Development of Classification Features of Mental Disorder Characteristics Using The Fuzzy Logic

Abstract—Mental disorders are related to self-injurious behavior problems of mind, such as the tendency to commit suicide. This research has built a system to classify the disorder. It explains that a system is used to help the people recognize mental illness as a diagnosis detection. Diagnosis can be done in the form of automation system using data mining with Fuzzy Logic method. This system can make decision to classify the mental illnesses based on symptoms. The first stage of the research was collecting and preprocessing the data by type. There are six types of psychiatric disorders that are determined, namely Schizophrenia Paranoid, Phobia, Depression, Anxiety, Obsessive Compulsive Disorder (OCD), and Anti-Social. The source of the data were questionnaires that consisted of the list of symptoms and types of disorders that were distributed to 16 selected respondents, including psychiatric specialists, psychology lecturers, general practitioners, psychiatric hospital nurses, and psychology students. The next stage was building the fuzzy process to determine ten inputs in the form of symptoms. Outputs system were six types of the disease. The fuzzy inference system used Mamdani model and obtained 65 rules in determining the classification. The result of system test is done for both training and testing data and accuracy level of 91.67% for training data and 81.94% for testing dat

E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods

During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches

A High-level EDA Environment for the Automatic Insertion of HD-BIST Structures

This paper presents a High-Level EDA environment based on the Hierarchical Distributed BIST (HD-BIST), a flexible and reusable approach to solve BIST scheduling issues in System-on-Chip applications. HD-BIST allows activating and controlling different BISTed blocks at different levels of hierarchy, with a minimum overhead in terms of area and test time. Besides the hardware layer, the authors present the HD-BIST application layer, where a simple modeling language, and a prototypical EDA tool demonstrate the effectiveness of the automation of the HD-BIST insertion in the test strategy definition of a complex System-on-Chip

Test exploration and validation using transaction level models

The complexity of the test infrastructure and test strategies in systems-on-chip approaches the complexity of the functional design space. This paper presents test design space exploration and validation of test strategies and schedules using transaction level models (TLMs). Since many aspects of testing involve the transfer of a significant amount of test stimuli and responses, the communication-centric view of TLMs suits this purpose exceptionally wel