysis of these two indicators over the evaluations of 2nd-year residents, we found a slight decrease in the value of the first indicator and relative stable behavior of the second one. Conclusions This tool is useful for objective assessment of reporting skill of radiology residents. It can provide an opportunity for continuing medical education with case-based learning from those cases with clinically significant discrepancies between the preliminary and the final report.

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