A multiple in-camera processing system for machine vision.

In a typical machine vision application, a line-scan camera positioned on the production line captures images of the parts to be inspected and sends them to the machine vision computer. The computer then uses high-speed data acquisition devices and sophisticated analysis software to extract information from these cameras and generates decisions about the product and manufacturing system. As the manufacturing systems increasingly generate more fine featured and advanced products, the need for higher resolution and faster processing of these camera images is necessary to maintain quality control. To reduce the overwhelming amount of data from multiple camera systems to the analysis computer, an in-camera processing system is introduced. This system involves placing a computing system inside the camera which can perform similar operations to the analysis system, but without all of the additional overhead components. The work presented in this thesis describes an enhanced embedded system which is mounted into a DALSA line-scan camera. This system provides support for real-time one dimensional signal processing with the aid of integrated hardware and software resources.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .M89. Source: Masters Abstracts International, Volume: 40-03, page: 0757. Adviser: Graham A. Jullien. Thesis (M.Sc.)--University of Windsor (Canada), 1999

Difficult operations in the multi-dimensional logarithmic number system.

The Multi-Dimensional Logarithmic Number System (MDLNS), with similar properties to the Logarithmic Number System (LNS), provides more degrees of freedom than the LNS by virtue of having two or more orthogonal bases and the ability to use multiple digits. Unlike the LNS there is no direct functional relationship between binary/floating point representation and the MDLNS representation. Traditionally look-up tables (LUTs) were used to move from the binary domain to the MDLNS domain. This method could be unrealistic for hardware implementation when large binary ranges or multiple digits were used. The lack of this direct relationship also complicated the addition and subtraction operations in MDLNS. Again LUTs were used to perform these operations but they could become unrealistically large when multiple digits or large index ranges were used. The work presented in this thesis describes efficient techniques for implementing difficult MDLNS operations such as binary to MDLNS conversion as well as addition and subtraction. These techniques require the use of a new memory device with range addressing capabilities, a RALUT (range addressable look-up table). The RALUT reduces the exponential complexity associated with the traditional use of potentially large LUTs by physically removing redundant hardware used to store the MDLNS conversion, addition, and subtraction information. Other significant MDLNS improvements such as choosing efficient and optimal bases, the one-bit sign architecture, and single-digit MDLNS RALUT reduction are also discussed. These improvements are shown to reduce the hardware implementation and improve performance without sacrificing any of the MDLNS accuracy.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .M87. Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0365. Adviser: Graham Arnold Jullien. Thesis (Ph.D.)--University of Windsor (Canada), 2003

Digit-Level Serial-In Parallel-Out Multiplier Using Redundant Representation for a Class of Finite Fields

Two digit-level finite field multipliers in F2m using redundant representation are presented. Embedding F2m in cyclotomic field F2(n) causes a certain amount of redundancy and consequently performing field multiplication using redundant representation would require more hardware resources. Based on a specific feature of redundant representation in a class of finite fields, two new multiplication algorithms along with their pertaining architectures are proposed to alleviate this problem. Considering area-delay product as a measure of evaluation, it has been shown that both the proposed architectures considerably outperform existing digit-level multipliers using the same basis. It is also shown that for a subset of the fields, the proposed multipliers are of higher performance in terms of area-delay complexities among several recently proposed optimal normal basis multipliers. The main characteristics of the postplace&route application specific integrated circuit implementation of the proposed multipliers for three practical digit sizes are also reported

A Low-Power Two-Digit Multi-dimensional Logarithmic Number System Filterbank Architecture for a Digital Hearing Aid

This paper addresses the implementation of a filterbank for digital hearing aids using a multi-dimensional logarithmic number system (MDLNS). The MDLNS, which has similar properties to the classical logarithmic number system (LNS), provides more degrees of freedom than the LNS by virtue of having two, or more, orthogonal bases and the ability to use multiple MDLNS components or digits. The logarithmic properties of the MDLNS also allow for reduced complexity multiplication and large dynamic range, and a multiple-digit MDLNS provides a considerable reduction in hardware complexity compared to a conventional LNS approach. We discuss an improved design for a two-digit 2D MDLNS filterbank implementation which reduces power and area by over two times compared to the original design

A ventilator strategy combining low tidal volume ventilation, recruitment maneuvers, and high positive end-expiratory pressure does not increase sedative, opioid, or neuromuscular blocker use in adults with acute respiratory distress syndrome and may improve patient comfort

Background: The Lung Open Ventilation Study (LOV Study) compared a low tidal volume strategy with an experimental strategy combining low tidal volume, lung recruitment maneuvers, and higher plateau and positive end-expiratory pressures (PEEP) in adults with acute respiratory distress syndrome (ARDS). Herein, we compared sedative, opioid, and neuromuscular blocker (NMB) use among patients managed with the intervention and control strategies and clinicians\u27 assessment of comfort in both groups. Methods: This was an observational substudy of the LOV Study, a randomized trial conducted in 30 intensive care units in Canada, Australia, and Saudi Arabia. In 16 centers, we recorded daily doses of sedatives, opioids, and NMBs and surveyed bedside clinicians about their own comfort with the assigned ventilator strategy and their perceptions of patient comfort. We compared characteristics and outcomes of patients who did and did not receive NMBs. Results: Study groups received similar sedative, opioid, and NMB dosing on days 1, 3, and 7. Patient comfort as assessed by clinicians was not different in the two groups: 93% perceived patients had no/minimal discomfort. In addition, 92% of clinicians were comfortable with the assigned ventilation strategy without significant differences between the two groups. When clinicians expressed discomfort, more expressed discomfort about PEEP levels in the intervention vs control group (2.9% vs 0.7%, P \u3c 0.0001), and more perceived patient discomfort among controls (6.0% vs 4.3%, P = 0.049). On multivariable analysis, the strongest associations with NMB use were higher plateau pressure (hazard ratio (HR) 1.15; 95% confidence interval (CI) 1.07 to 1.23; P = 0.0002) and higher daily sedative dose (HR 1.03; 95% CI 1.02 to 1.05; P \u3c 0.0001). Patients receiving NMBs had more barotrauma, longer durations of mechanical ventilation and hospital stay, and higher mortality. Conclusions: In the LOV Study, high PEEP, low tidal volume ventilation did not increase sedative, opioid, or NMB doses in adults with ARDS, compared with a lower PEEP strategy, and appeared at least as comfortable for patients. NMB use may reflect worse lung injury, as these patients had more barotrauma, longer durations of ventilation, and higher mortality

Detecting Feature-Interaction Hotspots in Automotive Software using Relational Algebra

Modern software projects are programmed by multiple teams, consist of millions of lines of code, and are split into separate components that, during runtime, may not be contained in the same process. Due to these complexities, software defects are a common reality; defects cost the global economy over a trillion dollars each year. One area where developing safe software is crucial is the automotive domain. As the typical modern vehicle consists of over 100 million lines of code and is responsible for controlling vehicle motion through advanced driver-assistance systems (ADAS), there is a potential for these systems to malfunction in catastrophic ways. Due to this risk, automotive software needs to be inspected to verify that it is safe. The problem is that it can be difficult to carry out this detection in code; manual analysis does not scale well, search tools like grep have no contextual awareness of code, and although code reviews can be effective, they cannot target the entire codebase properly. Furthermore, automotive systems are comprised of numerous, communicating features that can possibly interact in unexpected or undefined ways. This thesis addresses this problem through the development of a static-analysis methodology that detects custom interaction patterns coined as hotspots. We identify several classes of automotive hotspots that describe patterns in automotive software that have the possibility of manifesting as a feature interaction. To detect these hotspots, this methodology employs a static, relational analysis toolchain that create a queryable model from source code and enable engineer defined queries to be run on the model that aim to reveal potential hotspots in the underlying source code. The purpose of this methodology is not to detect bugs with surety but work towards an analysis methodology that can scale to automotive software systems. We test this hotspot detection methodology through a case study conducted on the Autonomoose autonomous driving platform. In it, we generate a model of the entire Autonomoose codebase and run relational algebra queries on the model. Each script in the case study detects a type of hotspot we identify in this thesis. The results of each query are presented

The development of task performance across the worker lifespan

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