Ultra-low Resolution Research Articles

Refinement of crystal structures at ultralow resolution with assistance from AlphaFold modeling and Rosetta optimization

Refinement of crystal structures at ultralow resolution with assistance from AlphaFold modeling and Rosetta optimization

Refinement of crystal structures at ultralow resolution with assistance from AlphaFold modeling and Rosetta optimization

Refinement of crystal structures at ultralow resolution with assistance from AlphaFold modeling and Rosetta optimization

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

AbstractFor decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

Study on the Arctic Underwater Terrain-Aided Navigation Based on Fuzzy-Particle Filter

The ultra-low resolution underwater terrain maps of the Arctic region reduce the localization and navigation accuracy of the underwater vehicle relying on terrain-aided navigation. In this paper, we study the navigation ability of Autonomous Underwater Vehicles (AUVs) under the ultralow-resolution terrain map. Firstly, the low-resolution map is transformed into a continuous map by bilinear interpolation. Then, a Terrain-Aided Navigation (TAN) system based on the Particle Filter (PF) is constructed to estimate the state of AUV position by particles. Particles of a random distribution of fixed variance can effectively track targets. However, a fixed variance distribution is not well adapted to many different situations. To improve navigation accuracy and robustness, fuzzy logic is used to estimate the distribution variance of particles under the current terrain gradient dynamically. The simulation results show that our proposed Fuzzy-PF TAN system is robust under various current disturbance situations. The position error of our system is within a map resolution unit of 500 m.

Reliable OFDM Receiver With Ultra-Low Resolution ADC

The use of low-resolution analog-to-digital converters (ADCs) can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy

Submegabase-size topologically associating domains (TAD) have been observed in high-throughput chromatin interaction data (Hi-C). However, accurate detection of TADs depends on ultra-deep sequencing and sophisticated normalization procedures. Here we propose a fast and normalization-free method to decode the domains of chromosomes (deDoc) that utilizes structural information theory. By treating Hi-C contact matrix as a representation of a graph, deDoc partitions the graph into segments with minimal structural entropy. We show that structural entropy can also be used to determine the proper bin size of the Hi-C data. By applying deDoc to pooled Hi-C data from 10 single cells, we detect megabase-size TAD-like domains. This result implies that the modular structure of the genome spatial organization may be fundamental to even a small cohort of single cells. Our algorithms may facilitate systematic investigations of chromosomal domains on a larger scale than hitherto have been possible.

Power-Efficient Wideband Spectrum Sensing for Cognitive Radio Systems

This paper proposes a wideband spectrum sensing architecture by utilizing a one-bit quantization at the cognitive radio receiver. A window-based autocorrelation is utilized to provide the power spectral density of the quantized signal. Closed-form expressions are derived for the one-bit quantized correlation, where the critical impact of the ultralow resolution is quantified. It is shown that the introduced method can still provide enough information about the sparse spectrum even at low signal-to-noise ratios (SNR). A suboptimal detection algorithm is presented to sense whether individual subbands are occupied or vacant. To evaluate the detection algorithm, the probability of false alarm and probability of detection are simulated for various system parameters. The sensing performance and the accuracy of the offered expressions are justified through comparisons with respective results from computer simulations. When compared to other methods, in addition to the significant saving in power, results indicate that, with the proper parameter selection and SNR down to $-$ 12 dBs, the proposed method provides low complexity, low sensing period, and better performance for less sparsity levels even though an aggressive quantization has been applied.

Ultra-Low Resolution Character Recognition with Increased Training Data and Image Enhancement

Ultra-Low Resolution Character Recognition with Increased Training Data and Image Enhancement

A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution

In this paper, we propose a new font and size identification method for ultra-low resolution Arabic word images using a stochastic approach. The literature has proved the difficulty for Arabic text recognition systems to treat multi-font and multi-size word images. This is due to the variability induced by some font family, in addition to the inherent difficulties of Arabic writing including cursive representation, overlaps and ligatures. This research work proposes an efficient stochastic approach to tackle the problem of font and size recognition. Our method treats a word image with a fixed-length, overlapping sliding window. Each window is represented with a 102 features whose distribution is captured by Gaussian Mixture Models (GMMs). We present three systems: (1) a font recognition system, (2) a size recognition system and (3) a font and size recognition system. We demonstrate the importance of font identification before recognizing the word images with two multi-font Arabic OCRs (cascading and global). The cascading system is about 23% better than the global multi-font system in terms of word recognition rate on the Arabic Printed Text Image (APTI) database which is freely available to the scientific community.

High-speed reconstruction for ultra-low resolution faces

In this paper, a learning-based high-speed reconstruction system for ultra-low resolution faces is implemented using a software/hardware co-design paradigm. The hardware component working at 60 MHz contains a field programmable gate array, which is reconfigured to contain parallel processing units, and multiple memories to create parallel data. The hardware component effectively handles generating and sorting computationally intensive similarity metrics. This solves the processing speed problem in learning-based super-resolution reconstruction for ultra-low resolution faces. The system can reconstruct faces using 8×6, 16×12, and 32×24 sized images, with 4×4, 8×8, or 16×16 times magnification. The experimental results verify the effectiveness of our system in terms of both visual effect and low root mean square errors. The processing speed can be improved up to a maximum of 7900 times faster than a pure software implementation using C.

Adaptive Interrogation for Fast Optical Sensing Based on Cascaded Micro-Ring Resonators

An adaptive interrogation technique that employs integrated semiconductor micro-ring resonators (MRR) for fast-response sensing applications is proposed and numerically studied. The basic sensing scheme consists of two MRRs in an add/drop signal configuration; the first MRR plays the role of the measuring sensor, while the second one acts as a reference optical filter that converts any frequency shift induced in the spectral characteristics of the measuring sensor into optical power variations. The proposed photonic sensor has the potential to adjust its transfer function dynamically, due to the varying response of the MMRs to different operating conditions, exhibiting either an ultra-low resolution operation capable of detecting extremely small refractive index changes, or an operation with a wide dynamic range. Finally, we propose a more sophisticated approach for simultaneous interrogation that incorporates multiple MRR sensors, based on thermal heaters that modulate the response of each MRR sensor with different modulation frequencies. In contrast to the conventional interrogation techniques, the proposed configurations enable ultra-high sampling rates crucial for studying fast biochemical reactions, such as enzyme kinetics.

Design and Performance Analysis of Control System for a Micro-Displacement Platform

It is difficult to balance long travel and high precision, especially in multi-coordinate machining. A stage with nanometer positioning precision is established and the control technologies are studied. Micro-EDM provides the potential feasibility for the nano-scale precision machining. The micro-EDM transmission modular is applied to to complete the discharge process of the servo in the experiment system ,which improves the processing efficiency and the processing quality. The strategy is one part of micro-EDM machine control system, which greatly enhances the positioning accuracy. Results show that the system works well. The system also has many advantages such as quick response, being smooth, ultra low speed and super resolution etc, and meets the needs of micro-EDM control. The research provides a new solution for the design of precision location stages.

Design and Analysis for a Nano-Positioning Micro-Motion Platform Driven by Piezoelectric Ceramic Linear Motors

Micro-EDM provides the potential feasibility for the nano-scale precision machining, and Micro-EDM is suitable for three-dimensional complicated structure processing. A three-dimensional model is conducted in this paper, and the key factors of a platform are analysed in detail, the positioning accuracy of the platform is tested on this basis of the model. The results of the test show that dynamic behavior and stability of this mechanism has many advantages of quick response, smooth, ultra low speed and super resolution etc. High positioning accuracy can be achieved by this platform, so it complies with the requirement of micro-EDM.

A Language-Independent, Open-Vocabulary System Based on HMMs for Recognition of Ultra Low Resolution Words

A Language-Independent, Open-Vocabulary System Based on HMMs for Recognition of Ultra Low Resolution Words

Similarity-based analysis for large networks of ultra-low resolution sensors

By analyzing the similarities between bit streams coming from a network of motion detectors, we can recover the network geometry and discover structure in the human behavior being observed. This means that a low-cost network of sensors can provide powerful contextual information to building systems: improving the efficiency of elevators, lighting, heating, and cooling; enhancing safety and security; and opening up new opportunities for human-centered information systems. This paper will show how signal similarity can be used to calibrate a sensor network to accuracies below the resolution of the individual sensors. This is done by analyzing the similarity structures in the unconstrained movement of people in the observed space. We will also present our efficient behavior-learning algorithm that yields 90% correct behavior-detection in data from a sensor network comprised of motion detectors by employing similarity-based clustering to automatically decompose complex activities into detectable sub-classes.

Spatially resolved tunneling spectroscopy on TTF-TCNQ

Local tunneling spectroscopy has been measured with low temperature UHV-STM on in-situ cleaved ab surface of organic TTF-TCNQ crystal. Due to ultra low image drift and clear molecular resolution, the spectroscopy is performed at specific molecular site either on TCNQ or TTF chains. In normal state (T= 63 K), a large pseudo-gap exists both in TTF and TCNQ chains. Above pseudo-gap local density of states differs for TTF and TCNQ chains that is in good agreement with double band model. By the signature of an anomalous in local spectroscopy measurement, a single impurity has been detected on a TTF chain. Charge density wave fluctuation is pinned by impurity above critical temperature (T=54K). Results obtained show that, Scanning Tunneling Spectroscopy can provide spatially resolved spectroscopic information at nanometer scale.

Male-female Classification from 8×6 Ultra Low Resolution Face Images by Neural Network

Male-female Classification from 8×6 Ultra Low Resolution Face Images by Neural Network