1,288 research outputs found
A Complex Model via Phase-Type Distributions to Study Random Telegraph Noise in Resistive Memories
A new stochastic process was developed by considering the internal performance of
macro-states in which the sojourn time in each one is phase-type distributed depending on time.
The stationary distribution was calculated through matrix-algorithmic methods and multiple interesting
measures were worked out. The number of visits distribution to a determine macro-state
were analyzed from the respective differential equations and the Laplace transform. The mean
number of visits to a macro-state between any two times was given. The results were implemented
computationally and were successfully applied to study random telegraph noise (RTN) in resistive
memories. RTN is an important concern in resistive random access memory (RRAM) operation.
On one hand, it could limit some of the technological applications of these devices; on the other
hand, RTN can be used for the physical characterization. Therefore, an in-depth statistical analysis to
model the behavior of these devices is of essential importance.Spanish Ministry of Science, Innovation and Universities (FEDER program)
MTM2017-88708-P
TEC2017-84321-C4-3-RGovernment of Andalusia (Spain)
FQM-307Andalusian Ministry of Economy, Knowledge, Companies and Universities
A-TIC-117-UGR18
FPU18/0177
Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.
The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome.
This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd
Automated 3D model generation for urban environments [online]
Abstract
In this thesis, we present a fast approach to automated
generation of textured 3D city models with both high details at
ground level and complete coverage for birds-eye view.
A ground-based facade model is acquired by driving a vehicle
equipped with two 2D laser scanners and a digital camera under
normal traffic conditions on public roads. One scanner is
mounted horizontally and is used to determine the approximate
component of relative motion along the movement of the
acquisition vehicle via scan matching; the obtained relative
motion estimates are concatenated to form an initial path.
Assuming that features such as buildings are visible from both
ground-based and airborne view, this initial path is globally
corrected by Monte-Carlo Localization techniques using an aerial
photograph or a Digital Surface Model as a global map. The
second scanner is mounted vertically and is used to capture the
3D shape of the building facades. Applying a series of automated
processing steps, a texture-mapped 3D facade model is
reconstructed from the vertical laser scans and the camera
images. In order to obtain an airborne model containing the roof
and terrain shape complementary to the facade model, a Digital
Surface Model is created from airborne laser scans, then
triangulated, and finally texturemapped with aerial imagery.
Finally, the facade model and the airborne model are fused
to one single model usable for both walk- and fly-thrus. The
developed algorithms are evaluated on a large data set acquired
in downtown Berkeley, and the results are shown and discussed
Efficient and Robust Neuromorphic Computing Design
In recent years, brain inspired neuromorphic computing system (NCS) has been intensively studied in both circuit level and architecture level. NCS has demonstrated remarkable advantages for its high-energy efficiency, extremely compact space occupation and parallel data processing. However, due to the limited hardware resources, severe IR-Drop and process variation problems for synapse crossbar, and limited synapse device resolution, it’s still a great challenge for hardware
NCS design to catch up with the fast development of software deep neural networks (DNNs). This dissertation explores model compression and acceleration methods for deep neural networks to save both memory and computation resources for the hardware implementation of DNNs. Firstly, DNNs’ weights quantization work is presented to use three orthogonal methods to learn synapses with one-level precision, namely, distribution-aware quantization, quantization regularization and bias tuning, to make image classification accuracy comparable to the state-ofthe-art. And then a two-step framework named group scissor, including rank clipping and group connection deletion methods, is presented to address the problems on large synapse crossbar
consuming and high routing congestion between crossbars.
Results show that after applying weights quantization methods, accuracy drop can be well controlled within negligible level for MNIST and CIFAR-10 dataset, compared to an ideal system without quantization. And for the group scissor framework method, crossbar area and routing area could be reduced to 8% (at most) of original size, indicating that the hardware implementation area has been saved a lot. Furthermore, the system scalability has been improved significantly
Flash Memory Devices
Flash memory devices have represented a breakthrough in storage since their inception in the mid-1980s, and innovation is still ongoing. The peculiarity of such technology is an inherent flexibility in terms of performance and integration density according to the architecture devised for integration. The NOR Flash technology is still the workhorse of many code storage applications in the embedded world, ranging from microcontrollers for automotive environment to IoT smart devices. Their usage is also forecasted to be fundamental in emerging AI edge scenario. On the contrary, when massive data storage is required, NAND Flash memories are necessary to have in a system. You can find NAND Flash in USB sticks, cards, but most of all in Solid-State Drives (SSDs). Since SSDs are extremely demanding in terms of storage capacity, they fueled a new wave of innovation, namely the 3D architecture. Today “3D” means that multiple layers of memory cells are manufactured within the same piece of silicon, easily reaching a terabit capacity. So far, Flash architectures have always been based on "floating gate," where the information is stored by injecting electrons in a piece of polysilicon surrounded by oxide. On the contrary, emerging concepts are based on "charge trap" cells. In summary, flash memory devices represent the largest landscape of storage devices, and we expect more advancements in the coming years. This will require a lot of innovation in process technology, materials, circuit design, flash management algorithms, Error Correction Code and, finally, system co-design for new applications such as AI and security enforcement
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching
We propose a non-learning depth completion method for a sparse depth map
captured using a light detection and ranging (LiDAR) sensor guided by a pair of
stereo images. Generally, conventional stereo-aided depth completion methods
have two limiations. (i) They assume the given sparse depth map is accurately
aligned to the input image, whereas the alignment is difficult to achieve in
practice. (ii) They have limited accuracy in the long range because the depth
is estimated by pixel disparity. To solve the abovementioned limitations, we
propose selective stereo matching (SSM) that searches the most appropriate
depth value for each image pixel from its neighborly projected LiDAR points
based on an energy minimization framework. This depth selection approach can
handle any type of mis-projection. Moreover, SSM has an advantage in terms of
long-range depth accuracy because it directly uses the LiDAR measurement rather
than the depth acquired from the stereo. SSM is a discrete process; thus, we
apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to
generate a continuous depth map while preserving depth discontinuity across
object boundaries. Experimentally, compared with the previous state-of-the-art
stereo-aided depth completion, the proposed method reduced the mean absolute
error (MAE) of the depth estimation to 0.65 times and demonstrated
approximately twice more accurate estimation in the long range. Moreover, under
various LiDAR-camera calibration errors, the proposed method reduced the depth
estimation MAE to 0.34-0.93 times from previous depth completion methods.Comment: 15 pages, 13 figure
Spatially Controlled Generation and Probing of Random Telegraph Noise in Metal Nanocrystal Embedded HfO2Using Defect Nanospectroscopy
Random telegraph noise (RTN) is often considered a nuisance or, more critically, a key reliability challenge for miniaturized semiconductor devices. However, this picture is gradually changing as recent works have shown emerging applications based on the inherent randomness of the RTN signals in state-of-The-Art technologies, including true random number generator and IoT hardware security. Suitable material platforms and device architectures are now actively explored to bring these technologies from an embryonic stage to practical application. A key challenge is to devise material systems, which can be reliably used for the deterministic creation of localized defects to be used for RTN generation. Toward this goal, we have investigated RTN in Au nanocrystal (Au-NC) embedded HfO2stacks at the nanoscale by combining conduction atomic force microscopy defect spectroscopy and a statistical factorial hidden Markov model analysis. With a voltage applied across the stack, there is an enhanced asymmetric electric field surrounding the Au-NC. This in turn leads to the preferential generation of atomic defects in the HfO2near the Au-NC when voltage is applied to the stack to induce dielectric breakdown. Since RTN arises from various electrostatic interactions between closely spaced atomic defects, the Au-NC HfO2material system exhibits an intrinsic ability to generate RTN signals. Our results also highlight that the spatial confinement of multiple defects and the resulting electrostatic interactions between the defects provides a dynamic environment leading to many complex RTN patterns in addition to the presence of the standard two-level RTN signals. The insights obtained at the nanoscale are useful to optimize metal nanocrystal embedded high-Îş stacks and circuits for on-demand generation of RTN for emerging random number applications
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