Biblio

Arrays of nanosized hollow spheres of Ni were studied using micromagnetic simulation by the Object Oriented Micromagnetic Framework. Before all the results, we will present an analysis of the properties for an individual hollow sphere in order to separate the real effects due to the array. The results in this paper are divided into three parts in order to analyze the magnetic behaviors in the static and dynamic regimes. The first part presents calculations for the magnetic field applied parallel to the plane of the array; specifically, we present the magnetization for equilibrium configurations. The obtained magnetization curves show that decreasing the thickness of the shell decreases the coercive field and it is difficult to obtain magnetic saturation. The values of the coercive field obtained in our work are of the same order as reported in experimental studies in the literature. The magnetic response in our study is dominated by the shape effects and we obtained high values for the reduced remanence, Mr/MS = 0.8. In the second part of this paper, we have changed the orientation of the magnetic field and calculated hysteresis curves to study the angular dependence of the coercive field and remanence. In thin shells, we have observed how the moments are oriented tangentially to the spherical surface. For the inversion of the magnetic moments we have observed the formation of vortex and onion modes. In the third part of this paper, we present an analysis for the process of magnetization reversal in the dynamic regime. The analysis showed that inversion occurs in the nonhomogeneous configuration. We could see that self-demagnetizing effects are predominant in the magnetic properties of the array. We could also observe that there are two contributions: one due to the shell as an independent object and the other due to the effects of the array.

We present an optimization approach that can be employed to calculate the globally optimal segmentation of a 2-D magnetic system into uniformly magnetized pieces. For each segment, the algorithm calculates the optimal shape and the optimal direction of the remanent flux density vector, with respect to a linear objective functional. We illustrate the approach with results for magnet design problems from different areas, such as a permanent magnet electric motor, a beam-focusing quadrupole magnet for particle accelerators, and a rotary device for magnetic refrigeration.

The manufacturing process of electrical machines influences the geometric dimensions and material properties, e.g. the yoke thickness. These influences occur by statistical variation as manufacturing tolerances. The effect of these tolerances and their potential impact on the mechanical torque output is not fully studied up to now. This paper conducts a sensitivity analysis for geometric and material parameters. For the general approach these parameters are varied uniformly in a range of 10 %. Two dimensional finite element analysis is used to simulate the influences at three characteristic operating points. The studied object is an internal permanent magnet machine in the 100 kW range used for hybrid drive applications. The results show a significant dependency on the rotational speed. The general validity is studied by using boundary condition variations and two further machine designs. This procedure offers the comparison of matching qualitative results for small quantitative deviations. For detecting the impact of the manufacturing process realistic tolerance ranges are used. This investigation identifies the airgap and magnet remanence induction as the main parameters for potential torque fluctuation.

The inevitable temperature raise leads to the demagnetization of permanent magnet synchronous motor (PMSM), that is undesirable in the application of electrical vehicle. This paper presents a nonlinear demagnetization model taking into account temperature with the Wiener structure and neural network characteristics. The remanence and intrinsic coercivity are chosen as intermediate variables, thus the relationship between motor temperature and maximal permanent magnet flux is described by the proposed neural Wiener model. Simulation and experimental results demonstrate the precision of temperature dependent demagnetization model. This work makes the basis of temperature compensation for the output torque from PMSM.

Substituting neodymium with ferrite based magnets comes with the penalty of significant reduced magnetic field energy. Several possibilities to compensate for the negative effects of a lower remanence and coercivity provided by ferrite magnets are presented and finally combined into the development of a new kind of BLDC-machine design. The new design is compared to a conventional machine on the application example of an electric 800 W/48 V automotive coolant pump.

This paper presents the analysis and the design of a ferrite permanent magnet synchronous generator (FePMSG) with flux concentration. Despite the well-known advantages of rare earth permanent magnet synchronous generators (REPMSG), the high cost of the rare earth permanent magnets represents an important drawback, particularly in competitive markets like the wind power. To reduce the cost of permanent magnet machines it is possible to replace the expensive rare earth materials by ferrite. Once ferrite has low remanent magnetization, flux concentration techniques are used to design a cheaper generator. The designed FePMSG is compared with a reference rare earth (NdFeB) permanent magnet synchronous generator (REPMSG), both with 3 kW, 220 V and 350 rpm. The results, validated with finite element analysis, show that the FePMSG can replace the REPMSG reducing significantly the active material cost.

REMAN is a reputation management infrastructure for composite Web services. It supports the aggregation of client feedback on the perceived QoS of external services, using reputation mechanisms to build service rankings. Changes in rankings are pro-actively notified to composite service clients to enable self-tuning properties in their execution.

In this paper we propose a mechanism of prediction of domestic human activity in a smart home context. We use those predictions to adapt the behavior of home appliances whose impact on the environment is delayed (for example the heating). The behaviors of appliances are built by a reinforcement learning mechanism. We compare the behavior built by the learning approach with both a merely reactive behavior and a state-remanent behavior.

A brief review is given of the memory properties of non-linear ferroelectric materials in terms of the direction of polarization. A sensitive pulse method has been developed for obtaining static remanent polarization data of ferroelectric materials. This method has been applied to study the effect of pulse duration and amplitude and decay of polarization on ferroelectric ceramic materials with fairly high crystalline orientation. These studies indicate that ferroelectric memory devices can be operated in the megacycle ranges. Attempts have been made to develop electrostatically induced memory devices using ferroelectric substances as a medium for storing information. As an illustration, a ferroelectric memory using a new type of switching matrix is presented having a selection ratio 50 or more.

Contrary to widespread assumption, dynamic RAM (DRAM), the main memory in most modern computers, retains its contents for several seconds after power is lost, even at room temperature and even if removed from a motherboard. Although DRAM becomes less reliable when it is not refreshed, it is not immediately erased, and its contents persist sufficiently for malicious (or forensic) acquisition of usable full-system memory images. We show that this phenomenon limits the ability of an operating system to protect cryptographic key material from an attacker with physical access to a machine. It poses a particular threat to laptop users who rely on disk encryption: we demonstrate that it could be used to compromise several popular disk encryption products without the need for any special devices or materials. We experimentally characterize the extent and predictability of memory retention and report that remanence times can be increased dramatically with simple cooling techniques. We offer new algorithms for finding cryptographic keys in memory images and for correcting errors caused by bit decay. Though we discuss several strategies for mitigating these risks, we know of no simple remedy that would eliminate them.

As non-volatile memory (NVM) technologies are expected to replace DRAM in the near future, new challenges have emerged. For example, NVMs have slow and power-consuming writes, and limited write endurance. In addition, NVMs have a data remanence vulnerability, i.e., they retain data for a long time after being powered off. NVM encryption alleviates the vulnerability, but exacerbates the limited endurance by increasing the number of writes to memory. We observe that, in current systems, a large percentage of main memory writes result from data shredding in operating systems, a process of zeroing out physical pages before mapping them to new processes, in order to protect previous processes' data. In this paper, we propose Silent Shredder, which repurposes initialization vectors used in standard counter mode encryption to completely eliminate the data shredding writes. Silent Shredder also speeds up reading shredded cache lines, and hence reduces power consumption and improves overall performance. To evaluate our design, we run three PowerGraph applications and 26 multi-programmed workloads from the SPEC 2006 suite, on a gem5-based full system simulator. Silent Shredder eliminates an average of 48.6% of the writes in the initialization and graph construction phases. It speeds up main memory reads by 3.3 times, and improves the number of instructions per cycle (IPC) by 6.4% on average. Finally, we discuss several use cases, including virtual machines' data isolation and user-level large data initialization, where Silent Shredder can be used effectively at no extra cost.

Smart mobile devices are becoming the main vessel of personal privacy information. While they carry valuable information, data erasure is somehow much more vulnerable than was predicted. The security mechanisms provided by the Android system are not flexible enough to thoroughly delete sensitive data. In addition to the weakness among several provided data-erasing and file-deleting mechanisms, we also target the Android OS design flaws in data erasure, and unveil that the design of the Android OS contradicts some secure data-erasure demands. We present the data-erasure flaws in three typical scenarios on mainstream Android devices, such as the data clearing flaw, application uninstallation flaw, and factory reset flaw. Some of these flaws are inherited data-deleting security issues from the Linux kernel, and some are new vulnerabilities in the Android system. Those scenarios reveal the data leak points in Android systems. Moreover, we reveal that the data remanence on the disk is rarely affected by the user’s daily operation, such as file deletion and app installation and uninstallation, by a real-world data deletion latency experiment. After one volunteer used the Android phone for 2 months, the data remanence amount was still considerable. Then, we proposed DataRaider for file recovering from disk fragments. It adopts a file-carving technique and is implemented as an automated sensitive information recovering framework. DataRaider is able to extract private data in a raw disk image without any file system information, and the recovery rate is considerably high in the four test Android phones. We propose some mitigation for data remanence issues, and give the users some suggestions on data protection in Android systems.

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.

In this paper we consider recovering data from USB Flash memory sticks after they have been damaged or electronically erased. We describe the physical structure and theory of operation of Flash memories; review the literature of Flash memory data recovery; and report results of new experiments in which we damage USB Flash memory sticks and attempt to recover their contents. The experiments include smashing and shooting memory sticks, incinerating them in petrol and cooking them in a microwave oven.

BootJacker is a proof-of-concept attack tool which demonstrates that authentication mechanisms employed by an operating system can be bypassed by obtaining physical access and simply forcing a restart. The key insight that enables this attack is that the contents of memory on some machines are fully preserved across a warm boot. Upon a reboot, BootJacker uses this residual memory state to revive the original host operating system environment and run malicious payloads. Using BootJacker, an attacker can break into a locked user session and gain access to open encrypted disks, web browser sessions or other secure network connections. BootJacker's non-persistent design makes it possible for an attacker to leave no traces on the victim machine.

Current post-mortem cyber-forensic techniques may cause significant disruption to the evidence gathering process by breaking active network connections and unmounting encrypted disks. Although newer live forensic analysis tools can preserve active state, they may taint evidence by leaving footprints in memory. To help address these concerns we present Forenscope, a framework that allows an investigator to examine the state of an active system without the effects of taint or forensic blurriness caused by analyzing a running system. We show how Forenscope can fit into accepted workflows to improve the evidence gathering process. Forenscope preserves the state of the running system and allows running processes, open files, encrypted filesystems and open network sockets to persist during the analysis process. Forenscope has been tested on live systems to show that it does not operationally disrupt critical processes and that it can perform an analysis in less than 15 seconds while using only 125 KB of memory. We show that Forenscope can detect stealth rootkits, neutralize threats and expedite the investigation process by finding evidence in memory.