Current Compensation Techniques for Low-voltage High-performance Current Mirror Circuits

This paper presents two current mirror circuits for low-voltage applications. Unlike most current mirrors that use stacked transistors in the output branch to boost the output resistance, the proposed designs use current compensation techniques to achieve high output resistance. By avoiding stacked transistors in the output branch, the minimum output voltages of the proposed circuits are significantly lower compared to those of other current mirror circuits with comparable output resistance. Particularly, the first design emphasizes on reducing the minimum output voltage to an extremely low level of around 20mV. The second design stresses minimizing implementation cost. Compared to a simple current mirror circuit, the second design requires only one additional transistor but boosts the output resistance by more than 10 times. Both circuit analysis and simulations are presented to examine the performance of the proposed designs

Digital LDO modelling techniques for performance estimation at early design stage

This work studies the transient responses and steady-state ripples of digital low dropout (LDO) voltage regulators. Simulation models as well as closed-form expressions are provided for estimating the LDO output settling behaviour after load current or reference voltage changes. Estimation equations for the magnitude and frequency of LDO output steady-state ripples are also presented. The accuracy of the developed models is verified by comparing estimation data with results obtained from circuit simulations. The use of the developed estimation equations in design space exploration is also demonstrated

Design of Scalable Hardware-Efficient Compressive Sensing Image Sensors

This work presents a new compressive sensing (CS) measurement method for image sensors, which limits pixel summation within neighbor pixels and follows regular summation patterns. Simulations with a large set of benchmark images show that the proposed method leads to improved image quality. Circuit implementation for the proposed CS measurement method is presented with the use of current mode pixel cells; and the resultant CS image sensor circuit is significantly simpler than existing designs. With compression rates of 4 and 8, the developed CS image sensors can achieve 34.2 dB and 29.6 dB PSNR values with energy consumption of 1.4 mJ and 0.73 mJ per frame, respectively

A Probabilistic Fatigue Strength Assessment in AlSi-Cast Material by a Layer-Based Approach

An advanced lightweight design in cast aluminium alloys features complexly shaped geometries with strongly varying local casting process conditions. This affects the local microstructure in terms of porosity grade and secondary dendrite arm spacing distribution. Moreover, complex service loads imply changing local load stress vectors within these components, evoking a wide range of highly stressed volumes within different microstructural properties per load sequence. To superimpose the effects of bulk and surface fatigue strength in relation to the operating load sequence for the aluminium alloy EN AC 46200, a layer-based fatigue assessment concept is applied in this paper considering a non-homogeneous distribution of defects within the investigated samples. The bulk fatigue property is now obtained by a probabilistic evaluation of computed tomography results per investigated layer. Moreover, the effect of clustering defects of computed tomography is studied according to recommendations from the literature, leading to a significant impact in sponge-like porosity layers. The highly stressed volume fatigue model is applied to computed tomography results. The validation procedure leads to a scattering of mean fatigue life from −2.6% to 12.9% for the investigated layers, inheriting strongly varying local casting process conditions

In-medium Hadrons - Properties, Interaction and Formation

In this talk various aspects of in-medium behavior of hadrons are discussed with an emphasis on observable effects. Examples for theoretical predictions of in-medium spectral functions are given and the importance of resonance-hole excitations is stressed. It is also stressed that final state interactions can have a major effect on observables and thus have to be considered as part of the theory. This is demonstrated with examples from neutrino-nucleus interactions. Finally, the possibility to access hadron formation times in high-energy photonuclear (or neutrino-induced) reactions is illustrated.Comment: Invited talk given by U. Mosel at Vth Conference on Hadronic Physics, ICTP, Trieste, May 200

QuantUM: Quantitative Safety Analysis of UML Models

When developing a safety-critical system it is essential to obtain an assessment of different design alternatives. In particular, an early safety assessment of the architectural design of a system is desirable. In spite of the plethora of available formal quantitative analysis methods it is still difficult for software and system architects to integrate these techniques into their every day work. This is mainly due to the lack of methods that can be directly applied to architecture level models, for instance given as UML diagrams. Also, it is necessary that the description methods used do not require a profound knowledge of formal methods. Our approach bridges this gap and improves the integration of quantitative safety analysis methods into the development process. All inputs of the analysis are specified at the level of a UML model. This model is then automatically translated into the analysis model, and the results of the analysis are consequently represented on the level of the UML model. Thus the analysis model and the formal methods used during the analysis are hidden from the user. We illustrate the usefulness of our approach using an industrial strength case study.Comment: In Proceedings QAPL 2011, arXiv:1107.074

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.Comment: Intelligent Vehicle Conference (oral presentation