LUT Method Research Articles

The Resistance Analysis Attack and Security Enhancement of the IMC LUT Based on the Complementary Resistive Switch Cells

The resistive random access memory (RRAM) based in-memory computing (IMC) is an emerging architecture to address the challenge of the “memory wall” problem. The complementary resistive switch (CRS) cell connects two bipolar RRAM elements anti-serially to reduce the sneak current in the crossbar array. The CRS array is a generic computing platform, for the arbitrary logic functions can be implemented in it. The IMC CRS LUT consumes fewer CRS cells than the static CRS LUT. The CRS array has built-in polymorphic characteristics because the correct logic function cannot be distinguished based on the circuit layout. However, the logic state of every CRS cell can be readout after each operation. It helps the attacker to recover the correct function of the IMC CRS LUT. This work discusses the resistance analysis attack of the IMC LUT based on the CRS array. The proposed resistance analysis attack method is able to be applied to different computation styles based on the CRS array, such as the CRS IMPLY, CRS NOR-OR/NAND-AND, and so on. The attacker can recover the logic function of the LUT by tracing the states of CRS cells. Furthermore, an improved IMC CRS LUT method is proposed and discussed to enhance security. The simulation and analysis results show that the improved IMC CRS LUT can resist various attacks, and it maintains the polymorphic characteristics of the IMC CRS LUT. And the N-bit full adder circuit based on the improved IMC CRS NOR-OR LUTs achieves the best performance compared with the previous counterparts.

Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations

Leaf carotenoids (Cxc) play a crucial role in vegetation as essential pigments responsible for capturing sunlight and protecting leaf tissues. They provide vital insights into a plant physiological status and serve as sensitive indicators of plant stress. However, remote sensing of Cxc at the leaf level has been challenging due to the low Cxc content and weaker absorption features compared to those of chlorophylls in the visible domain. Existing vegetation indices have been widely applied but often lack a solid physical foundation, which limits their applicability and robustness in characterizing Cxc. Yet, physical models can confront this ill-posed problem, though with high operational costs. To address this issue, this study presents a novel hybrid inversion method that combines the multilayer perceptron (MLP) algorithm with PROSPECT model simulations to accurately retrieve Cxc. The effectiveness of the MLP method was investigated through comparisons with the classical PROSPECT model inversion (look-up table [LUT] method), the convolutional neural network (CNN) hybrid model, and the Transformer hybrid model. In the pooled results of six experimental datasets, the MLP method exhibited its robustness and generalization capabilities for leaf Cxc content estimation, with RMSE of 3.12 μg/cm2 and R2 of 0.52. The Transformer (RMSE = 3.14 μg/cm2, R2 = 0.46), CNN (RMSE = 3.42 μg/cm2, R2 = 0.28), and LUT (RMSE = 3.82 μg/cm2, R2 = 0.24) methods followed in descending order of accuracy. A comparison with previous studies using the same public datasets (ANGERS and LOPEX) also demonstrated the performance of the MLP method from another perspective. These findings underscore the potential of the proposed MLP hybrid method as a powerful tool for accurate Cxc retrieval applications, providing valuable insights into vegetation health and stress response.

Maintaining Secure Level on Symmetric Encryption under Quantum Attack

Quantum computing is currently being researched in many countries, and if implemented in the near future, it may pose a threat to existing encryption standards. In the quantum computer environment, asymmetric encryption can be solved by Shor’s Algorithm in polynomial time, and the difficulty of breaking symmetric encryption using brute force is reduced from N times to square root N times by Grover’s Algorithm. We take the Advanced Encryption Standard as the theme and increase the key length from the original standard 192 bits and 256 bits to 384 bits and 512 bits, respectively, in order to maintain the security level of AES 192/256 under the environment of quantum computing, so we propose the key schedule of AES 384/512, and write the software in C++ on FPGA. The experimental results show that our scheme can achieve Level III and Level V security levels in a quantum computer attack environment. In addition to increasing the length of the key, we use the LUT method in the process of writing SubBytes to replace the array and speed up the computation to optimize the execution speed. In addition, the proposed scheme is still based on 128-bit computing blocks, rather than computing blocks in larger blocks.

CORDIC-Based General Multiple Fading Generator for Wireless Channel Digital Twin

A wireless channel digital twin is a useful tool to evaluate the performance of a communication system at the physical or link level by generating the physical channel controllably. In this paper, a stochastic general fading channel model is proposed, which considered most of the channel fading types for various communication scenarios. By using the sum-of-frequency-modulation (SoFM) method, the phase discontinuity of the generated channel fading was well addressed. On this basis, a general and flexible generation architecture for channel fading was developed on a field programmable gate array (FPGA) platform. In this architecture, improved CORDIC-based hardware circuits for the trigonometric function, exponential function, and natural logarithm were designed and implemented, which improved the real-time performance of the system and the utilization rate of the hardware resources compared with the traditional LUT and CORDIC method. For a 16-bit fixed-point data bit width single-channel emulation, the hardware resource consumption was significantly reduced from 36.56% to 15.62% for the overall system by utilizing the compact time-division (TD) structure. Moreover, the classical CORDIC method brought an extra latency of 16 system clock cycles, while the latency caused by the improved CORDIC method was decreased by 62.5%. Finally, a generation scheme of a correlated Gaussian sequence was developed to introduce a controllable arbitrary space-time correlation for the channel generator with multiple channels. The output results of the developed generator were consistent with the theoretical results, which verified the correctness of both the generation method and hardware implementation. The proposed channel fading generator can be applied for the emulation of large-scale multiple-input, multiple-output (MIMO) channels under various dynamic communication scenarios.

Low-Latency Hardware Implementation of High-Precision Hyperbolic Functions Sinhx and Coshx Based on Improved CORDIC Algorithm

CORDIC algorithm is used for low-cost hardware implementation to calculate transcendental functions. This paper proposes a low-latency high-precision architecture for the computation of hyperbolic functions sinhx and coshx based on an improved CORDIC algorithm, that is, the QH-CORDIC. The principle, structure, and range of convergence of the QH-CORDIC are discussed, and the hardware circuit architecture of functions sinhx and coshx using the QH-CORDIC is plotted in this paper. The proposed architecture is implemented using an FPGA device, showing that it has 75% and 50% latency overhead over the two latest prior works. In the synthesis using TSMC 65 nm standard cell library, ASIC implementation results show that the proposed architecture is also superior to the two latest prior works in terms of total time (latency × period), ATP (area × total time), total energy (power × total time), energy efficiency (total energy/efficient bits), and area efficiency (efficient bits/area/total time). Comparison of related works indicates that it is much more favorable for the proposed architecture to perform high-precision floating-point computations on functions sinhx and coshx than the LUT method, stochastic computing, and other CORDIC algorithms.

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging.

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

Least Square Based Iteration Approach for Agricultural Soil Moisture Retrieval Using Multi-Sensor Data

The main objective of this study is to develop a robust soil moisture retrieval approach using multi-sensor remote sensing data. Firstly, the water cloud model was employed to eliminate the vegetation effects on SAR observations over vegetated areas, thus to obtain the bare soil backscatter associated with soil moisture. Then, against the underdetermined system for soil moisture retrieval, the advanced integral equation model and calibrated integral equation model were integrated to construct soil moisture retrieval scheme in combination with multi-sensor SAR observations. Through the above processing the influences of vegetation and surface roughness can be minimized in the developed soil moisture retrieval scheme. Finally, the least square based iteration approach was applied to derive soil moisture with multi-sensor SAR observations as inputs. Based on the field measurements and multi-sensor SAR data (C-band and X-band). acquired on June 2015 and October 2015 over Hebei agricultural areas, quantitative evaluation of the developed approach was implemented. The results indicate that accurate soil moisture was obtained by the developed approach over corn covered area and bare agricultural area. In comparison with the results obtained by LUT method, the least square based iteration approach achieved better estimations for soil moisture.

Soil Moisture Inversion with Sparse Vegetation Coverage Areas and Analysis of Spatial Characteristics Based on RADARSAT-2

Backscatter coefficient of radar imagery is mainly affected by the surface soil moisture and roughness. This paper was based on the RADARSAT-2 polarimetric SAR image data, field measured soil moisture data, surface roughness, soil particle composition and soil bulk density data. The soil moisture from SAR was retrieved by using Oh2004 semi empirical model inversion method, Oh2004 semi empirical model LUT method, AIEM theory model which is based on the effective correlation length and AIEM theory model on account of the effective RMS height. After soil moisture retrieval of the study area sampling points at 0-6cm depth, this study analyzed the corresponding to the four kinds of methods of the optimal filtering window size, made a comparative analysis of the four methods and determined the optimum inversion model of the study area soil moisture microwave remote sensing at 0-6cm depth. This article used the best model for retrieving soil moisture in depth of 0-6cm, made the distribution map of soil moisture at 0-6cm depth, and analyzed spatial distribution characteristics.

Full-scale one-dimensional NLUT method for accelerated generation of holographic videos with the least memory capacity.

A full-scale one-dimensional novel-look-up-table (1-D NLUT) method enabling faster generation of holographic videos with the minimum memory capacity is proposed. Only a pair of half-sized 1-D baseline and depth-compensating principal-fringe-patterns (PFPs) is pre-calculated and stored based on the concentric-symmetry property of the PFP, and from which a set of half-sized 1-D PFPs for all depth planes are generated based on its thin-lens property, which enables minimization of the required memory size down to a few KB regardless of the number of depth planes. Moreover, all those hologram calculations are fully one-dimensionally performed with a set of half-sized 1-D PFPs based on its shift invariance property, which also allows minimization of its overall hologram calculation time. From experiments with test videos, the proposed method has been found to have the shortest hologram calculation time even with the least memory in comparison with several modified versions of the conventional NLUT and LUT methods, which confirms its feasibility.

Depth compensating calculation method of computer-generated holograms using symmetry and similarity of zone plates

Computer-generated hologram (CGH) is a promising 3D display technology while it is challenged by heavy computation load and vast memory requirement. To solve these problems, a depth compensating CGH calculation method based on symmetry and similarity of zone plates is proposed and implemented on graphics processing unit (GPU). An improved LUT method is put forward to compute the distances between object points and hologram pixels in the XY direction. The concept of depth compensating factor is defined and used for calculating the holograms of points with different depth positions instead of layer-based methods. The proposed method is suitable for arbitrary sampling objects with lower memory usage and higher computational efficiency compared to other CGH methods. The effectiveness of the proposed method is validated by numerical and optical experiments.

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

Process time and MPPT performance analysis of CF, LUT, and ANN control methods for a PMSG-based wind energy generation system

Due to environmental issues such as global warming and the greenhouse effect, there is a growing interest in renewable sources of energy. Wind energy, which is the most important of these energy sources, can potentially meet a portion of the global energy demand. Numerous studies are being conducted worldwide to determine how the maximum level of power can be obtained from wind energy. In these studies, there is a particular interest in permanent magnet synchronous generators (PMSGs). This is because PMSGs exhibit a good performance within a wide range wind speeds and can be driven directly. In this study, the maximum power point tracking (MPPT) of a PMSG has been carried out by using a prototype built in a laboratory environment. The simulation model has been realized by utilizing MATLAB/Simulink and implemented by using dSPACE. MPPT has been performed by employing such control algorithms as an artificial neural network, a look-up table, and curve fitting in order to carry out comparative performance analyses of these control algorithms. Controllers were analyzed through comparisons between their MPPT and process performance. Based on our analysis results, we were able to identify controllers that were better in terms of power tracking and process performance.

Fast one-step calculation of holographic videos of three-dimensional scenes by combined use of baseline and depth-compensating principal fringe patterns.

As a new approach for rapid generation of holographic videos, a so-called compressed novel-look-up-table(C-NLUT), which is composed of only two principal fringe patterns (PFPs) of baseline and depth-compensating PFPs (B-PFP, DC-PFP), is proposed. Here, the hologram pattern for a 3-D video frame are generated by calculating the fringe patterns for all depth layers only by using the B-PFP, and then transforming them into those for their depth layers by being multiplied with corresponding DC-PFPs. With this one-step calculation process, the computational speed (CS) of the proposed method can be greatly enhanced. Experimental results show that the CS of the proposed method has been improved by 30.2% on the average compared to that of the conventional method. Furthermore, the average calculation time of a new hybrid MC/C-NLUT method, in which both of motion-compensation (MC) and one-step calculation schemes are employed, has been reduced by 99.7%, 65.4%, 60.2% and 30.2%, respectively compared to each of the conventional ray-tracing, LUT, NLUT, and MC-NLUT methods. In addition, the memory size of the proposed method has been also reduced by 82 × 10(6)-fold and 128-fold compared to those of the conventional LUT and NLUT methods, respectively.

Efficient Baseband Digital Predistorter Using Lut for Power Amplifier (PA) with Memory Effect

Modern communications systems will have to integrate multiple radio technologies to support a large number of wireless standards (WLAN, WPAN…). This system should operate with third generation (3G) air interface standards, where non constant envelope modulation formats (MQAM, OFDM…) are required. The amplification of a modulated signal with large variation of envelope introduces distortions when the input-output characteristic is nonlinear. These distortions are particularly important when the power amplifier (PA) has non-linear memory effects with a wideband signal. The technique proposed in this paper aims to fight against this phenomenon by optimizing the method developed in the laboratory for a real-time implementation. This technique is able to provide exactly the reverse characteristics of a PA modeled by a Hammerstein model in base band. Its principle is to find the largest real root of a polynomial related to the analytical model of the PA and to the input signal to be amplified to calculate a pre-distorted signal. To reduce significantly the computation time, an implementation by the LUT method is proposed. This method uses two pre-distortion principles and can be implemented on a FPGA board with table bases method known for simplicity including in the same way the correction of the memory effect.

Design and Realization of a Full Digitalization GMSK Modem on FPGA

To implement GMSK modem with the characteristics of good spectrum, low complexity and fast acquisition, a full digitizationGMSKmodem scheme was proposed. Based on LUT method, the generation of GMSK signals has better spectrum characteristicand easy to realize. In receive terminal, a non-coherent digitized GMSKreceiver with fast signal detection and symbol timing acquisitionalgorithm was presented. Double sliding windows was used to conquer the influence of communication channel gain on the detection algorithm and realized within 6 symbols with accuracy. Additionally, in order to reduce the complexity of the receiver, the received signals are demodulated sample by sample in advance and then decided with the estimated symbol timing. The symbol timing acquisition algorithm proposed is realized based on a preamble sequence rapidly within only 8 symbols. Simulation results and integrated FPGA realizationprove its feasibility and superiority.

Efficient Method for Look-Up-Table Design in Memory based FIR Filters

Distributed arithmetic (DA)-based computation is well known for efficient memory-based implementation of Finite impulse response (FIR) filter where the filter outputs are computed as inner-product of input-sample vectors and filter-coefficient vector. In this paper, we show that the LUT multiplier based approach in which the memory elements store all the possible values of product of filter co-efficient will be the efficient in terms of area with the same throughput in comparison of DA. We present two new approaches to LUT-based multiplication, which could be used to reduce the memory size to half of the conventional LUT-based multiplication. The proposed method in this paper have half memory required than the existing DA method .The DA and the proposed LUT method are simulated and synthesized using the Xilinx tool and the memory required by the proposed LUT is nearly 50% lesser than the DA.

Effective memory reduction of the novel look-up table with one-dimensional sub-principle fringe patterns in computer-generated holograms

We propose a novel approach to massively reduce the memory of the novel look-up table (N-LUT) for computer-generated holograms by employing one-dimensional (1-D) sub-principle fringe patterns (sub-PFPs). Two-dimensional (2-D) PFPs used in the conventional N-LUT method are decomposed into a pair of 1-D sub-PFPs through a trigonometric relation. Then, these 1-D sub-PFPs are pre-calculated and stored in the proposed method, which results in a remarkable reduction of the memory of the N-LUT. Experimental results reveal that the memory capacity of the LUT, N-LUT and proposed methods have been calculated to be 149.01 TB, 2.29 GB and 1.51 MB, respectively for the 3-D object having image points of 500 × 500 × 256, which means the memory of the proposed method could be reduced by 103 × 10(6) fold and 1.55 × 10(3) fold compared to those of the conventional LUT and N-LUT methods, respectively.