The benefits and costs of netlist randomization based side-channel countermeasures: an in-depth evaluation

Exchanging FPGA-based implementations of cryptographic algorithms during run-time using netlist randomized versions has been introduced recently as a unique countermeasure against side channel attacks. Using partial reconfiguration, it is possible to shuffle between structurally different but functionally similar versions of a cryptographic implementation. The resulting varying power profile enhances the resistance against power-based side channel attacks. While side channel leakage is reduced, costs in terms of additional resources and/or lowered throughput are often increased due to the overheads of the required online partial reconfiguration. In this work, we provide an in-depth evaluation of the leakage-area-throughput trade-off

Investigation of a hybrid switching control system

Bibliography: pages 84-86.A servo motor is to be used to position the cutting arm in a hypothetical pattern generation application. The motor is controlled in closed-loop in order to track, with zero asymptotic error, a reference signal represented by either a sinusoidal, triangular, or square wave. In addition, the schedule of reference signal type changes is not known a priori and the controlled system must achieve asymptotic tracking without operator intervention. As no simple single controller can satisfy these requirements for all setpoint types, a Hybrid Switching Control System is proposed which combines intuitive logic with standard control techniques. Under the guidance of a simple supervisor, the controller corresponding to each type of setpoint is switched in and out of the active feedback loop as required. A simple Multi-layer Perceptron neural network was selected to identify the type of signal being tracked and hence initiate controller switching. This network performed very well even in the presence of measurement noise, and the hybrid system automatically tracked each of the three types of reference signal over a wide range of signal amplitude and frequency. However, the reconfiguration interval was quite long (although still acceptable in terms of the proposed application), and the size of the neural net structure had to be limited for the system to work in real-time

The Sleeping Monster: NuSTAR observations of SGR 1806-20, 11 years after the Giant Flare

We report the analysis of 5 NuSTAR observations of SGR 1806-20 spread over a year from April 2015 to April 2016, more than 11 years following its Giant Flare (GF) of 2004. The source spin frequency during the NuSTAR observations follows a linear trend with a frequency derivative $\dot{\nu}=(-1.25\pm0.03)\times10^{-12}$ Hz s$^{-1}$, implying a surface dipole equatorial magnetic field $B\approx7.7\times10^{14}$ G. Thus, SGR 1806-20 has finally returned to its historical minimum torque level measured between 1993 and 1998. The source showed strong timing noise for at least 12 years starting in 2000, with $\dot{\nu}$ increasing one order of magnitude between 2005 and 2011, following its 2004 major bursting episode and GF. SGR 1806-20 has not shown strong transient activity since 2009 and we do not find short bursts in the NuSTAR data. The pulse profile is complex with a pulsed fraction of $\sim8\%$ with no indication of energy dependence. The NuSTAR spectra are well fit with an absorbed blackbody, $kT=0.62\pm0.06$ keV, plus a power-law, $\Gamma=1.33\pm0.03$. We find no evidence for variability among the 5 observations, indicating that SGR 1806-20 has reached a persistent and potentially its quiescent X-ray flux level after its 2004 major bursting episode. Extrapolating the NuSTAR model to lower energies, we find that the 0.5-10 keV flux decay follows an exponential form with a characteristic timescale $\tau=543\pm75$ days. Interestingly, the NuSTAR flux in this energy range is a factor of $\sim2$ weaker than the long-term average measured between 1993 and 2003, a behavior also exhibited in SGR $1900+14$. We discuss our findings in the context of the magnetar model.Comment: 10 pages, 5 figures, accepted for publication in Ap

Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

Evolution of Network Architecture in a Granular Material Under Compression

As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study suggest that these network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup

An embedded system supporting dynamic partial reconfiguration of hardware resources for morphological image processing

Processors for high-performance computing applications are generally designed with a focus on high clock rates, parallelism of operations and high communication bandwidth, often at the expense of large power consumption. However, the emphasis of many embedded systems and untethered devices is on minimal hardware requirements and reduced power consumption. With the incessant growth of computational needs for embedded applications, which contradict chip power and area needs, the burden is put on the hardware designers to come up with designs that optimize power and area requirements. This thesis investigates the efficient design of an embedded system for morphological image processing applications on Xilinx FPGAs (Field Programmable Gate Array) by optimizing both area and power usage while delivering high performance. The design leverages a unique capability of FPGAs called dynamic partial reconfiguration (DPR) which allows changing the hardware configuration of silicon pieces at runtime. DPR allows regions of the FPGA to be reprogrammed with new functionality while applications are still running in the remainder of the device. The main aim of this thesis is to design an embedded system for morphological image processing by accounting for real time and area constraints as compared to a statically configured FPGA. IP (Intellectual Property) cores are synthesized for both static and dynamic time. DPR enables instantiation of more hardware logic over a period of time on an existing device by time-multiplexing the hardware realization of functions. A comparison of power consumption is presented for the statically and dynamically reconfigured designs. Finally, a performance comparison is included for the implementation of the respective algorithms on a hardwired ARM processor as well as on another general-purpose processor. The results prove the viability of DPR for morphological image processing applications

PV reconfiguration systems: A technical and economic study

Dynamical electrical array reconfiguration strategies for grid-connected PV systems have been proposed as solution to improve energy production due to the mismatch effect of PV plants during partial shading conditions. Strategies are based on the use of dynamic connections between PV panels given by the employment of switches that allow for each panel the series, parallel or exclusion connections, physically changing the electrical connections between the related PV modules, consequentially modifying the layout of the plant. Usually the cost of the dynamic matrix is not taken into account. This novel work evaluates the economic advantages obtained by the use of reconfiguration strategies in PV systems, by taking into consideration the price of energy due to incentives in different European and non-European countries and correlates it with the employment of two types of reconfigurators, with different internal structures. For each of the incentives proposed by the different Countries, the main strength and weakness points of the possible investment are highlighted and critically analyzed. From this analysis, it can be stated that the adoption of reconfiguration systems, in certain cases, can be a very convenient solution

An Error-Detection and Self-Repairing Method for Dynamically and Partially Reconfigurable Systems

Reconfigurable systems are gaining an increasing interest in the domain of safety-critical applications, for example in the space and avionic domains. In fact, the capability of reconfiguring the system during run-time execution and the high computational power of modern Field Programmable Gate Arrays (FPGAs) make these devices suitable for intensive data processing tasks. Moreover, such systems must also guarantee the abilities of self-awareness, self-diagnosis and self-repair in order to cope with errors due to the harsh conditions typically existing in some environments. In this paper we propose a selfrepairing method for partially and dynamically reconfigurable systems applied at a fine-grain granularity level. Our method is able to detect, correct and recover errors using the run-time capabilities offered by modern SRAM-based FPGAs. Fault injection campaigns have been executed on a dynamically reconfigurable system embedding a number of benchmark circuits. Experimental results demonstrate that our method achieves full detection of single and multiple errors, while significantly improving the system availability with respect to traditional error detection and correction methods