We propose a meta-language-based approach enabling Large Language Models (LLMs) to reliably generate structured, machine-readable artifacts referred to as Meta-Languagedefined Structures (MLDS) adapted to domain requirements, without adhering strictly to standard formats like JSON or XML.By embedding explicit schema instructions within prompts, we evaluated the method across diverse use cases, including automated Virtual Reality environment generation and automotive security modeling.Our experiments demonstrate that the meta-language approach significantly improves LLM-generated structure compliance, with an 88% validation rate across 132 test scenarios.Compared to traditional methods using LangChain and Pydantic, our MLDS method reduces setup complexity by approximately 80%, despite a marginally higher error rate.Furthermore, the MLDS artifacts produced were easily editable, enabling rapid iterative refinement.This flexibility greatly alleviates the "blank page syndrome" by providing structured initial artifacts suitable for immediate use or further human enhancement, making our approach highly practical for rapid prototyping and integration into complex industrial workflows.

The climate crisis has highlighted the environmental impact of information and communication technologies (ICT), underscoring the need for sustainable solutions to reduce carbon emissions from ICT. As video streaming continues to dominate Internet traffic, research in this field has become increasingly important. The energy consumption of OLED TVs relies on the video brightness. Thus, an approach to optimized the energy consumption is reducing the video brightness. In this work, we provide an open dataset for energy consumption in OLED TVs while playing videos with various brightness levels. The dataset comprises the energy data of four OLED TVs with different screen sizes and manufacturers in playing 176 videos in a range of dark and bright content. As results, 704 data traces of energy consumption are collected.

Rapid advances of connected and autonomous vehicle technology have led to an increase in cyber-attacks.This in turn has driven the development of the ISO 21434 standard aimed at supporting the management of cybersecurity risks in the automotive industry.There is, however, a disconnect between the standard and the currently applied model-based development approaches that are increasingly applied for systems and software development.In this paper, we present tool support created for model-based automotive cybersecurity engineering.This tool is built upon the existing automotive systems development language, EAST-ADL, with extensions to address security in accordance with the ISO 21434 standard covering modeling support, calculation of security-related metrics such as impact, risk, and attack feasibility, and generation of ISO 21434 compliant security threat reports.Meeting the requirements of cybersecurity engineeering according to ISO 21434 are demonstrated with two examples.

We are interested in the problem of time-optimal control of omnidirectional robots with bounded acceleration (TOC-ORBA). While there exist approximate solutions for such problems, and exact solutions with unbounded acceleration, exact solvers to the TOC-ORBA problem have remained elusive until now. In this paper, we present a real-time solver for true time-optimal control of omnidirectional robots with bounded acceleration. We first derive the general parameterized form of the solution to the TOC-ORBA problem by application of Pontryagin's maximum principle. We then frame the boundary value problem of TOC-ORBA as an optimization problem over the parameterized control space. To overcome local minima and poor initial guesses to the optimization problem, we introduce a two-stage optimal control solver (TSOCS): The first stage computes an upper bound to the total time for the TOC-ORBA problem and holds the time constant while optimizing the parameters of the trajectory to approach the boundary value conditions. The second stage uses the parameters found by the first stage, and relaxes the constraint on the total time to solve for the parameters of the complete TOC-ORBA problem. Furthermore, we implement TSOCS as a closed loop controller to overcome actuation errors on real robots in realtime. We empirically demonstrate the effectiveness of TSOCS in simulation and on real robots, showing that 1) it runs in real time, generating solutions in less than 0.5ms on average; 2) it generates faster trajectories compared to an approximate solver; and 3) it is able to solve TOC-ORBA problems with nonzero final velocities that were previously unsolvable in real-time.

Despite continuous progress in robust automatic speech recognition acoustic mismatch between training and test conditions is still a major problem. Consequently, large speech collections must be conducted in many environments. An alternative approach is to generate training data synthetically by filtering clean speech with impulse responses and/or adding noise signals from the target domain. We compare the performance of a speech recognizer trained on recorded speech in the target domain with a system trained on suitably transformed clean speech. In order to obtain comparable results, our experiments are based on two channel recordings with a close talk and a distant microphone which produce the clean signal and the target domain signal respectively. By filtering and adding noise we obtain error rates which are only 10% higher for natural number recognition and 30% higher for a command recognition task compared to training with target domain data.

Despite continuous progress in robust automatic speech recognition acoustic mismatch between training and test conditions is still a major problem. Consequently, large speech collections must be conducted in many environments. An alternative approach is to generate training data synthetically by filtering clean speech with impulse responses and/or adding noise signals from the target domain. We compare the performance of a speech recognizer trained on recorded speech in the target domain with a system trained on suitably transformed clean speech. In order to obtain comparable results, our experiments are based on two channel recordings with a close talk and a distant microphone which produce the clean signal and the target domain signal respectively. By filtering and adding noise we obtain error rates which are only 10% higher for natural number recognition and 30% higher for a command recognition task compared to training with target domain data.

Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps. First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener filtering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 different cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener filtering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with artificial, non-stationary noise.

Data collections in the car environment require much more effort in terms of cost and time as compared to the telephone or the office environment. Therefore we apply supervised database adaptation from the telephone environment to the car environment to allow quick setup of car environment recognizers. Further reduction of word error rate is obtained by unsupervised online adaptation during recognition. We investigate the common techniques MLLR and MAP for that purpose. We give results on command word recognition in the car environment for all combinations of database and online adaptation in task-dependent and task-independent scenarios. The possibility of setting up speech recognizers for the car environment based on telephone data and a limited amount of adaptation material from the car environment is demonstrated.

Automatic discrimination between music, speech and noise has grown in importance as a research topic over recent years. The need to classify audio into categories such as music or speech is an important part of the multimedia document retrieval problem. This paper extends work previously carried out by the authors which compared performance of static and transitional features based on cepstra, amplitude, zerocrossings and pitch for music and speech discrimination. Two approaches are described to combine the features to improve overall performance. The first approach uses separate GMM classifiers for each feature type and fuses the outputs of the classifiers. The second approach combines different features into a single vector prior to modelling the data with a GMM. Significant improvements in performance have been observed using both approaches over the results achieved by a single type of feature. An equal error rate of 0.3% is achieved for the best system on ten second tests using seventeen hours of test material. The performance is maintained as the length of test file is reduced with an equal error rate of less than 1% being achieved with only two seconds of data.

This paper presents results of speaker-independent speech recognition experiments concerning acoustic front-ends, models and their structures in car environments. The database comprises 350 speakers in 6 different cars. We investigate whole-word models, context-independent phoneme models and context-dependent within-word phoneme models. We studied task-dependent (same vocabulary context in training and test) phoneme models and present first results on task-independent (broad context in training, i.e. phonetically rich material) scenarios. The latter allows flexible vocabulary definition for applications with dynamically changing command words or new applications avoiding an expensive data collection. Acoustic preprocessing is carried out with mel-cepstrum combined with spectral subtraction and SNR normalization. The task-dependent word error rates are well below 3% for both whole-word and phoneme models. The task-independent scenarios have to be worked on further.

This paper describes speaker-independent speech recognition experiments concerning acoustic front end processing on a speech database that was recorded in 3 different cars. We investigate different feature analysis approaches (mel-filter bank, mel-cepstrum, perceptually linear predictive coding) and present results with noise compensation techniques based on spectral subtraction. Although the methods employed lead to considerable error rate reduction the error analysis shows that low signal-to-noise ratios are still a problem