Design and Characterization of an Ultrasonic Surgical Tool Using d₃₁ PMN-PT Plate

AbstractAn ultrasonic surgical tool for tissue incision and dissection has been designed and characterized. The surgical tool is based on a simple geometry to which PMN-PT d31 plates are bonded directly. The performance of the surgical tool has been defined numerically with the Abaqus finite element analysis (FEA) package and practically with laser vibrometer and impedance spectroscopy. The results show the ability of FEA to accurately predict the behaviors of an ultrasonic device as numerical and practical analysis were found to be in a good agreement. The design of the tool presented has the ability to generate displacement amplitude high enough to carry out soft tissue incision with relatively low driving voltage

Coherent states for polynomial su(1,1) algebra and a conditionally solvable system

In a previous paper [{\it J. Phys. A: Math. Theor.} {\bf 40} (2007) 11105], we constructed a class of coherent states for a polynomially deformed $su(2)$ algebra. In this paper, we first prepare the discrete representations of the nonlinearly deformed $su(1,1)$ algebra. Then we extend the previous procedure to construct a discrete class of coherent states for a polynomial su(1,1) algebra which contains the Barut-Girardello set and the Perelomov set of the SU(1,1) coherent states as special cases. We also construct coherent states for the cubic algebra related to the conditionally solvable radial oscillator problem.Comment: 2 figure

Evolutionary algorithms for state justification in sequential automatic test pattern generation

Sequential circuit test generation using deterministic, fault-oriented algorithms is highly complex and time consuming. New approaches are needed to enhance the existing techniques, both to reduce execution time and improve fault coverage. Evolutionary algorithms have been effective in solving many search and optimization problems. A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. In this work, a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification is proposed. A General Algorithm is employed, to engineer state justification sequences vector by vector. This is in contrast to previous approaches where GA is applied to the whole sequence. The proposed method is compared with previous GA-based approaches. Significant improvements have been obtained for ISCAS benchmark circuits in terms of state coverage and CPU time. Furhtermore, it is demonstrated that the state justification sequence generated, helps the ATPG in detecting a large number of hard to detect faults

Evolutionary algorithms for state justification in sequential automatic test pattern generation

Sequential circuit test generation using deterministic, fault-oriented algorithms is highly complex and time consuming. New approaches are needed to enhance the existing techniques, both to reduce execution time and improve fault coverage. Evolutionary algorithms have been effective in solving many search and optimization problems. A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. In this work, a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification is proposed. A General Algorithm is employed, to engineer state justification sequences vector by vector. This is in contrast to previous approaches where GA is applied to the whole sequence. The proposed method is compared with previous GA-based approaches. Significant improvements have been obtained for ISCAS benchmark circuits in terms of state coverage and CPU time. Furhtermore, it is demonstrated that the state justification sequence generated, helps the ATPG in detecting a large number of hard to detect faults

An Iterative Heuristic for State Justi�cation in Sequential Automatic Test Pattern Generation

State justifcation is one of the most time-consuming tasks in sequential Automatic Test Pattern Generation (ATPG). For states that are difficult to justify, deterministic algorithms take significant CPU time without much success most of the time. In this work, we adopt a hybrid approach for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justifcation sequences vector by vector. The proposed method is compared with previous GA-based approaches. Significant improvements have been obtained for ISCAS benchmark circuits in terms of state coverage and CPU time

Effect of Climate Change on Arable Crop Farmers Productivity in Ibarapa Centra Local Government Area of Oyo State Nigeria

The study was an analysis of the Effect of climate change on arable crop farmers’ productivity in ibarapa central local government of Oyo state, Nigeria.  Primary data were collected using structured interview guide, administered on 100 arable crop farmers using multistage sampling technique. Data collected were analyzed using descriptive statistics (frequency and percentage) and inferential (partial correlation) statistical tools. The result showed that the mean age was 47.6years while 97% were married. However 97.00% of the arable crop farmers reported to have had malaria attack occasionally within the last10 years and 83.00% in the last five years  while 64% reported malaria occurrence within the last one year.Majority (83.00%) of the respondent stated that erratic rainfall as compared to other causes  had more effect on their productivity while just a few (7.00%) of the respondent claimed that low heat was the cause of their low productivity. The hypothesis tested revealed that the socioeconomic characteristics of the arable crop farmers such as age (0.7490), sex (0.3200), household size (0.4580), income (0.5500) and educational status (0.3240) had significant positive relationship with their levels of productivity. It was recommended that the arable crop farmers should form farmers association to pool resources together to acquire irrigation gadgets so as to alleviate the problem of erratic nature of rainfall in the area and Nigerian Meteorological Agency (NIMET) should be more alive to her weather forecast responsibilities in the area of using diverse languages of the people in the Nigerian agricultural zones. Keywords:Climate Change, Arable crop, Farmers, Productivity

Evolutionary algorithms for state justification in sequential automatic test pattern generation

Sequential circuit test generation using deterministic, fault-oriented algorithms is highly complex and time consuming. New approaches are needed to enhance the existing techniques, both to reduce execution time and improve fault coverage. Evolutionary algorithms have been effective in solving many search and optimization problems. A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. In this work, a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification is proposed. A General Algorithm is employed, to engineer state justification sequences vector by vector. This is in contrast to previous approaches where GA is applied to the whole sequence. The proposed method is compared with previous GA-based approaches. Significant improvements have been obtained for ISCAS benchmark circuits in terms of state coverage and CPU time. Furhtermore, it is demonstrated that the state justification sequence generated, helps the ATPG in detecting a large number of hard to detect faults

An Iterative Heuristic for State Justi�cation in Sequential Automatic Test Pattern Generation

State justifcation is one of the most time-consuming tasks in sequential Automatic Test Pattern Generation (ATPG). For states that are difficult to justify, deterministic algorithms take significant CPU time without much success most of the time. In this work, we adopt a hybrid approach for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justifcation sequences vector by vector. The proposed method is compared with previous GA-based approaches. Significant improvements have been obtained for ISCAS benchmark circuits in terms of state coverage and CPU time

An Iterative Heuristic for State Justification in Sequential Automatic Test Pattern Generation

• Motivation • Test Pattern Generation for Sequential Circuits • Genetic Algorithms (GA) • Problem Definition • The Proposed Approach • Experiments and Results • Contributions • Future Direction