Look-up Table Research Articles

Non-interactive Private Multivariate Function Evaluation using Homomorphic Table Lookup

To address security issues in cloud computing, fully homomorphic encryption (FHE) enables a third party to evaluate functions using ciphertexts that do not leak information to the cloud server. The remaining problems of FHE include high computational costs and limited arithmetic operations, only evaluating additions and multiplications. Arbitrary functions can be evaluated using a precomputed lookup table (LUT), which is one of the solutions for those problems. Previous studies proposed LUT-enabled computation methods 1) with bit-wise FHE and 2) with word-wise FHE. The performance of LUT-enabled computation with bit-wise FHE drops quickly when evaluating BigNum functions because of the complexity being O(s·2^d·m), where m represents the number of inputs, d and s represent the bit lengths of the inputs and outputs, respectively. Thus, LUT-enabled computation with word-wise FHE, which handles a set of bits with one operation, has also been proposed; however, previous studies are limited in evaluating multivariate functions within two inputs and cannot speed up the evaluation when the domain size of the integer exceeds 2N, where N is the number of elements packed into a single ciphertext. In this study, we propose a non-interactive model, in which no decryption is required, to evaluate arbitrary multivariate functions using homomorphic table lookup with word-wise FHE. The proposed LUT-enabled computation method 1) decreases the complexity to O(2^d·m/l), where l is the element size of FHE packing; 2) extends the input and output domain sizes to evaluate multivariate functions over two inputs; and 3) adopts a multidimensional table for enabling multithreading to reduce latency. The experimental results demonstrate that evaluating a 10-bit two-input function and a 5-bit three-input function takes approximately 90.5 and 105.5 s with 16-thread, respectively. Our proposed method achieves 3.2x and 23.1x speedup to evaluate two-bit and three-bit 3-input functions compared with naive LUT-enabled computation with bit-wise FHE.

Health utilities of patients with epilepsy in a Canadian population.

Health state utilities are required to obtain quality adjusted life years, a common metric that informs clinical decision-making at individual, group, and health policy levels. Health state utilities are different from health-related quality of life, and their distribution across patients with epilepsy, as well as the factors that impact them, have not been studied in depth. We aimed to describe the distribution of health state utilities in people with epilepsy and the impact of different combinations of clinical and demographic factors on health state evaluation. We performed a retrospective analysis of patients' data prospectively collected in the Calgary Comprehensive Epilepsy Program registry. Patient-reported health state utilities were measured using the 5-level EuroQol 5-Dimension scale (EQ-5D-5L) completed at their initial assessment. EQ-5D-5L index scores were derived via the time trade-off approach based on Canadian norms, and their distribution across different health states and patient characteristics was obtained. The Tobit regression model was used to evaluate the determinants of EQ-5D-5L index scores. Of 1446 patients included in this analysis, 724 (50.5%) were female. The median (interquartile range) Canada-normed EQ-5D-5L index score was .87 (.71-.91). Patients with significantly lower health utilities were more likely to be female (p = .008), to be older (p = .034), to be unmarried (p = .013), to have failed to achieve 1-year seizure freedom (p < .001), to have no postsecondary education (p = .028), to be depressed (p < .001), to have antiseizure medication side effects (p = .001), to be unemployed (p < .001), and to be unable to drive (p < .001). A look-up table of health utilities based on combinations of clinical-demographic characteristics was produced. Health utility estimates for combinations of different health states in people with epilepsy attending specialty clinics are now available. These can help guide clinical decision-making in routine clinical practice, economic evaluations of treatment interventions, and health care policies.

A Scaled Monocular 3D Reconstruction Based on Structure from Motion and Multi-View Stereo

Three-dimensional digital modeling at actual scales is essential for digitally preserving cultural relics. While 3D reconstruction using a monocular camera offers a cost-effective solution, the lack of scale information in the resulting models limits their suitability for geometric measurements. Objects with monotonous textures, such as batteries, pose additional challenges due to insufficient feature points, increasing positional uncertainty. This article proposes a method incorporating point and line features to address the scale ambiguity in multi-view 3D reconstruction using monocular cameras. By pre-measuring the lengths of multiple sets of real line segments, building a lookup table, and associating the line features in different images, the table was input into the improved reconstruction algorithm to further optimize the scale information. Experimental results on real datasets showed that the proposed method outperformed the COLMAP method by 70.82% in reconstruction accuracy, with a scale recovery reaching millimeter-level accuracy. This method is highly generalizable, cost-effective, and supports lightweight computation, making it suitable for real-time operation on a CPU.

Constructing 3D SPAD distribution using hyperspectral LiDAR point cloud by PROSPECT model inversion

ABSTRACT Accurately estimating leaf chlorophyll concentrations in a three-dimensional (3D) manner is crucial for precisely monitoring crop growth and development. To reduce destructive detection and achieve real-time estimation, researchers often utilize crop leaves’ SPAD (Soil and Plant Analyzer Development) to represent relative chlorophyll content. However, SPAD measurement is time-consuming and laborious work. Many inversion methods have been proposed to estimate SPAD based on one-dimensional reflectance or two-dimensional spectral images, which rely on external light sources and also cannot obtain 3D distribution. Hyperspectral LiDAR (HSL), as an active remote sensing technology, can acquire target spatial and spectral data simultaneously, making 3D SPAD inversion and reconstruction possible. This research aims to invert SPAD values with HSL spectral information aided by the PROSPECT model and reconstruct 3D SPAD distribution with HSL spatial information. Firstly, we constructed new ratio chlorophyll indices (RCI) based on the multi-feature analysis method with the PROSPECT model and HSL spectral data. Next, we employed RCIs to modify the Look-Up Table (LUT) cost function (SPAD-LUT) of the PROSPECT model, which was validated with the ANGERS dataset. Then, we conducted SPAD inversion with HSL spectral information from vegetable crop samples and integrated the inverted SPAD values into their corresponding 3D coordinates, achieving 3D SPAD distribution reconstruction. The predicted values of the model inversion with the HSL dataset are consistent with the measured SPAD (R2 = 0.421, RMSE = 10.615). The results indicate that our method can invert sample SPAD values and reconstruct their distribution with HSL point cloud despite their complex spatial structure.

The spectral mismatch correction factor estimation using broadband photometer measurements and catalog parameters for tested white LED sources

Using a modeled look-up chart developed in this work, we show that accurate photometric measurements of characteristics like illuminance or luminous intensity of test LED sources can be measured in one step. This is a simple broadband measurement that may substitute for complicated and costly spectral measurements currently in place. To develop the look-up chart, the typical minimum and maximum of the Spectral Mismatch Correction Factor (SMCF) for a given photometers was estimated in relation to catalog parameters of the LEDs such as correlated color temperature (CCT) and melanopic daylight efficacy ratio (mDER). This research was based on the unique and large dataset of real photometers spectral response srel(λ) collected and measured at accredited laboratories located at America, Asia and Europe and modeled LED's spectral power distribution (SPD) data. Independent look-up tables were developed for color-mixed LEDs (cm-LEDs) and white phosphor-converted LEDs (pc-LEDs), two common LED types.

A wind farm control strategy for frequency regulation reserve: Optimize wake loss and frequency support capability

—With a high proportion of renewable energy, the issue of grid frequency fluctuations is becoming increasingly prominent. To tackle this challenge, wind farms can enhance frequency regulation response by increasing rotational kinetic energy through overspeed control. However, this approach may result in energy losses due to the wake effect. This paper proposes an optimized configuration strategy for wind farm frequency regulation reserve (FRR), utilizing pitch angle cooperative control to reduce wake losses. The objective is to explore the optimal configuration for maximizing wind energy utilization under FRR conditions. A genetic algorithm is used to find the optimal control parameters in the established FLORIS-based wind farm power reserve wake model, which involves rotor speed and pitch angle control, to maximize output power and rotational kinetic energy. Based on this, a lookup table is constructed to map wind conditions to the optimal control parameters for different FRR level. According to the reserve power schedule, optimization control is applied to the wind farm. Simulation results show that, compared to the traditional equal distribution strategy for FRR power, the proposed control strategy can enhance the total kinetic energy reserve of the wind farm by optimizing wake loss, thereby improving frequency support capability.

Design and characterization of a hybrid PET detector with DOI capability.

Monolithic or semi-monolithic detectors are attractive for positron emission tomography (PET) scanners with depth-of-interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. We introduce a novel hybrid detector design that combines pixelated and semi-monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. A prototype detector with eight hybrid lutetium-yttrium oxyorthosilicate (LYSO)layers having dimensions of 25.8×12.9×15mm3 was constructed. The energy-weighted and energy-squared weighted averages were used for estimating the x- (pixelated direction) and y-positions (non-pixelated direction). Pseudo-pixels were defined as discrete areas on the flood image based on the crystal look-up table (LUT). The intrinsic spatial resolutions in the pixelated and non-pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy-weighted average of the earliest n time stamps. Two energy windows of 250-700 and 400-600keV were applied for the measurements. The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x- and y-positions of events could be achieved. Under 400-600keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34mm for the pixelated and non-pixelated directions; the average DOI resolution of the second row of pseudo-pixels was 5.1mm in full width at half maximum (FWHM); when using the energy-weighted average of the earliest four-time stamps, the best CTR of 350ps was achieved. Applying a broader energy window of 250-700keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410ps. The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability.

Peat profile database from peatlands in Canada.

Peatlands cover approximately 12% of the Canadian landscape and play an important role in the carbon cycle through their centennial- to millennial-scale storage of carbon under waterlogged and anoxic conditions. In recognizing the potential of these ecosystems as natural climate solutions and therefore the need to include them in national greenhouse gas inventories, the Canadian Model for Peatlands module (CaMP v. 2.0) was developed by the Canadian Forest Service. Model parameterization included compiling peat profiles across Canada to calibrate peat decomposition rates from different peatland types, to define typical bulk density profiles, and to describe the hydrological (i.e., water table) response of peatlands to climatic changes. A total of 1217 sites were included in the dataset from published and unpublished sources. The CORESITES table contains site location and summary data for each profile, as well as an estimate of total carbon mass per unit area (in megagrams of C per hectare). Total carbon mass per unit area at each location was calculated using bulk density and carbon content through each profile. The PROFILES table contains data for depth (in centimeters), bulk density (in grams per cubic meter), ash and carbon content (in percentage), and material descriptions for contiguous samples through each peat profile. Data gaps for bulk density and C content were filled using interpolation, regression trees, and assigned values based on material description and/or soil classification to allow for the estimation of total carbon mass per unit area. A subset of the sites (N = 374) also have pH and pore water trace-elemental geochemistry data and are found in the WATER table. The REFERENCES table contains the full citation of each source of the data and is linked to each core location through the SOURCEDATA table. The LOOKUP table defines codes in the database that required more space that what was sufficient in the metadata tables. The data can be accessed on Open Government Canada and will be useful for future work on carbon stock mapping and ecosystem modeling. All metadata and data are provided © Her Majesty the Queen in Right of Canada, 2023 and information contained in this publication may be reproduced for personal or public noncommercial purposes with attribution, whereas commercial reproduction and distribution are prohibited except with written permission from NRCan; complete details are noted in the Supporting Information file Metadata S1 (see Class III.B.3: Copyright restrictions).

The MOOSE fluid properties module

The Fluid Properties module within the Multiphysics Object-Oriented Simulation Environment (MOOSE) is used to compute fluid properties for numerous applications, ranging from nuclear reactor thermal hydraulics to geothermal energy. Those applications drove the development of the module to enable numerous different fluid equations of states, property lookups with primitive and conserved flow variable to cater to pressure and density-driven solvers, and an object-oriented design facilitating expansion and maintenance. Each fluid property is implemented in its own class but inherits capabilities such as automatic differentiation, automated out-of-bounds handling or variable conversion capabilities. This paper presents the module, its design, its user and developer interface, its content in terms of fluids and properties, and several of its applications showing its major role in the MOOSE simulation ecosystem. Program summaryProgram title: MOOSE Fluid Properties moduleCPC Library link to program files:https://doi.org/10.17632/cwzhsyp6pd.1Developer's repository link:https://github.com/idaholab/moose/tree/next/modules/fluid_propertiesLicensing provisions: LGPLProgramming language: C++, PythonNature of problem: The simulation of thermal hydraulics of advanced nuclear reactor systems, such as heat pipe micro-reactors or molten-salt cooled pebble bed reactors, requires a wide variety of discretizations of the fluid flow equations, from 1D thermal hydraulics to computational fluid dynamics at various levels of fidelity, with a wide variety of coolants. Applications are developed within the MOOSE C++ framework by Argonne and Idaho National Laboratories to simulate these reactors for research and design purposes. These applications (Sockeye, SAM, others) rely on MOOSE for the computation of fluid properties. The fluid properties module contains properties for most advanced nuclear reactor coolants, including an interface to the Molten Salt Thermodynamics Database (MSTDB) developed by Oak Ridge National Laboratory. Single phase, two phase, and gas mixtures fluid properties are computed by the module.Solution method: The fluid properties module includes numerous numerical methods to support the wide range of applications, notably forward automatic differentiation, conversion methods between pressure and density-driven variable sets, spline-based table lookups which are the current state of the art for the fast computation of fluid properties. The integration with MOOSE facilitates uncertainty quantification with regards to the fluid properties and optimization studies with regards to the fluid composition.

Analysis of FPGA Architecture with Hybrid Logic Blocks Based on ULG and LUT

This paper analyzes the efficiency of different FPGA architectures consisting of Hybrid logic blocks (HLB) formed with Look-up Tables (LUTs) and Universal Logic Gates (ULGs). Pure LUT-based architectures induce area overhead and a longer delay. A ULG is designed to realize an efficient architecture compared to pure LUT-based architectures. Using this ULG and LUT in different proportions, HLBs are designed. The Field Programmable Gate Array (FPGA) architectures containing these proposed HLB structures are analyzed using various Microelectronics Centre of North Carolina (MCNC) benchmark circuits and compared with the performance of the existing designs. The result shows that the proposed architecture design can save a tremendous amount of time in overall flow and routing along with area improvements.

Rejection of Surface Clutter in Far Sidelobes for Airborne Radars

In airborne radar, false alarms caused by surface clutter returns through antenna sidelobes need to be suppressed to detect the target of interest. Sidelobe blanking is widely used to reject false alarms. Ideally, an auxiliary antenna radiation pattern should cover entire sidelobes of the main antenna radiation pattern. However, this may not always be satisfied, especially in the case of far sidelobes (FSL) of the main antenna, for various reasons. In this work, rejection techniques for false alarms caused by surface clutter returns through FSL are proposed. The distortion of the measured angle and velocity of the surface clutter are explained, and a measurement process for estimating the true location and velocity of the clutter is derived. Subsequently, two techniques for false alarm rejection are presented. The first technique is based on the look-up table (LUT) of FSL, which is robust but not practical due to the time and cost required to build a LUT with sufficient resolution. In the second technique, false alarms are rejected when they satisfy predefined criteria derived from their common characteristics, and flight test results confirm that false alarms can be effectively rejected by applying the second technique.

Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types

The satellite-based Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising technique for detecting surface soil moisture (SSM), which plays a pivotal role in various applications. Different approaches have been developed for SSM retrieval, including parametric semi-empirical models and non-parametric machine learning methods. This study, however, specifically focuses on constructing and evaluating semi-empirical fit models. To this end, the study compares the effectiveness of our enhanced pixel-by-pixel model, the land cover-based linear model, and the Reflectivity-Vegetation-Roughness (R-V-R) model across different land surface types, aiming to both evaluate their efficacy and identify potential limitations. In the assessment, various factors were taken into consideration, such as the correction for vegetation and roughness attenuation, fitting functions employed, and the utilization of a lookup table (LUT). The results of the evaluation showed variations in the performance of the retrieval models across different land cover types, highlighting the impact of the choice of fitting functions and attenuation correction strategies on the accuracy of soil moisture retrieval. The pixel-by-pixel model demonstrated the highest prediction accuracy, with an unbiased root mean square difference (ubRMSD) of 0.056 cm3/cm3 and a correlation coefficient of 0.896. By showcasing these outcomes, this research underscores the significance of accounting for surface conditions and integrating relevant data to enhance the accuracy of GNSS-R SSM retrieval, thereby contributing to the advancement of SSM monitoring methodologies.

A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

0.765-FJ/Bit/Search Content Addressable Memory Using Search Line Pre-Charge-Free (SLPF) Scheme

Content Addressable Memories (CAMs) are high-speed hardware lookup tables that are crucial in routing, packet classification, search, and other fast look-up applications. Despite having the advantage of high speed, they have limitations due to their power hungriness. This paper addresses the limitations of existing CAMs with two novel modifications in the proposed Search Line Pre-Charge-Free Low-Power-Content Addressable Memory (SLPFCAM) structure. The first novelty is their gated evaluation matching circuit and the second is a match line pre-charge controller. The proposed design reduces the energy consumption and improves the speed of the CAM by eliminating the SLprecharge phase in the CAM operation. The pre-charge controller prevents the attempt of match line pre-charge when it is not required. The design has been implemented using Cadence Virtuoso in a 90-nm CMOS technology with 1-V supply, and the post-layout simulation results show that the SLPFCAM makes significant improvements with a minimum of 83% less power consumption, a 91% less search delay, and the average energy consumption being 0.765 fJ/bit/search.

On Matrix Representation of Extension Field GF(pL) and Its Application in Vector Linear Network Coding.

For a finite field GF(pL) with prime p and L>1, one of the standard representations is L×L matrices over GF(p) so that the arithmetic of GF(pL) can be realized by the arithmetic among these matrices over GF(p). Based on the matrix representation of GF(pL), a conventional linear network coding scheme over GF(pL) can be transformed to an L-dimensional vector LNC scheme over GF(p). Recently, a few real implementations of coding schemes over GF(2L), such as the Reed-Solomon (RS) codes in the ISA-L library and the Cauchy-RS codes in the Longhair library, are built upon the classical result to achieve matrix representation, which focuses more on the structure of every individual matrix but does not shed light on the inherent correlation among matrices which corresponds to different elements. In this paper, we first generalize this classical result from over GF(2L) to over GF(pL) and paraphrase it from the perspective of matrices with different powers to make the inherent correlation among these matrices more transparent. Moreover, motivated by this correlation, we can devise a lookup table to pre-store the matrix representation with a smaller size than the one utilized in current implementations. In addition, this correlation also implies useful theoretical results which can be adopted to further demonstrate the advantages of binary matrix representation in vector LNC. In the following part of this paper, we focus on the study of vector LNC and investigate the applications of matrix representation related to the aspects of random and deterministic vector LNC.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.

Estimation of electrical conductivity models using multi-coil rigid-boom electromagnetic induction measurements

Electromagnetic induction measurements from multi-coil configuration instruments are used to obtain information about the electrical conductivity distribution in the subsurface. The resulting inverse problem might not have a unique and stable solution. In that case, a local inversion method can be trapped in a local minimum and lead to an incorrect solution. In this study, we evaluate the well-posedness of the inverse problem for two and three-layered electrical conductivity models. We show that for a two-layered model, uniqueness is ensured only when both in-phase and quadrature data are available from the measurements. Results from a Gauss–Newton inversion and a lookup table demonstrate that the solution space is convex. Furthermore, we demonstrate that for even a simple three-layered model, the data contained in such measurements are insufficient to reach a correct or stable solution. For models with more than 2 layers, independent prior information is necessary to solve the inverse problem. The insights from the numerical examples are applied in a field case.

Low-latency 100 Gb/s PAM-4 PON with a 42.5 dB power budget over the 20 km anti-resonant hollow-core fiber.

The surging growth in data traffic has necessitated the development of higher-speed, lower-latency intensity modulation and direct detection (IM/DD) passive optical networks (PONs) with higher power budgets. To address the inherent limitations of traditional silica-based solid-core fibers, anti-resonant hollow-core fibers have garnered significant attention from both academia and industry. In this Letter, we present an experimental demonstration of 100 Gb/s PAM-4 IM/DD PON transmission over a 20 km anti-resonant hollow-core fiber in the C band, utilizing low-complexity digital signal processing (DSP). The result achieves a record high power budget of 42.5 dB, facilitated by a 3-tap weighted lookup table (LUT) at the optical line terminal (OLT) side and a semiconductor optical amplifier used as a preamplifier at the optical network unit (ONU) side. This represents the highest power budget reported for IM/DD PON to date, to the best of our knowledge,and offers a promising alternative for the future evolution of PON systems.

Compact widely tunable laser integrated on an indium phosphide membrane platform

We present the design, fabrication, and characterization results of a compact, widely tunable laser realized on an indium phosphide membrane-on-silicon (IMOS) platform. The laser features a compact Mach–Zehnder interferometric structure as the wavelength-selective intracavity filter with a footprint of 0.13 mm2. The filter design is optimized to ensure narrow filter transmission and high side-mode-to-main-mode-ratio, enabling single-mode operation for the laser. The high optical confinement on the IMOS platform can support tight waveguide bends. Leveraging this, the laser achieves a short cavity length, further enhancing the single-mode operation. Measurement results indicate a threshold current of 29 mA and a maximum on-chip output power of approximately 3.6 dBm and wall plug efficiency of 1.8%. The side-mode suppression ratio ranges from 30 to 44 dB, with a tuning range spanning 40 nm, from 1555 to 1595 nm. A complete tuning lookup table is generated via an automated setup incorporating a stochastic search algorithm.

3D Lightweight Cryptosystem Design For IoT Applications Based On Composite S-Box

The security of the Internet of Things (IoT) depends on strong cryptographic functions that require extensive computation and resources. The security of Internet of Things (IoT) relies on the use of strong cryptographic functions that demand extensive computation and resources. Therefore, the selection of a cryptographic system is influenced by the computational and communicational capabilities of IoT devices, including their energy requirements, memory constraints, and execution time. This paper aims to develop a lightweight and secure encryption algorithm for IoT applications, focusing on advancing cryptanalysis techniques. We propose a new lightweight algorithm named enhanced 3D RECTANGLE, designed to deliver robust security for IoT applications and optimized for cell phones with minimal memory usage, low power consumption, and efficient performance. The RECTANGLE block cipher was chosen for this research due to its high efficiency and speed relative to other lightweight algorithms, despite some security issues. The proposed enhanced 3D RECTANGLE algorithm improves confusion and diffusion properties through a new 3D array block rotation method for 4x4 plaintext, based on a 128-bit key and 16 rounds. Additionally, we designed a 4x4 composite S-Box with a Galois field pipelining structure, offering a more advanced solution compared to the Look-Up Table method. The cryptanalysis, avalanche effect, and bit error rate tests were conducted to verify the security strength of the proposed algorithm. The proposed algorithm was evaluated against two existing algorithms, RECTANGLE and Extended 3D RECTANGLE. The enhanced 3D RECTANGLE algorithm provides better cryptanalysis, demonstrating a 40% avalanche effect and achieving a bit error rate (BER) of 31%, indicating its greater effectiveness compared to the other two lightweight algorithms.