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To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

Almost every automotive noise, vibration, and harshness (NVH) engineer who has ever looked at a fast Fourier transform knows the difference between instruments with 90 dB and instruments with 120 dB of dynamic range. NVH engineers understand instrumentation specs such as 24-bit resolution analog-to-digital converters and alias-free signal bandwidth. However, with modern noise and vibration instrumentation systems now being almost completely built on the PC, these specs neglect the most important X factor: the PC itself. No other aspect can affect the performance of an instrumentation system for a sensor array more than the components of the PC. Fortunately, a variety of off-the-shelf PC technologies built on industry standards are available to make it easier and less expensive than ever before to instrument and manage data from large systems. But an NVH engineer must wade through a sea of options to choose the right PC technologies for desired instrumentation system performance.

Modular instrumentation is being widely used in noise and vibration measurement systems that demand higher channel counts and the wider dynamic range that 24-bit delta-sigma ADCs make available at lower costs. This is an overview how flexible modular instrumentation employing the latest software technology can be used in making high precision noise and vibration measurements where higher sampling rates, higher channel counts, increased dynamic range, and distributed architectures were needed in smaller packages. An example where this is being used is in acoustic beam forming in aircraft pass by noise tests to measure and distinguish engine and airframe noise sources.

The increasing number of features and complexity of today's automotive software architectures bring new challenges to the product development cycle. As a product is being developed, there is a need for information created during the early phases to flow seamlessly into subsequent phases. For example, information defined for an ECU during the design phase should be re-used when that ECU is tested during manufacture. Challenges often arise from the fact that one vendor's tools may be appropriate for design, but a different vendor's tools are best suited for manufacturing test. This paper discusses business and technical issues surrounding the transfer of information between such tools. Two case studies are used for discussion. One deals with databases describing signals transferred over an in-vehicle network and the other discusses simulation models as both transition from early designs through various test phases.

Military jet aircraft expose both ground maintenance personnel and the community to high levels of noise. The U.S. Department of Defense is funding research to develop advanced modeling tools for noise reduction techniques and community noise exposure. A large-scale microphone array for portable near-field acoustic holography (NAH) and data acquisition system was created for this purpose. The system was designed for measuring high-amplitude jet noise from current and next-generation military aircraft to provide model refinement and benchmarking, evaluate performance of noise control devices, and predict ground maintenance personnel and community noise exposure. The acoustical instrumentation system was designed to be easy to use with scalable data processing as the primary focus. The data acquisition system allowed up to 152 channels simultaneously sampled at a rate of 96 kHz.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

Empowering the end-user is the primary focus of most software developers, whether in the general computing industry or in automotive instrumentation applications. End-users' expectations for both ease of use and flexibility in software products are high. Products in the general computing industry, such as Microsoft Word and Excel, have set standards for what a user expects from a software product. Because of the complex nature of most analytical instrumentation applications, it is difficult to deliver a software product for these applications that is both easy to use and flexible for many applications. And, if the product does exist, it usually comes with a relatively high price tag. There are, however, some lower cost software development tools available for instrumentation applications that combine a good mix of flexibility and ease of use. These tools require some development on the part of the end-user, but they do not require a computer science background.

This is an overview of the development of a portable, real-time acoustic beamformer based on FPGA (Field Programmable Gate Arrays) and digital microphones for noise source identification. Microphone arrays can be a useful tool in identifying noise sources and give designers an image of noise distribution. The beamforming algorithm is a classic and efficient algorithm for signal processing of microphone arrays and is the core of many microphone array systems. High-speed real-time beamforming has not been implemented much in a portable instrument because it requires large computational resources. Utilizing a beamforming algorithm running on a Field Programmable Gate Array (FPGA), this camera is able to detect and locate both stationary and moving noise sources. A high-resolution optical camera located in the middle of the device records images at a rate of 25 frames per second. The use of the FPGA technology and digital microphones provides increased performance, reduced cost and weight.

Test and validation of control systems for hybrid vehicle power trains provide a unique set of challenges. Not only does the electronic control unit (ECU), or pair of ECUs, need to smoothly coordinate power flow between two or more power plants, but it also must handle the power electronics' high-speed dynamics due to PWM signals frequently in the 10-20 kHz range. The trend in testing all-electric and hybrid-electric ECUs has moved toward using field-programmable gate arrays (FPGAs) as the processing node for simulating inverter and electric motor dynamics in real time. Acting as a purpose-built processor colocated with analog and digital input and output, the FPGA makes it possible for real-time simulation loop rates on the order of one microsecond.