One of the first tasks in engineering embedded systems is the selection of the hardware; more specifically, the MicroController Units (MCUs). This selection is driven by business, technical, organizational, and legal constraints. Often, the hardware is delivered together with proprietary environments in which the software to be deployed has to be developed. This imposes architecturally significant constraints that are usually communicated inappropriately (in terms of time and format) to the engineering team. Examples are the usage of proprietary data types and programming language constructs (e.g., macros in C) and limitations to the reuse of existing software assets. To overcome this challenge, we propose an approach that has two main constituents: (i) the Embedded System Checklist, which aims at guiding the selection of MCUs according to the architecture constraints; and (ii) the Embedded System ESDT Dependency (SED) Architecture View, which connects the software design view with the technical view, describing hardware-related development environment limitations and their influences on the software architecture. We evaluated the application of these concepts in two industrial projects and show that making these dependencies transparent saves a lot of effort during software system development.

In this work, we address an acoustic beamforming application where two speakers are simultaneously active. We construct one subband domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly adjust the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). In order to calculate the mutual information of the complex subband snapshots, we consider four probability density functions (pdfs), namely the Gaussian, Laplace, K0and Г pdfs. The latter three belong to the class of super-Gaussian density functions that are typically used in independent component analysis as opposed to conventional beamforming. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge. In the experiments, the delay-and-sum beamformer achieved a word error rate (WER) of 70.4 %. The MMI beamformer under a Gaussian assumption achieved 55.2 % WER which was further reduced to 52.0 % with a K0pdf, whereas the WER for data recorded with close-talking microphone was 21.6 %.

In this paper, we consider an acoustic beamforming application where two speakers are simultaneously active. We construct one subband-domain beamformer in generalized sidelobe canceller (GSC) configuration for each source. In contrast to normal practice, we then jointly optimize the active weight vectors of both GSCs to obtain two output signals with minimum mutual information (MMI). Assuming that the subband snapshots are Gaussian-distributed, this MMI criterion reduces to the requirement that the cross-correlation coefficient of the subband outputs of the two GSCs vanishes. We also compare separation performance under the Gaussian assumption with that obtained from several super-Gaussian probability density functions (pdfs), namely, the Laplace$K_0$and$\Gamma$pdfs. Our proposed technique provides effective nulling of the undesired source, but without the signal cancellation problems seen in conventional beamforming. Moreover, our technique does not suffer from the source permutation and scaling ambiguities encountered in conventional blind source separation algorithms. We demonstrate the effectiveness of our proposed technique through a series of far-field automatic speech recognition experiments on data from the PASCAL Speech Separation Challenge (SSC). On the SSC development data, the simple delay-and-sum beamformer achieves a word error rate (WER) of 70.4%. The MMI beamformer under a Gaussian assumption achieves a 55.2% WER, which is further reduced to 52.0% with a$K_0$pdf, whereas the WER for data recorded with a close-talking microphone is 21.6%.