Publikationen

The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate- and oscillation-based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between somatostatin, parvalbumin, and vasoactive intestinal polypeptide cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high-frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.

The term ‘biological material’ includes many meanings, and here it means materials that constitute living organisms. The variety of material parts of living organisms is huge. They may be hard and soft, elastic and viscoelastic, quite often sizes of constitutive parts are within micro or nano scales and they can be considered as structured biocomposite materials. The traditional methods of materials testing are not applicable to evaluating mechanical properties of materials of very small volumes. It is also very difficult to apply traditional approaches for characterization of very soft materials. Therefore, indentation techniques are widely used to estimate mechanical properties of biological materials. In this review paper, we briefly discuss some results related to mechanics of contact between an indenter and a deformable sample. Then we critically examine the common approaches to interpretation of indentation experimental data. Finally we discuss the results of indentation tests of biomaterials having rather different properties: bones, snake skins, and cartilages, along with resilin and elastin-based materials. We argue that also depth-sensing indentation is a valuable tool for studying mechanical properties of biomaterials, one should be aware that the theoretical models used for justification of modern nanoindentation tests are based on non-adhesive contact, while the influence of adhesive interactions increases as the scale of samples goes down to micro and nanoscales.

Abstract: The here presented study investigated the influence of magnetic particle-particle interactions within the MPI tracer MNP on the imaging performance of the tracers. To realize a proper separation and to increase the distances between the individual tracer MNP (which results in reduced particle-particle interactions), the MNP were diluted by non-magnetic SiO2 spacers in the nanometer range. The obtained MNP/SiO2 mixtures were characterized and used to build up measurement phantoms by embedding the mixture into a long-term stable polymer matrix. In MPS and MPI measurements it was found that reduction of the magnetic interactions encompassed by increasing the MNP distances leads for the tested tracer system to a weaker decrease of higher harmonics in the MPS spectra after immobilization of the particles and thereby, a higher spatial MPI resolution can be achieved.

Recently developed fault classification methods for industrial processes are mainly data-driven. Notably, models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns. However, these data-driven models are vulnerable to adversarial attacks; thus, small perturbations on the samples can cause the models to provide incorrect fault predictions. Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples. This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system: Only one variable can be perturbed to craft adversarial samples. Moreover, to hide the adversarial samples in the visualization space, a Jacobian matrix is used to guide the perturbed variable selection, making the adversarial samples in the dimensional reduction space invisible to the human eye. Using the one-variable attack (OVA) method, we explore the vulnerability of industrial variables and fault types, which can help understand the geometric characteristics of fault classification systems. Based on the attack method, a corresponding adversarial training defense method is also proposed, which efficiently defends against an OVA and improves the prediction accuracy of the classifiers. In experiments, the proposed method was tested on two datasets from the Tennessee-Eastman process (TEP) and steel plates (SP). We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets. For industrial fault classification systems, the attack success rate of our method is close to (on TEP) or even higher than (on SP) the current most effective first-order white-box attack method, which requires perturbation of all variables.

Side-Channel Analysis (SCA) requires the detection of the specific time frame within which Cryptographic Operations (COs) take place in the side-channel signal. In laboratory conditions with full control over the Device under Test (DuT), dedicated trigger signals can be implemented to indicate the start and end of COs. For real-world scenarios, waveform-matching techniques have been established which compare the side-channel signal with a template of the CO's pattern in real time to detect the CO in the side channel. State-of-the-art approaches are implemented on Field-Programmable Gate Arrays (FPGAs). However, current waveform-matching designs process the samples from Analog-to-Digital Converters (ADCs) sequentially and can only work with low sampling rates due to the limited clock speed of FPGAs. This makes it increasingly difficult to apply existing techniques on modern DuTs that operate with clock speeds in the GHz range. In this paper, we present a parallel waveform-matching architecture that is capable of performing waveform matching at the speed of fast ADCs. We implement the proposed architecture in a high-end FPGA-based digitizer and deploy it to detect AES COs from the side channel of a single-board computer operating at 1 GHz. Our implementation allows for waveform matching at 10 GS/s with high accuracy, thus offering a speedup of 50× compared to the fastest state-of-the-art implementation known to us.