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A Secure Face Recognition System for Mobile-devices without The Need of Decryption
No full textFace recognition technology has received much attention due to its application in defense and crime prevention. In such applications, there is great need to incorporate face recognition technologies onto mobile devices to allow onthe-spot field usage. However there are four major problems that need to be solved, namely the limited storage and processing power of the mobile device, connection instability, security and privacy concerns, and limited network bandwidth. Existing methods do not solve all the problems. This paper addresses all of the above problems holistically by proposing a novel approach. The core of the approach is a DCT-based compression method. This method has high compression ratio such that the compressed image database can be easily stored at a mobile device. Further, face recognition algorithms can be run directly on the compressed database without decompression, which enables on-the-spot field usage. The overhead of network transfer is also greatly reduced due to compression. The security and privacy issue is addressed by pruning most DCT coefficients of images and by a random permutation protocol. As a result, the reconstructed images are not visually recognizable even if the permutation is known. Additional security can also be provided by encrypting the coefficients for network transfer. The system has been implemented on a commercially available general purpose PDA-phone and experimental results demonstrate the potential of the proposed solution.https://www.academia.edu/download/96254814/skm08-secure-face-recog.pd
Administration, Delivery, and Creation of Public Value: Zambia’s Public Pension Fund
No full textD.P.A. -- The University of Baltimore, 2024This study pioneers the application of the Public Value (PV) framework to Zambia’s
Public Pension Funds (PPFs); it explores stakeholder’s conceptions of public value and
its alignment with Ubuntu principles. Through 47 in-depth interviews over 7 months, the
research identifies challenges in addressing the growing elderly population’s needs and
proposes reforms to improve the pension system. The study’s innovative alignment of PV
with Ubuntu principles offers social solidarity and collective well-being. The research
investigates how stakeholders’ conceptions of public value align with their goals, the
existing administrative frameworks, and the needs of the PPFs, and implications for
reforming PPF to better serve retired public servants and the growing elderly population.
This qualitative study contributes to the understanding of public value in pension system
reform and informs policy and administrative improvements for Zambia’s PPFs. Key
findings highlight the need for aligning vision and mission statements with social
protection goals and Ubuntu principles, addressing funding and technological constraints,
enhancing transparency and stakeholder engagement, and digitizing and decentralizing
services. This research seeking to improve public pension systems in Africa and beyond
Thiophene Hydrodesulfurization on High-Capacity Mesoporous Perovskite Oxide Catalysts
No full textHydrodesulfurization (HDS) is an essential industrial process used to remove sulfur from hydrocarbons, such as crude oil, to mitigate the harmful environmental and health effects associated with sulfur emissions. The presence of sulfur compounds, particularly in transportation fuels, can poison catalysts used in refining processes and contribute to the formation of sulfur dioxide, a greenhouse gas. As stricter environmental regulations on sulfur content in fuels emerge, the need for efficient, cost-effective, and sustainable catalysts for HDS has become increasingly critical. Thiophene, a sulfur-containing compound found in petroleum, is often used as a model molecule to study HDS reactions. This study focuses on comparing the performance of perovskite oxide catalysts like LaCoO3, LaNiO3, and LaFeO3 with the widely used CoMo catalyst. The research explores the role of metal composition, catalyst structure, and reaction mechanisms in influencing the efficiency of sulfur removal. By evaluating these materials, this study aims to identify potential alternative or supplementary catalysts for HDS that can improve sulfur conversion rates, enhance catalyst stability, and meet environmental goals. The findings contribute to advancing HDS technology, with a focus on optimizing catalyst design for future applications in cleaner fuel production
Design and Verification of a Synchronus First In First Out (FIFO)
No full textEEE TRANSACTIONS ON COMPUTER-AIDEDThis project focuses on designing and verifying a synchronous FIFO First In First Out (FIFO) memory, a critical component in digital systems for temporary data storage and seamless data transfer. The FIFO operates under a single clock domain, ensuring synchronized read and write operations, making it suitable for systems requiring high-speed, reliable data buffering. This design includes FIFO's key features, such as read and write operations, full and empty flag generation, and pointer management for memory control. The FIFO was implemented using Verilog to define the Register Transfer Level (RTL) design, ensuring functionality and timing requirements were met. For verification, three approaches were employed: (1) UVM-based Verification: A Universal Verification Methodology (UVM) testbench was developed to test the FIFO design rigorously. The testbench includes components like interface, sequence item, driver, monitor, scoreboard, agent, and environment. Directed and random tests were performed to verify corner cases, such as simultaneous reads and writes, full and empty conditions, and overflow and underflow scenarios; (2) Traditional Verilog Testbench: A standalone Verilog testbench was also used to validate the functionality of the FIFO through directed test scenarios and waveform analysis; (3) FPGA implementation: Additionally, the design was implemented on an FPGA for real-time testing to verify its functionality and timing behavior in hardware. FPGA-based verification ensured the design performed as expected under practical conditions. The results confirmed the correct operation of the FIFO, including accurate data transfer, flag behavior, and timing synchronization. The project successfully demonstrated the robustness and reliability of the synchronous FIFO design, highlighting its importance in modern digital systems for efficient data handling and buffering.http://arxiv.org/abs/2504.1090
Towards enhanced precision in thermometry with nonlinear qubits
No full textQuantum thermometry refers to the study of measuring ultra-low temperatures in quantum systems. The precision of such a quantum thermometer is limited by the degree to which temperature can be estimated by quantum measurements. More precisely, the maximal precision is given by the inverse of the quantum Fisher information. In the present analysis, we show that quantum thermometers that are described by nonlinear Schrödinger equations allow for a significantly enhanced precision, that means larger quantum Fisher information. This is demonstrated for a variety of pedagogical scenarios consisting of single and two-qubits systems. The enhancement in precision is indicated by non-vanishing quantum speed limits, which originate in the fact that the thermal, Gibbs state is typically not invariant under the nonlinear equations of motion.It is a pleasure to thank the original quantum wizard, Steve Campbell, for many insightful discussions. S D acknowledges support from the U.S. National Science Foundation under Grant No. DMR-2010127 and the John Templeton Foundation under Grant No. 62422.https://iopscience.iop.org/article/10.1088/2058-9565/adac0
A convergence research approach to resolving ‘wicked problems’: Lessons from an interdisciplinary research team in land use science
No full textMany contemporary social and environmental problems are increasingly ‘wicked.’ Convergence research offers an effective approach to tackle wicked problems by integrating diverse epistemologies, methodologies, and expertise. Yet, there exists little discussion of how to develop and employ a convergence research approach. This article describes our collaborative research efforts to achieve convergence research and team science. For over a decade, we have sought to understand how drug trafficking activities, and the counternarcotics efforts designed to thwart them, catalyze catastrophic changes in landscapes and communities. We first discuss how understanding our wicked problem called for epistemological convergence of diverse data through a team science approach. We then unpack the potential insights and challenges of methodological convergence by drawing upon examples from our land cover and land use change analysis. Third, we argue that the nature of complex, pressing problems requires convergence research to be politically engaged and accountable to the multiple communities affected. This article aims to provide research teams insight into how to pursue epistemological and methodological convergence while attending to the inherent politics of producing knowledge about wicked problems.This work was supported by the National Socio Environmental Synthesis Center SESYNC under funding received from the National Science Foundation NSF DBI 1052875 and NASA Land Cover and Land Use Change Program 80NSSC21K0297 Subsequent support for select authors was provided by NSF EAGER ISN 1837698 and NSF DISN2039975 NRM was additionally supported by NSF GCR 2317819 The findings and conclusions in this article are those of the author s and do not necessarily represent the views of the U S Fish and Wildlife Service Any use of trade firm or product names is for descriptive purposes only and does not imply endorsement by the U S Governmenthttps://www.sciencedirect.com/science/article/pii/S014362282500033
A Diagnosis of Oceanic Precipitation in IMERG-GMI
No full textDiagnosing errors in spaceborne oceanic precipitation estimates is difficult due tocomplicated multi-satellite algorithms and limited surface-based measurements. The Global Precipitation Measurement (GPM) mission helps to alleviate these challenges with NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) product, which is transparently designed to encourage community validation activities, and the GPM Validation Network, which collects observations across global precipitation regimes from over 100 ground-based weather radars to serve as reference datasets for the GPM precipitation products. This study uses the GPM Validation Network’s oceanic precipitation observations from 32 island and coastal radars to diagnose the performance of IMERG V06B & V07B Final Run products during GPM Microwave Imager (GMI) overpasses (i.e., IMERG-GMI) in the period June 2014 – September 2021. Errors are traced from the input Level-2 (satellite footprint) Goddard Profiling Algorithm climate (GPROF-CLIM) GMI product through the successive gridding, calibration and precipitation distribution restoration steps of IMERG’s Level-3 (gridded) algorithm. Results highlight that IMERG-GMI V07B outperforms V06B in detecting and quantifying oceanic precipitation, with a significant improvement over high-latitude ocean (V06B: +143%; V07B: +50%). Furthermore, there is a clear oceanic latitudinal trend in the mean relative bias of IMERG-GMI V07B (high-latitude: +50%; mid-latitude: +10%; tropical: -41%), which largely traces back to GPROF-CLIM V07 (high-latitude: +22%; mid-latitude: -8%; tropical: -44%), with bias differences driven by IMERG’s passive microwave calibration scheme. This error tracing approach supports future IMERG algorithm developments by disentangling how algorithm steps enhance or mitigate errors.Daniel Watters was supported by an appointment to the NASA Postdoctoral Program at NASA Marshall Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA. Patrick Gatlin, David Bolvin, George Huffman, Robert Joyce, Eric Nelkin, Jackson Tan, and David Wolff were supported by NASA Precipitation Measurement Missions funding (program manager Will McCarty). Pierre Kirstetter was funded by the NASA Global Precipitation Measurement Ground Validation Program under Grant 80NSSC21K2045 and the Precipitation Measurement Missions Program under Grant 80NSSC19K0681. We thank Christian Kummerow (Colorado State University) for discussions on quality control criteria and GPROF performance. We thank two anonymous reviewers for their helpful comments and recommendations which improved the paper.https://journals.ametsoc.org/view/journals/hydr/aop/JHM-D-24-0137.1/JHM-D-24-0137.1.xm
Volume-Wise Task fMRI Decoding with Deep Learning:Enhancing Temporal Resolution and Cognitive Function Analysis
No full textIn recent years,the application of deep learning in task functional Magnetic Resonance Imaging (tfMRI) decoding has led to significant advancements. However,most studies remain constrained by assumption of temporal stationarity in neural activity,resulting in predominantly block-wise analysis with limited temporal resolution on the order of tens of seconds. This limitation restricts the ability to decode cognitive functions in detail. To address these limitations, this study proposes a deep neural network designed for volume-wise identification of task states within tfMRI data,thereby overcoming the constraints of conventional methods. Evaluated on Human Connectome Project (HCP) motor and gambling tfMRI datasets,the model achieved impressive mean accuracy rates of 94.0% and 79.6%,respectively. These results demonstrate a substantial enhancement in temporal resolution,enabling more detailed exploration of cognitive processes. The study further employs visualization algorithms to investigate dynamic brain mappings during different tasks,marking a significant step forward in deep learning-based frame-level tfMRI decoding. This approach offers new methodologies and tools for examining dynamic changes in brain activities and understanding the underlying cognitive mechanisms.This study was supported by National Science and Technology Innovation 2030 Major Program 2022ZD0204801. Funding supports from the National Key R&D Program of China (grant 2022YFB4702700, G.-Z.Y.)http://arxiv.org/abs/2503.0192
Fast synergetic simulation of soliton molecules in microresonators in the presence of noise
No full text LASE, 2025, San Francisco, CaliforniaWe introduce a synergetic simulation method that makes it possible to take time steps that are many orders of magnitude larger than is possible with conventional methods. We apply this method to a two-soliton molecule in a microresonator to carry out Monte Carlo simulations and determine when the molecule becomes unstable due to white noise. We validate the model by solving for the steady-state probability density using the Fokker-Planck equation.https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13349/1334904/Fast-synergetic-simulation-of-soliton-molecules-in-microresonators-in-the/10.1117/12.3043349.ful
