AbstractLearning-enabled autonomous systems (LEAS) use machine learning (ML) components for essential functions of autonomous operation, such as perception and control. LEAS are often safety-critical. The development and integration of trustworthy ML components present new challenges that extend beyond the boundaries of system’s design to the system’s operation in its real environment. This paper introduces the methodology and tools developed within the frame of the FOCETA European project towards the continuous engineering of trustworthy LEAS. Continuous engineering includes iterations between two alternating phases, namely: (i) design and virtual testing, and (ii) deployment and operation. Phase (i) encompasses the design of trustworthy ML components and the system’s validation with respect to formal specifications of its requirements via modeling and simulation. An integral part of both the simulation-based testing and the operation of LEAS is the monitoring and enforcement of safety, security and performance properties and the acquisition of information for the system’s operation in its environment. Finally, we show how the FOCETA approach has been applied to realistic continuous engineering workflowsfor three different LEAS from automotive and medical application domains.

After first being standardized by the 3rd Generation Partnership Project (3GPP) in Release 15, 5th Generation (5G) mobile systems have been rapidly deployed worldwide [...].

We suggest a new technique for developing noisy tree data structures. We call it a “walking tree”. As applications of the technique we present a noisy Self-Balanced Binary Search Tree (we use a Red–Black tree as an implementation) and a noisy segment tree. The asymptotic complexity of the main operations for the tree data structures does not change compared to the case without noise. We apply the data structures in quantum algorithms for several problems on strings like the string-sorting problem and auto-complete problem. For both problems, we obtain quantum speed-up. Moreover, for the string-sorting problem, we show a quantum lower bound.

For on-chip oscillator phase noise characterization and monitoring a common technique is the measurement of the equivalent cycle-to-cycle jitter. In this letter we show that the quantization introduced to the recorded jitter by the measurement with a time-to-digital converter can be prohibitive for calculating the phase noise, especially at high oscillation frequencies and for spectrally pure oscillators. A second drawback of this method is that supply voltage noise can mask the phase noise due to intrinsic device noise. To avoid these limitations, we investigate an approach where the oscillator under test is compared to an identical replica. The resulting time difference measurements are introduced as delta jitter. The validity of the method is proven in simulation and measurement of an on-chip jitter measurement macro designed in 16 nm FinFET CMOS technology. The measurement results indicate both robustness against quantization error and a self-canceling of correlated influences on phase noise in delta jitter measurements.