Tool Integration for Automated Synthesis of Distributed Embedded Controllers

Controller design and their software implementations are usually done in isolated design spaces using respective COTS design tools. However, this separation of concerns can lead to long debugging and integration phases. This is because assumptions made about the implementation platform during the design phase—e.g., related to timing—might not hold in practice, thereby leading to unacceptable control performance. In order to address this, several control/architecture co-design techniques have been proposed in the literature. However, their adoption in practice has been hampered by the lack of design flows using commercial tools. To the best of our knowledge, this is the first article that implements such a co-design method using commercially available design tools in an automotive setting, with the aim of minimally disrupting existing design flows practiced in the industry. The goal of such co-design is to jointly determine controller and platform parameters in order to avoid any design-implementation gap , thereby minimizing implementation time testing and debugging. Our setting involves distributed implementations of control algorithms on automotive electronic control units ( ECUs ) communicating via a FlexRay bus. The co-design and the associated toolchain Co-Flex jointly determines controller and FlexRay parameters (that impact signal delays) in order to optimize specified design metrics. Co-Flex seamlessly integrates the modeling and analysis of control systems in MATLAB/Simulink with platform modeling and configuration in SIMTOOLS/SIMTARGET that is used for configuring FlexRay bus parameters. It automates the generation of multiple Pareto-optimal design options with respect to the quality of control and the resource usage, that an engineer can choose from. In this article, we outline a step-by-step software development process based on Co-Flex tools for distributed control applications. While our exposition is automotive specific, this design flow can easily be extended to other domains.

Software quality improvement and validation using reengineering

In software development life cycle, software maintenance is among the critical phases. It is a post-implementation activity that requires rigorous human efforts. For any software developer, maintaining software for a longer period is the primary objective. This objective can be accomplished if good quality software is developed. Maintainability is one of the vital characteristics of software maintenance. Maintainability enables developers to keep the system alive for a longer period of time at a limited cost. Software Maintainability can be enhanced using reengineering. The proposed research validates improvement in the quality of the reengineered software system. The quality of the software is analyzed using a coupling, cohesion, inheritance, and other essential design metrics. The observed improvement in the software design is 62.1%. The execution time of the software is also reduced by 6.5%. Reduction in the cost of maintenance is also another important outcome of this research. The observed reduction in the maintenance cost is 36.8%. Thus, the main objective of the proposed research is to analyze and validate the quality improvement in the reengineered software. Agile Scrum methodology has been used to perform software reengineering. Design Metrics are measured using the Chidamber and Kemerer Java metric (CKJM) version-9.0 tool. For reengineering implementation, Net Beans 7.3 has been used.

If a picture is worth 1000 words, is a word worth 1000 features for design metric estimation?

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because they encode a plethora of information. When evaluating designs, we aim to capture a range of information, including usefulness, uniqueness, and novelty of a design. The subjective nature of these concepts makes their evaluation difficult. Still, many attempts have been made and metrics developed to do so, because design evaluation is integral to the creation of novel solutions. The most common metrics used are the consensual assessment technique (CAT) and the Shah, Vargas-Hernandez, and Smith (SVS) method. While CAT is accurate and often regarded as the “gold standard,” it relies on using expert ratings, making CAT expensive and time-consuming. Comparatively, SVS is less resource-demanding, but often criticized as lacking sensitivity and accuracy. We utilize the complementary strengths of both methods through machine learning. This study investigates the potential of machine learning to predict expert creativity assessments from non-expert survey results. The SVS method results in a text-rich dataset about a design. We utilize these textual design representations and the deep semantic relationships that natural language encodes to predict more desirable design metrics, including CAT metrics. We demonstrate the ability of machine learning models to predict design metrics from the design itself and SVS survey information. We show that incorporating natural language processing improves prediction results across design metrics, and that clear distinctions in the predictability of certain metrics exist.

Gate-Level Static Approximate Adders: A Comparative Analysis

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

Bit Design Methodology to Mitigate High-Frequency Torsional Oscillation

This paper reviews field data where high-frequency torsional oscillation (HFTO) was seen on previous bit runs and hypothesizes on features or design metrics that may have directly influenced this vibration. This paper investigates four metrics of bit design: Cutter wear, shear length:shear area ratio, choice of secondary cutter material, and effective backrake. Hypotheses are established linking these metrics to HFTO, and then data from field runs is shown to correlate the hypotheses. At this point, a bit was designed and manufactured to put the HFTO avoidance hypotheses into practice. Prior to laboratory testing, a theoretical model is used to identify resonant torsional frequencies. A series of laboratory experiments followed to test the hypotheses and demonstrated that there is correlation between all factors, but in one case is counter to the hypothesis. This information is of use when selecting or designing bits in environments where HFTO is known to occur. The findings may also assist in explaining performance that's below expectations where HFTO is not able to be explicitly measured.

If a Picture is Worth 1000 Words, Is a Word Worth 1000 Features for Design Metric Estimation?

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because of their ability to encode a plethora of information. When evaluating designs, we aim to capture a range of information as well, information including usefulness, uniqueness, and novelty of a design. The subjective nature of these concepts makes their evaluation difficult. Despite this, many attempts have been made and metrics developed to do so, because design evaluation is integral to innovation and the creation of novel solutions. The most common metrics used are the consensual assessment technique (CAT) and the Shah, Vargas-Hernandez, and Smith (SVS) method. While CAT is accurate and often regarded as the “gold standard,” it heavily relies on using expert ratings as a basis for judgement, making CAT expensive and time consuming. Comparatively, SVS is less resource-demanding, but it is often criticized as lacking sensitivity and accuracy. We aim to take advantage of the distinct strengths of both methods through machine learning. More specifically, this study seeks to investigate the possibility of using machine learning to facilitate automated creativity assessment. The SVS method results in a text-rich dataset about a design. In this paper we utilize these textual design representations and the deep semantic relationships that words and sentences encode, to predict more desirable design metrics, including CAT metrics. We demonstrate the ability of machine learning models to predict design metrics from the design itself and SVS Survey information. We demonstrate that incorporating natural language processing (NLP) improves prediction results across all of our design metrics, and that clear distinctions in the predictability of certain metrics exist. Our code and additional information about our work are available at http://decode.mit.edu/projects/nlp-design-eval/.

A Multi-Objective Genetic Algorithm methodology for sizing and power electronics selection of standalone renewable energy systems

<p>This work proposes a design methodology to optimize multiple design metrics of a stand-alone PV/battery system at the same time. The relevance of each objective can be adjusted by the designer and this paper explores the correlations among them. An application example is proposed, where the objectives are the minimization of investment and operational cost, with a boundary set on the system reliability. The variables are six and represent the size of the generation, storage, and power conversion elements, as well as the converters selection. The example design is repeated with two battery types, Lead-Acid and Li-Ion. The use of a genetic algorithm reduces the computational power, allowing the quick execution of several optimizations with different settings.</p>

A Multi-Objective Genetic Algorithm methodology for sizing and power electronics selection of standalone renewable energy systems

<p>This work proposes a design methodology to optimize multiple design metrics of a stand-alone PV/battery system at the same time. The relevance of each objective can be adjusted by the designer and this paper explores the correlations among them. An application example is proposed, where the objectives are the minimization of investment and operational cost, with a boundary set on the system reliability. The variables are six and represent the size of the generation, storage, and power conversion elements, as well as the converters selection. The example design is repeated with two battery types, Lead-Acid and Li-Ion. The use of a genetic algorithm reduces the computational power, allowing the quick execution of several optimizations with different settings.</p>

Impact of Artificial Noise on Security Capability of Energy Harvesting Overlay Networks

Artificial noise, energy harvesting, and overlay communications can assure design metrics of modern wireless networks such as data security, energy efficiency, and spectrum utilization efficiency. This paper studies impact of artificial noise on security capability of energy harvesting overlay networks in which the cognitive transmitter capable of self-powering its operation by harvesting radio frequency energy and self-securing its communications against eavesdroppers by generating artificial noise amplifies and forwards the signal of the primary transmitter as well as transmits its individual signal concurrently. To quantify this impact, the current paper firstly suggests accurate expressions of crucial security performance indicators. Then, computer simulations are supplied to corroborate these expressions. Finally, numerous results are demonstrated to expose insights into this impact from which optimum specifications are determined. Notably, primary/cognitive communications can be secured at distinct degrees by flexibly controlling multiple specifications of the suggested system model.