Perception failures in autonomous vehicles (AV) remain a major safety concern because they are the basis for many accidents. To study how these failures affect safety, researchers typically inject artificial faults into hardware or software components and observe the outcomes. However, existing fault injection studies often target a single sensor or machine perception (MP) module, resulting in siloed frameworks that are difficult to generalize or integrate into unified simulation environments. This work addresses that limitation by reframing perception failures as hallucinations, false perceptions that distort an AV situational awareness and may trigger unsafe control actions. Since hallucinations describe only observable effects, this abstraction enables analysis independent of specific sensors or algorithms, focusing instead on how their faults manifest along the MP pipeline. Building on this concept, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an iterative open-source simulator. More than 18,350 simulations were executed in which hallucinations were injected while AVs crossed an unsignalized transverse street with traffic. The results statistically validate the framework and quantify the impact of each hallucination type on collisions and near misses. Certain hallucinations, such as perceptual latency and drift, significantly increase the risk of collision in the scenario tested, validating the proposed paradigm can stress the AV system safety. The framework offers a scalable, statistically validated, component agnostic, and fully interoperable toolset that simplifies and accelerates AV safety validations, even those with novel MP architectures and components. It can potentially reduce the time-to-market of AV and lay the foundation for future research on fault tolerance, and resilient AV design.

This work proposes a sliding mode control barrier function to robustly deal with high relative-degree safety constraints in safety-critical control systems. Stability/tracking objectives, expressed as a nominal control law, and safety constraints, expressed as control barrier functions are unified through quadratic programming. The proposed control framework is numerically validated considering a Furuta pendulum and a magnetic levitation system. For the first system, a linear quadratic regulator is considered as a nominal control law, and a safety constraint is considered to guarantee that the pendulum angular position never exceeds a predetermined value. For the second one, a sliding mode controller is considered as a nominal control law and multiple safety constraints are considered to guarantee that the magnetic levitation system positions never exceed predetermined values. For both systems, we consider high relative-degree safety constraints robust against model uncertainties. The numerical results indicate that the stability/tracking objectives are reached and the safety constraints are respected even with model uncertainties.

Use real word data to evaluate the performance of the electrocardiographic markers of GEH as features in a machine learning model with Standard ECG features and Risk Factors in Predicting Outcome of patients in a population referred to a tertiary cardiology hospital. Patients forwarded to specific evaluation in a cardiology specialized hospital performed an ECG and a risk factor anamnesis. A series of follow up attendances occurred in periods of 6 months, 12 months and 15 months to check for cardiovascular related events (mortality or new nonfatal cardiovascular events (Stroke, MI, PCI, CS), as identified during 1-year phone follow-ups. The first attendance ECG was measured by a specialist and processed in order to obtain the global electric heterogeneity (GEH) using the Kors Matriz. The ECG measurements, GEH parameters and risk factors were combined for training multiple instances of XGBoost decision trees models. Each instance were optmized for the AUCPR and the instance with higher AUC is chosen as representative to the model. The importance of each parameter for the winner tree model was compared to better understand the improvement from using GEH parameters. The GEH parameters turned out to have statistical significance for this population specially the QRST angle and the SVG. The combined model with the tree parameters class had the best performance. The findings suggest that using VCG features can facilitate more accurate identification of patients who require tertiary care, thereby optimizing resource allocation and improving patient outcomes. Moreover, the decision tree model's transparency and ability to pinpoint critical features make it a valuable tool for clinical decision-making and align well with existing clinical practices.

This work compares open-source electronic design automation tools with a commercial environment using three representative integrated circuit blocks in the IHP 130 nm open PDK: a common-mode noise filter, a finite-state machine, and a voltage-controlled oscillator. The study reports design effort and quality of results for digital logic, including area, power, and timing closure, and examines analog layout feasibility. For the finite-state machine at 50 MHz, the open-source flow reached 0.029 mm$^2$ (post-layout) and 4.37 mW (estimated) with 828 standard cells, whereas the commercial flow achieved 0.019 mm$^2$ and 2.00 mW with 497 cells, corresponding to increases of 53\% in area and 118\% in power. The common-mode noise filter totals 1.879 mm$^2$ with 1703 flip-flops at 50 MHz. The voltage-controlled oscillator occupies 0.0025 mm$^2$ and achieves a simulated maximum oscillation frequency of 2.65 GHz. The contribution is a side-by-side quantification of quality of results across digital and analog blocks in the IHP open PDK. The results indicate that open-source tools are viable for early prototyping, training, and collaboration, while commercial flows retain advantages in automation and quality of results when strict targets on power and area or precision analog layout are required.