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Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur
Over the past decades, research in room acoustics has established several derivative measures of an impulse response, some of which are incorporated in the ISO 3382 standards. These parameters intend to represent perceptual qualities, but were developed without a consistent modeling of room acoustical perception. More recent research proposed comprehensive inventories of room acoustic perception that are purely based on evaluations by human subjects, such as the Room Acoustical Quality Index (RAQI). In this work RA-QI scores acquired for 70 room impulse responses were predicted from room acoustical parameters. Except for Reverberance, the prediction of RAQI factors performed rather poor. In most cases, the sound source had a greater impact on RAQI scores. All analyses are published in an online tool, where users can upload omnidirectional and binaural impulse responses, and instantly obtain and visualize several physical descriptors, as well as predicted RAQI scores for three different sound sources. So far, acceptable prediction accuracy is achieved for Reverberance, Strength, Irregular Decay, Clarity and Intimacy. Larger data sets of evaluated impulse responses are required to improve the model performance and enable reliable predictions of room acoustical quality. Therefore, the administration of RAQI evaluations within the website is currently being developed.
We present a process to develop secure software with an extensive pattern-based security requirements engineering phase. It supports identifying and analyzing conflicts between different security requirements. In the design phase, we proceed by selecting security software components that achieve security requirements. The process enables software developers to systematically identify, analyze, and finally realize security requirements using security software components. We illustrate our approach by a lawyer agency software example.
Considering legal aspects during software development is a challenging problem, due to the cross-disciplinary expertise required. The problem is even more complex for cloud computing systems, because of the international distribution, huge amounts of processed data, and a large number of stakeholders that own or process the data. Approaches exist to deal with parts of the problem, but they are isolated from each other. We present an integrated method for elicitation of legal requirements. A cloud computing online banking scenario illustrates the application of our methods. The running example deals with the problem of storing personal information in the cloud and based upon the BDSG (German Federal Data Protection Act). We describe the structure of the online banking cloud system using an existing pattern-based approach. The elicited information is further refined and processed into functional requirements for software development. Moreover, our method covers the analysis of security-relevant concepts such as assets and attackers particularly with regard to laws. The requirements artifacts then serve as inputs for existing patterns for the identification of laws relevant for the online banking cloud system. Finally, our method helps to systematically derive functional as well as security requirements that realize the previously identified laws.