543 research outputs found
A Physical Unclonable Function Based on Inter-Metal Layer Resistance Variations and an Evaluation of its Temperature and Voltage Stability
Keying material for encryption is stored as digital bistrings in non-volatile memory (NVM) on FPGAs and ASICs in current technologies. However, secrets stored this way are not secure against a determined adversary, who can use probing attacks to steal the secret. Physical Unclonable functions (PUFs) have emerged as an alternative. PUFs leverage random manufacturing variations as the source of entropy for generating random bitstrings, and incorporate an on-chip infrastructure for measuring and digitizing the corresponding variations in key electrical parameters, such as delay or voltage. PUFs are designed to reproduce a bitstring on demand and therefore eliminate the need for on-chip storage. In this dissertation, I propose a kind of PUF that measures resistance variations in inter-metal layers that define the power grid of the chip and evaluate its temperature and voltage stability. First, I introduce two implementations of a power grid-based PUF (PG-PUF). Then, I analyze the quality of bit strings generated without considering environmental variations from the PG-PUFs that leverage resistance variations in: 1) the power grid metal wires in 60 copies of a 90 nm chip and 2) in the power grid metal wires of 58 copies of a 65 nm chip. Next, I carry out a series of experiments in a set of 63 chips in IBM\u27s 90 nm technology at 9 TV corners, i.e., over all combination of 3 temperatures: -40oC, 25oC and 85oC and 3 voltages: nominal and +/-10% of the nominal supply voltage. The randomness, uniqueness and stability characteristics of bitstrings generated from PG-PUFs are evaluated. The stability of the PG-PUF and an on-chip voltage-to-digital (VDC) are also evaluated at 9 temperature-voltage corners. I introduce several techniques that have not been previously described, including a mechanism to eliminate voltage trends or \u27bias\u27 in the power grid voltage measurements, as well as a voltage threshold, Triple-Module-Redundancy (TMR) and majority voting scheme to identify and exclude unstable bits
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ReSCon '09, Research Student Conference: Book of Abstracts
The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Micro-Switch Design and Its Optimization Using Pattern Search Algorithm for Application in Reconfigurable Antenna
This chapter reports the design and optimization algorithm of metal-contact RF microswitch. Various important evolutionary optimization techniques that can be used to optimize non-linear and even non-differentiable types of radio frequency (RF) circuitâs problems are also reviewed. The transient response of the proposed switch shows displacement time (i.e., squeezed-film damping effect) of 5.0 Îźs and pull-in voltage varying from 9.0 to 9.25 V. Primarily, the switch exhibits insertion loss of 0.15 to 0.51 dB in on-position and isolation of 75.96 to 35.83 dB in off-position at 0.1â10 GHz. Also, the proposed RF switch equivalent circuit and layout are validated in ADS software which was earlier simulated in HFSS. A pattern search (PS) algorithm is used to optimize RF characteristics of the proposed switch after a brief review of the different optimization techniques. After optimization, the switch shows decrement in insertion loss and increment in isolation at 0.1â10 GHz. Further, two such optimized switches are introduced on the defected ground structure (DGS) antenna to make it reconfigurable in terms of frequency. Reconfigurable antenna (RA) is simulated using HFSS software and simulation results are verified by showing the mark of agreement with the fabrication results. The novelty in the proposed design is due to dual-band behavior and better resonance performance than antennas available in the literature. Attractions of proposed RA are its miniaturization and its utility in IEEE US S-(2.0â4.0 GHz) and C-(4.0â8.0 GHz) band
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
Fullwave Maxwell inverse design of axisymmetric, tunable, and multi-scale multi-wavelength metalenses
We demonstrate new axisymmetric inverse-design techniques that can solve
problems radically different from traditional lenses, including
\emph{reconfigurable} lenses (that shift a multi-frequency focal spot in
response to refractive-index changes) and {\emph{widely separated}}
multi-wavelength lenses (m and m). We also present
experimental validation for an axisymmetric inverse-designed monochrome lens in
the near-infrared fabricated via two-photon polymerization. Axisymmetry allows
fullwave Maxwell solvers to be scaled up to structures hundreds or even
thousands of wavelengths in diameter before requiring domain-decomposition
approximations, while multilayer topology optimization with degrees
of freedom can tackle challenging design problems even when restricted to
axisymmetric structures.Comment: 13 pages, 6 figure
Computational analysis and verifications of characteristic modes in real materials
Despite its long history, the Theory of Characteristic Modes has only been utilized in antenna design for perfect electric conductors. This is due to computational problems associated with dielectrics and magnetic materials. In particular, the symmetric form of the PMCHWT surface formulation for the Method of Moments (MoM) solves for both external (real) and internal (non-real) resonances of a structure. The external resonances are the characteristic modes, whereas the internal resonances are not. This article proposes a new post-processing method capable of providing unique and real characteristic modes in all physical mediums, including lossy magnetic and dielectric materials. The method removes the internal resonances of a structure by defining a minimum radiated power, which is found through utilizing the physical bounds of the structure. The characteristic modes found using the proposed method are verified through the use of a MoM volume formulation, time domain antenna simulations, and experiments involving multiple antenna prototypes
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the
sensors of choice for depth perception as they are typically cheaper and more
versatile than their active counterparts. Their biggest drawback, however, is
that they do not directly sense depth maps; instead, these must be estimated
through data-intensive processes. Therefore, appropriate algorithm selection
plays an important role in achieving the desired performance characteristics.
Motivated by applications in space and mobile robotics, we implement and
evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering
one of the best trade-offs between efficiency and accuracy, ELAS has only been
shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all
intriguing properties of the original algorithm, such as the slanted plane
priors, but can achieve a frame rate of 47fps whilst consuming under 4W of
power. Unlike previous FPGA based designs, we take advantage of both components
on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate
more complex and computationally diverse algorithms for such low power,
real-time systems.Comment: 8 pages, 7 figures, 2 table
Recognition of Planar Segments in Point Cloud Based on Wavelet Transform
Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies
EO-ALERT: A Novel Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Civil Alerts
Satellite Earth Observation (EO) data is ubiquitously used in many applications, providing basic services to
society, such as environment monitoring, emergency management and civilian security. Due to the increasing request
of EO products by the market, the classical EO data chain generates a severe bottleneck problem, further exacerbated
in constellations. A huge amount of EO raw data generated on-board the satellite must be transferred to ground,
slowing down the EO product availability, increasing latency, and hampering the growth of applications in
accordance with the increased user demand.
This paper provides an overview of the results achieved by the EO-ALERT project (http://eo-alert-h2020.eu/), an
H2020 European Union research activity led by DEIMOS Space. EO-ALERT proposes the definition and
development of the next-generation EO data processing chain, based on a novel flight segment architecture that
moves optimised key EO data processing elements from the ground segment to on-board the satellite, with the aim of
delivering the EO products to the end user with very low latency (quasi-real-time). EO-ALERT achieves, globally,
latencies below five minutes for EO products delivery, reaching latencies below 1 minute in some scenarios.
The proposed architecture solves the above challenges through a combination of innovations in the on-board
elements of the data chain and the communications. Namely, the architecture introduces innovative technological
solutions, including on-board reconfigurable data handling, on-board image generation and processing for the
generation of alerts (EO products) using Artificial Intelligence (AI), on-board data compression and encryption using
AI, high-speed on-board avionics, and reconfigurable high data rate communication links to ground, including a
separate chain for alerts with minimum latency and global coverage.
The paper presents the proposed architecture, its performance and hardware, considering two different user
scenarios; ship detection and extreme weather observation/nowcasting. The results show that, when implemented
using COTS components and available communication links, the proposed architecture can deliver alerts to ground
with latency lower than five minutes, for both SAR and Optical missions, demonstrating the viability of the EOALERT
concept and architecture. The paper also discusses the implementation on an avionics test bench for
testing the architecture with real EO data, with the aim of demonstrating that it can meet the requirements of the
considered scenarios in terms of detection performance and provides technologies at a high TRL (4-5). When
proven, this will open unprecedented opportunities for the exploitation of civil EO products, especially in latency
sensitive scenarios, such as disaster management
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