College of Engineering
Parent: UC Davis
eScholarship stats: History by Item for March through June, 2024
Item | Title | Total requests | 2024-06 | 2024-05 | 2024-04 | 2024-03 |
---|---|---|---|---|---|---|
8r5848vp | RXMesh: A GPU Mesh Data Structure | 284 | 61 | 53 | 80 | 90 |
1sm051d2 | Dynamic Mesh Processing on the GPU | 211 | 46 | 46 | 59 | 60 |
0x86w4w1 | Optimized GPU Implementation of Grid Refinement in Lattice Boltzmann Method | 143 | 15 | 34 | 94 | |
9fz7k633 | Neon: A Multi-GPU Programming Model for Grid-based Computations | 130 | 30 | 17 | 50 | 33 |
9534w5x1 | Multi-Stage Delivery of Malware | 123 | 14 | 35 | 39 | 35 |
48j4k7np | Dynamic Graphs on the GPU | 115 | 16 | 31 | 41 | 27 |
65t741zg | GPU LSM: A Dynamic Dictionary Data Structure for the GPU | 109 | 16 | 20 | 36 | 37 |
7dc8d5vb | Benchmarking Deep Learning Frameworks with FPGA-suitable Models on a Traffic Sign Dataset | 76 | 9 | 11 | 25 | 31 |
830502mm | Use of Photron Cameras and TEMA Software to Measure 3D Displacements in Centrifuge Tests | 66 | 13 | 23 | 13 | 17 |
3v12f7dn | Quotient Filters: Approximate Membership Queries on the GPU | 65 | 10 | 11 | 24 | 20 |
2jf918dh | CSE-92-18 - An Evaluation of Feature Selection Methodsand Their Application to Computer Security | 61 | 12 | 15 | 17 | 17 |
4sk284kw | Benchmarking Deep Learning Frameworks and Investigating FPGA Deployment for Traffic Sign Classification and Detection | 60 | 9 | 11 | 22 | 18 |
0b41q7v8 | Leveraging Security Metrics to Enhance System and Network Resilience | 48 | 15 | 11 | 10 | 12 |
37j8j27d | High-Performance Linear Algebra-based Graph Framework on the GPU | 48 | 16 | 5 | 14 | 13 |
6rt535s6 | Building a Performance Model for Deep Learning Recommendation Model Training on GPUs | 48 | 17 | 10 | 16 | 5 |
1xb249zt | Modeling Systems Using Side Channel Information | 47 | 13 | 13 | 7 | 14 |
74986309 | Numerical modeling of soil liquefaction and lateral spreading using the SANISAND-Sf model in the LEAP experiments | 47 | 21 | 6 | 8 | 12 |
7j96s061 | Maximum Clique Enumeration on the GPU | 47 | 15 | 7 | 13 | 12 |
6ww1c3bw | Parametrization and Effectiveness of Moving Target Defense Security Protections for Industrial Control Systems | 46 | 10 | 14 | 12 | 10 |
0227z2t1 | SANISAND-MSf: a sand plasticity model with memory surface and semifluidised state | 44 | 11 | 11 | 10 | 12 |
0xj8f0s6 | Effect of particle shape on cyclic liquefaction resistance of granular materials | 44 | 8 | 7 | 11 | 18 |
5rv3d8np | Effect of Anisotropic Consolidation on Cyclic Liquefaction Resistance of Granular Materials via 3D-DEM Modeling | 43 | 16 | 5 | 10 | 12 |
5qd0r4ws | Parallel Algorithms and Dynamic Data Structures on the Graphics Processing Unit: a warp-centric approach | 42 | 13 | 11 | 6 | 12 |
042876p3 | Effect of coefficient of uniformity on cyclic liquefaction resistance of granular materials | 40 | 10 | 7 | 9 | 14 |
6kp4p18t | Graph Coloring on the GPU | 40 | 6 | 8 | 14 | 12 |
0bg5p8ch | Generalizing Tanglegrams | 35 | 8 | 4 | 12 | 11 |
0hc042kf | CENTRIFUGE PREDICTION OF EGRESS SYSTEM PERFORMANCE | 35 | 22 | 4 | 6 | 3 |
0p96v327 | DYNAMIC BEHAVIOR OF FOUNDATIONS: AN EXPERIMENTAL STUDY IN A CENTRIFUGE | 34 | 18 | 7 | 5 | 4 |
2p48q0zg | A Dynamic Hash Table for the GPU | 31 | 10 | 5 | 5 | 11 |
3z8926ks | Evolution vs. Intelligent Design in Program Patching | 31 | 13 | 4 | 4 | 10 |
6p22t424 | Parametrization and Effectiveness of Moving Target Defense Security Protections within Industrial Control Systems | 31 | 6 | 12 | 4 | 9 |
9wd8g79f | Effects of pressure and flow rate on the efficiency and performance of autothermal reforming systems for hydrogen production | 31 | 31 | |||
24g1m0w0 | Modeling of Dry and Saturated Soil-Foundation Interfaces | 30 | 3 | 10 | 5 | 12 |
9v75738g | Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs | 29 | 5 | 10 | 10 | 4 |
1vh8z8hp | Characteristic limitations of advanced plasticity and hypoplasticity models for cyclic loading of sands | 28 | 7 | 6 | 6 | 9 |
5rz6t3q4 | NetSage: Open Privacy-Aware Network Measurement, Analysis, And Visualization Service | 28 | 9 | 4 | 11 | 4 |
5rj639x8 | Effects of inlet velocity and steam-to-methanol ratio on the phenomena of process intensification in protruded millisecond microchannel reactors | 27 | 27 | |||
9n3966b9 | Computational fluid dynamics and thermodynamic analysis of transport and reaction phenomena in autothermal reforming reactors for hydrogen production | 27 | 27 | |||
6j27m45d | Extracting and visualizing topological information from large high-dimensional data sets | 26 | 18 | 6 | 1 | 1 |
7w08k57j | A Hybrid Constrained Coral Reefs Optimization Algorithm with Machine Learning for Optimizing Multi-reservoir Systems Operation | 26 | 9 | 5 | 7 | 5 |
9459p69n | Continuous and efficient production of hydrogen from methanol in protruded millisecond microchannel reactors for fuel cell applications | 26 | 26 | |||
9vm907zv | High Efficiency Micromachined Sub-THz Channels for Low Cost Interconnect for Planar Integrated Circuits | 26 | 7 | 6 | 4 | 9 |
2000r1m0 | Computational fluid dynamics studies of catalytically stabilized combustion of propane in flow tube reactors | 24 | 5 | 1 | 6 | 12 |
1r37v116 | Roles of pre- and post-liquefaction stages in dynamic system response of liquefiable sand retained by a sheet-pile wall | 23 | 8 | 5 | 7 | 3 |
2gm296n9 | Transport phenomena in microchannel reactors for proton-exchange membrane fuel cell applications | 23 | 9 | 2 | 5 | 7 |
74f3q3wf | Applying Machine Learning to Identify NUMA End-System Bottlenecks for Network I/O | 23 | 12 | 1 | 4 | 6 |
7fw9b043 | Methods and mechanisms for improving combustion stability by fluid recirculation structures in micro-structured burners | 23 | 4 | 2 | 6 | 11 |
98d576pk | Liquefaction of granular materials in constant-volume cyclic shearing: Transition between solid-like and fluid-like states | 23 | 6 | 4 | 5 | 8 |
98m6d2hd | Heat transfer and thermodynamic analysis of synthesis gas production processes in chemical reactors with integrated heat exchangers by steam reforming | 21 | 3 | 3 | 4 | 11 |
8t42g1c5 | Improvements of efficiency and performance for steam reforming reactors with optimum conditions of wall thermal conductivities and channel dimensions | 20 | 3 | 4 | 3 | 10 |
Disclaimer: due to the evolving nature of the web traffic we receive and the methods we use to collate it, the data presented here should be considered approximate and subject to revision.