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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.

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.

LIN is a low-cost, low-speed vehicle communication sub-bus becoming increasingly pervasive in automotive subsystems. It is a simple, UART-based master-slave protocol designed as a low-speed supplement to a CAN or FlexRay bus. Its primary application is cabin comfort and human interface hardware such as dashboard controls, power seat harnesses, and power door/window systems. As automotive network designers attempt to reduce wiring complexity and lower system cost, modular, inexpensive sub-buses like LIN become an attractive option. This paper presents an overview of the LIN standard and its applications, and then proposes an architecture for rapid development of LIN networks via hardware simulations of LIN nodes. Using inexpensive, off-the-shelf hardware, LIN sensor and actuator applications can be tested in-place without microcode development, speeding overall network development time.

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.

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.

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.

In many measurement applications, there is a need to correlate data acquired from different systems or synchronize systems together with precise timing. Signal Based and Time Based are the two basic methods of synchronizing instrumentation. In Signal Based synchronization, clocks and triggers are physically connected between systems. Typically this provides the highest precision synchronization. In many NVH applications size and distance constrains physically connecting the systems needed for making measurements though the inter-channel phase information of simultaneously sampled signals is crucial. In Time Based synchronization, system components have a common reference of what time it is. Events, triggers and clocks can be generated based on this time.

Vehicle communication networks are challenged by increasing demands for bandwidth, safety, and security. New data is coming into the vehicle from personal devices (e.g. mobile phones), infotainment systems, camera-based driver assistance, and wireless communication with other vehicles and infrastructure. Ethernet (IEEE 802.3) provides high levels of bandwidth and security, making it a potential solution to the challenges of vehicle communication networks. However, in order to be used in control applications, Ethernet must provide known timing performance (e.g. bounded latency and jitter), and in some cases redundancy. This paper explores use of Ethernet for in-vehicle control applications.

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.

Several advanced signal processing algorithms beyond the FFT such as time-frequency analysis, quefrency, cestrum, wavelet analysis, and AR modeling uses are outlined. These advanced algorithms can solve some sound and vibration challenges that FFT-based algorithms cannot solve. Looking at signal characteristics of a unit under test in the time-frequency plane, it is possible to get a better understanding of signal characteristics. This is an overview of these algorithms and some application examples, such as speaker testing, bearing fault detection, dashboard motor testing, and engine knock detection where they can be applied to NVH applications.