RAM Size Research Articles

TinyML for smart agriculture: Comparative analysis of TinyML platforms and practical deployment for maize leaf disease identification

Maize, a vital global food crop, is susceptible to various diseases impacting agricultural yield and quality, necessitating effective monitoring strategies. This study presents a comprehensive approach utilizing advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classification. Leveraging datasets from Kaggle, Harvard Dataverse, and Mendeley, the proposed CNN maize disease detection (MDD) system integrates optimization techniques, data augmentation, batch size adjustments, and fine-tuning strategies. Evaluation through data splitting reveals significant correlations among maize leaf classifications and the influence of data augmentation on image quality. Following training, the CNN-MDD model is deployed on a Microcontroller Unit (MCU) simulation, achieving promising optimization parameters: latency of 7.60 ms, RAM size of 726.60 KB, classifier flash size of 344.70 KB, and an accuracy of 94.60 %. These optimized parameters align well with MCU specifications, highlighting the potential for practical deployment on low-cost edge devices like Arduino Ble 33 Sense. Additionally, this research contributes to the creation of a Ghanaian maize crop pest and disease dataset and the development of an optimal and cost-effective CNN-MDD MCU edge device, aiming to provide local farmers in Ghana and Sub-Saharan Africa with a swift, offline solution for maize disease detection, thus fostering sustainable agricultural practices. We conduct a comparative analysis of Edge Impulse and TensorFlow, two prominent machine-learning platforms for TinyML model development and deployment in various applications, including smart crop disease detection. Utilizing a dataset comprising 12,344 raw images and 7,454 augmented images of healthy and diseased maize leaves, we evaluate the workflow, features, performance, and usability of each platform. Our findings indicate that Edge Impulse holds a slight advantage over TensorFlow in terms of ease of use, reliability, and memory footprint, whereas TensorFlow offers greater flexibility, customization options, and higher accuracy. We conclude that while both platforms are suitable for crop disease detection, a combined approach allows for leveraging TensorFlow's robust training capabilities for complex neural networks alongside the simplicity of Edge Impulse for deploying models on low-powered edge devices. This synergistic approach enhances the effectiveness and efficiency of crop disease detection systems, ultimately benefiting agricultural practices and food security.

Comparative Analysis of Machine Learning Algorithms for Laptop Value Estimation

The increasing prevalence of laptops in homes, schools, and workplaces makes accurate value estimations essential to guide buyers in making informed decisions. This study compares the effectiveness of four machine learning algorithms—Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM)—in predicting laptop prices based on specifications such as brand, processor speed, RAM size, storage capacity, screen size, and weight. The performance of these models is evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared (R²) metrics. Results indicate that MLR and RF provide superior performance, achieving the lowest RMSE of 0.02 and the highest R² score of 1.0, demonstrating their suitability for datasets with linear feature relationships. The findings highlight the significance of selecting appropriate models based on the nature of the dataset, suggesting that while ANN and SVM are potent for complex, non-linear data, MLR and RF are preferable for linear datasets.

Analysis of Differences Across Types of Interior Parts of Computer and Computer Price

The enhancement of digital computers takes an active part in promoting the development of different aspects of the world. In the meantime, interior parts of computers are also rapidly enhanced and refined. Various combinations of inner parts of a computer would play a decisive factor in the cost so the aim of this article would discuss the relationship between the performance parameters of computers and the prices. This article divides the performance parameters of computers into nine aspects, such as speed, RAM size, etc., With an emphasis on the connection between the price of the computer and its performance parameters. This article employs a multiple linear regression and a logistic regression model to estimate the pricing using computer configuration parameters. The MSE, AIC and other parameters are established to measure the goodness of fit for models. In order to forecast prices for computers with certain performance specifications, an optimal model is finally developed.

Comparing Nonresponders and Responders of Online Intercept Surveys: A Large-Scale Experimental ICT Security-Related Study

Online surveys have become a popular way to collect data. However, response rates are low, specifically for online intercept-based surveys, which can be as low as 1%. This raises questions about the accuracy of the inferences based on these results. Furthermore, it is difficult to compare the characteristics and behavior of the responders and nonresponders as there is very limited information on nonresponders. The objective of this article is to present a unique comparison of online intercept survey responders, nonresponders, and partial responders. The sample includes 192,566 U.S.-based users who went through a research experiment during the installation process of ESET online security software. During the process, users were asked to enable or disable the detection of potentially unwanted applications. At the end, they could also opt to answer questions on a short security-related survey. The users were split into three groups: (a) nonresponders ( n = 184,010 ), (b) complete responders ( n = 6,750 ), and (c) partial responders ( n = 1,806 ). There were only slight differences between the responder and nonresponder groups in their hardware (i.e., computer CPU quality and RAM size). Responders and nonresponders differed in their behavior. Complete responders enabled the detection of potentially unwanted applications significantly more often than nonresponders (on average by 4.5%) and spent more time on the screen that provided details about this feature. Additional comparisons showed that complete responders were slightly younger and more educated than partial responders. We conclude that there are only slight differences between online intercept survey responders and nonresponders and that these differences manifest in computer usage-related decisions. Despite the low overall response rates, online product-related surveys can provide useful insights about the user base. Nevertheless, the companies that use online surveys should be careful because behavior might differ for users in specific situations.

Seasonal changes in testis size, testosterone levels and sperm production quality in meat rams.

The aim of the present study was to evaluate seasonal changes in testes size, serum testosterone (T) levels and sperm production characteristics of 24 adult (2-3 years old) rams of meat breeds: Charollais (n=6), Ile de France (n=6), Dorset (Polled) (n=5) and Dorper (Dorper, n=7). Semen was collected with artificial vagina using a female in spontaneous oestrus (September-November, breeding season, BS) or a female with induced oestrus (March-July, non-breeding season, NBS). A total of 672 ejaculates were examined. Jugular vein blood was collected to determine testosterone levels during BS and NBS, and serum was extracted. Results of the present study indicate that there was no significant change in parameters such as body weight (kg) and testes length, both right and left, during BS and NBS, irrespective of the breed. The remaining metrics of testes differ in varying degrees between breeds of rams (with the exception of the Dorper breed) between BS and NBS. No significant variation in these parameters was found in Dorper rams. For all breeds, season had a statistically significant effect on important parameters such as total sperm count and progressive sperm motility after thawing. There was also significant seasonal variation in ejaculate volume across the three breeds, excluding il-de-France rams. The most constant was the sperm concentration, which was unchanged in BS and NBS in the three breeds, excluding Dorset rams, which showed a slightly significant decrease in this indicator in the non-breeding season. The serum content of the major male hormone testosterone (T) in rams of all breeds showed significant changes between BS and NBS, decreasing by a highly significant amount in the non-breeding season.

Big–Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors

This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the processor core are determined at the run-time. In these systems, embedded RAM and flash memory size is typically limited to less than 1 megabyte to save power. This limited memory imposes restrictions on the complexity of the neural networks model that can be mapped to these devices and the required trade-offs between accuracy and battery life. To address these issues, we propose and evaluate alternative ‘big–little’ neural network strategies to improve battery life while maintaining prediction accuracy. The strategies are applied to a human activity recognition application selected as a demonstrator that shows that compared to the original network, the best configurations obtain an energy reduction measured at 80% while maintaining the original level of inference accuracy.

Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.

Peaberry and normal coffee bean classification using CNN, SVM, and KNN: Their implementation in and the limitations of Raspberry Pi 3

<abstract> <p>Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries.</p> </abstract>

Experimental Study on the Effect of Waste Valve Load and the Volume of Air Chamber on the Performance of the Hydraulic Ram Pump (Hydram) to Water the Paddy Field in Pakandangan, Padang Pariaman Regency

This 2019 DIPA Grand’s research designed and fabricated the size of hydraulic ram (hydram) pump utilized in Pakandangan, Padang Pariaman Regency. There is a water source in this area which has not been functioned adequately to irrigate the paddy field of 10 hectares due to the location of the paddy field which is higher than the water source. However, the use of designed hydram pump has no been maximized as the pump’s optimal performance was not determined yet. Therefore, a hydram pump was designed by varying the load of waste valve in the weight of 400 g, 600 g, 800 g, 1,000 g, and 1,200 gr. It was also varied in the volume of the chamber when the pump operated which were 4.86 lt, 5.76 lt, 6.48 lt, 7.29 lt, and 8.1lt. The height (Hd) of the inlet pipe was 1 m, and the lift height (Hs) of the outlet pipe was 5 m. The results obtained from Pump performance increases with increasing cylinder volume. The increase in the load of the exhaust valve volume of the tube remains a significant decrease. Hydram pump performance occurs at a load of 400 g with a tube volume of 8.1 l with an efficiency of 53%

CloudJS: novel cloud-based design framework for text-file encryption

Purpose Cloud computing is becoming increasingly popular as it facilitates convenient, ubiquitous, on-demand network access to a shared pool of configurable computing resources and applications that can be quickly retrieved and released. Despite its numerous merits, it faces setbacks in data security and privacy. Data encryption is one of the most popular solutions for data security in the cloud. Various encryption algorithms have been implemented to address security concerns. These algorithms have been reviewed along with the Jumbling Salting algorithm and its applications. The framework for using Jumbling Salting to encrypt text files in the cloud environment (CloudJS) has been thoroughly studied and improvised. The purpose of this paper is to implement the CloudJS algorithm, to discuss its performance and compare the obtained results with existing cloud encryption schemes. Design/methodology/approach The paper uses six research questions to analyze the performance of CloudJS algorithm in the cloud environment. The research questions are about measuring encryption time and throughput, decryption time and throughput, the ratio of cipher to the plain text of CloudJS algorithm with respect to other Cloud algorithms like AES and DES. For this purpose, the algorithm has been implemented using dockers-containers in the Linux environment. Findings It was found that CloudJS performs well in terms of encryption time, decryption time and throughput. It is marginally better than AES and undoubtedly better than DES in these parameters. The performance of the algorithm is not affected by a number of CPU cores, RAM size and Line size of text files. It performs decently well in all scenarios and all resultant values fall in the desired range. Research limitations/implications CloudJS can be tested with cloud simulation platforms (CloudSim) and cloud service providers (AWS, Google Cloud). It can also be tested with other file types. In the future, CloudJS algorithm can also be implemented in images and other files. Originality/value To the best of the knowledge, this is the first attempt to implement and analysis of a custom encryption algorithm (CloudJS) in the cloud environment using dockers-containers.

Emerging 5G IoT Smart System Based on Edge-to-Cloud Computing Platform

Security over internet communication has now become difficult as technology is increasingly more effective and faster, particularly in resource limited devices such as wireless sensors, embedded devices, internet of things (IoT), radio frequency identification (RFID) tags, etc. However, IoT is expected to connect billions of computers as a hopeful technology for the future. Hence, security, privacy, and authentication services must protect the communication in IoT. There are several recent considerations, such as restricted computing capacity, register width, RAM size, specific operating environment, ROM size, etc. that have compelled IoT to utilize conventional measures of security. These technologies require greater data speeds, high throughput, expanded power, lower bandwidth, and high efficiency. In addition, IoT has transformed the world in light of these new ideas by offering smooth communication between heterogeneous networks (HetNets).

Identification of Fruits and Vegetables using Embedded Sensor

In precision agriculture, computational techniques play a critical role. It is a vital job to detect and classify fruits and vegetables from crops and gardens. In fruit detection and yield estimation, an intelligent detection system is used as a key technology where embedded sensor techniques are being implemented. Many researchers developed a different classification system for classifying the fruits or vegetables using image processing techniques and machine learning algorithm. Big computers with fast CPUs and GPUs, massive RAM sizes or cloud algorithms have often been associated with them even simple applications. In this article Tensorflow and Arduino BLE sense are used to identify the fruits and vegetable with less resource.

Acehnese traditional clothing recognition based on augmented reality using hybrid tracking method

Aceh's traditional clothing is cultural heritage in Indonesia. In today's, the problem faced is the lack of media to introduce cultural heritage in Aceh. Therefore was formed a media which could recognition Acehnese traditional clothing using Augmented Reality technology. Augmented Reality technology is used to users can add virtual objects to real environments. In this research, the method used is the Hybrid Tracking method, a Hybrid Tracking method is a combination tracking system of two or more tracking techniques, hybrid tracking is used to get a better tracking system and to get tracking speeds based on better distances. The results showed that the reading of markers by the cameras was greatly influenced by distance, light intensity, marker angle, and size of the device's RAM. The closest distance from the marker to the camera that can display 3D objects is 20 cm while the farthest distance cannot display 3D objects is 100 cm. And the angle of reading the marker is only around 0° an angle of 45°. The average distance that can detect markers is 0.5 m, the average time that can be detected is 0.068 sec, and the average speed obtained is 7.505 m / sec.

Hardware Performance Evaluation of De novo Transcriptome Assembly Software in Amazon Elastic Compute Cloud

Background:Bioinformatics software for RNA-seq analysis has a high computational requirement in terms of the number of CPUs, RAM size, and processor characteristics. Specifically, de novo transcriptome assembly demands large computational infrastructure due to the massive data size, and complexity of the algorithms employed. Comparative studies on the quality of the transcriptome yielded by de novo assemblers have been previously published, lacking, however, a hardware efficiency-oriented approach to help select the assembly hardware platform in a cost-efficient way.Objective:We tested the performance of two popular de novo transcriptome assemblers, Trinity and SOAPdenovo-Trans (SDNT), in terms of cost-efficiency and quality to assess limitations, and provided troubleshooting and guidelines to run transcriptome assemblies efficiently.Methods:We built virtual machines with different hardware characteristics (CPU number, RAM size) in the Amazon Elastic Compute Cloud of the Amazon Web Services. Using simulated and real data sets, we measured the elapsed time, cost, CPU percentage and output size of small and large data set assemblies.Results:For small data sets, SDNT outperformed Trinity by an order the magnitude, significantly reducing the time duration and costs of the assembly. For large data sets, Trinity performed better than SDNT. Both the assemblers provide good quality transcriptomes.Conclusion:The selection of the optimal transcriptome assembler and provision of computational resources depend on the combined effect of size and complexity of RNA-seq experiments.

New software for corrosion research based on IPC series potentiostats

The developed IPC-CorrMeter software is intended for studying corrosion of metals, alloys, and coatings and estimating the efficiency of anti-corrosion protection by emulating the operation of a “Corrosimeter” device in potentiostats of the IPC series that allows implementing well-known and reliable corrosion-electrochemical methods, such as linear polarization resistance, zero resistance amperometry, and potentiometry in the existing serial IPC potentiostats. The advantages of the new software include: a possibility of continuous automatic use of these methods in the study of corrosion processes, obtaining results almost in real time mode, high sensitivity and a wide adjustable range of measurements of the rates of general and pitting corrosion (from 1 nm to tens of mm per year), no absence of any restrictions regarding the RAM size of the device. Quantitative dependences of the general and pitting corrosion rate of steel St3 in water are obtained in the presence of the industrial СARTEС-28B corrosion inhibitor for hydraulic testing of mud pumps. They confirm the reliability, accuracy, high sensitivity, and validity of the IPC-CorrMeter software and equipment for researching metal corrosion and allow evaluating the efficiency of anti-corrosion protection agents, as well as the high efficiency of this inhibitor.

Exogenous melatonin advances the ram breeding season and increases testicular function

Governed by melatonin, ovine reproductive seasonality limits production outcomes due to periods of decreased reproductive efficiency. Though it is established that slow-release melatonin implants improve out of season reproductive performance in the ewe, the comprehensive effects of exogenous melatonin in the ram remain inconclusive. This study aimed to ultimately clarify the ability of exogenous melatonin to alter ram reproductive function during the non-breeding season and the subsequent breeding season. Hence, we investigated the effect of exogenous melatonin on reproductive endocrinology, semen quality and production, testicular size and libido in Merino and Poll Dorset rams (n = 31, using a subset of 18 rams for analysis of semen production and quality). Melatonin treatment resulted in elevation of melatonin in seminal plasma from 1–8 weeks post-implantation and in blood plasma at 6 weeks post-implantation. The blood plasma testosterone of implanted rams was greater than controls at both 6 weeks post-implantation and during the following breeding season. Implanted rams exhibited increased testicular size and number of sperm per ejaculate from 3–12 weeks post-implantation but did not demonstrate any change in sperm motility or morphology in response to treatment. Compared to their control counterparts, melatonin-treated Poll Dorset rams exhibited a lower percentage of sperm DNA fragmentation during several weeks of the non-breeding season. Though melatonin increased the likelihood of ejaculate collection in Poll Dorset rams (P < 0.05), libido was otherwise unaffected by treatment. Melatonin did not alter seminal plasma concentrations of inhibin A or Anti-Mullerian hormone, however, for the first time in the ram we have shown Anti-Mullerian hormone to be positively correlated with the number of sperm per ejaculate and sperm motility (r = 0.464 and 0.3242 respectively, P < 0.001), and inhibin A to be correlated to the number of sperm per ejaculate (r = 0.1786, P = 0.0135). These results indicate that melatonin is able to both systemically upregulate reproduction and act directly upon testicular function in the ram.

MiniChain: A lightweight protocol to combat the UTXO growth in public blockchain

The current UTXO-based blockchains require the validators to keep the entire ever-growing UTXO set to verify transactions, which is unsuitable for ordinary machines since they occupy a large size of RAM in the long run, resulting in network centralizations. Recently, stateless blockchain technology has been proposed which uses the accumulator to combine the large UTXO set into one short, constant-sized commitment. However, the UTXO commitments in these methods are inefficient since the UXTO set is required dynamic addition and removal of elements as transactions are processed. In this work, we propose MiniChain, which replaces the UTXO set with two append-only data structures: STXO (Spent Transaction Outputs) set and TXO (Transaction Outputs) set. Thus, a valid UTXO must belong to the TXO set, but not in STXO set. Then, we construct a novel STXO commitment and TXO commitment by using a trapdoor-less RSA accumulator and a Merkle Mountain Range (MMR) respectively, greatly increasing the efficiency of accumulator. Besides, we introduce a cache mechanism, by storing the STXOs of latest N blocks, the transaction proof can be kept alive for a period of time, avoiding constantly recomputing proofs for unaccepted transactions. Our evaluation shows that (i) MiniChain only needs a fixed-size RAM and the disk usage grows very slow since only the block headers are stored; (ii) comparing to the state-of-the-art work, the performance of the accumulator update has been improved from O(n2) to O(n), enabling MiniChain to support a higher TPS.

Computational algorithms for calculating the algebraic immunity of nonlinear nodes of replacing symmetric ciphers

Block of nonlinear replacements (complication node, substitution block, S-box) is an important element of most modern symmetric ciphers. This is an elementary cryptographic primitive designed to mix input data and introduce non-linearity. By replacing small blocks with other blocks, mixing and scattering of data is achieved, and repeated cyclic repetition of such a procedure makes it possible to achieve the required cryptographic properties of the cipher. Many different criteria are put forward to substitution blocks (balance, high nonlinearity, low autocorrelation, correlation immunity, required avalanche properties, and many others). Each criterion expresses a formalized requirement of resistance to certain types of cryptographic analysis (differential, linear, etc.), i.e. when designing ciphers, they use an integrated approach, choosing S-blocks according to the totality of individual indicators. With the development of algebraic cryptanalysis, a new indicator of the effectiveness of substitution nodes has appeared – algebraic immunity, which is calculated as the minimum degree of the simplest (in a sense) algebraic equation describing the S-block. To search for such an equation, special methods are used, based on the construction of Gröbner bases. If you specify a knot of substitutions through a Boolean function, then to calculate algebraic immunity it is enough to find a function of the least degree from the annihilator space. This article discusses various methods of calculating algebraic immunity, analyzes their computational efficiency, discusses implementation details, substantiates methods for optimizing calculations in terms of time (in terms of number of operations) and capacitive (in terms of memory costs) of complexity. An advanced algorithm for calculating algebraic immunity is proposed, optimized for computing resources, including the necessary RAM sizes. The results of experimental studies are presented, in particular, the results of calculations of the algebraic immunity of 8x8 S-blocks for some well-known modern ciphers (AES, Kalina, Grasshopper, BelT), as well as the results obtained for random 8x8, 9x9 and 10x10 S-blocks.

Sonar Technology Assisted Vehicle Sensor

In the Philippines, more than half of its vehicular accidents are caused by motorcycles, and most of the reasons why these accidents are happening is because of distracted driving, lowered road awareness, delayed response to emergency such as accidents, and over speeding to name a few as confirmed by World Health Organization’s reports. Premised on this idea, the researchers decided to design, develop, and test a device that would provide a solution to this. Thus, the title “Sonar Technology Assisted Vehicle Sensor” was created. The device was completed after three months of research and development in Eastern Samar State University Salcedo Campus. Results of the four-stage test: the Benchmark; Alpha; Beta; and Usability tests indicated that the Sonar Technology Assisted Vehicle Sensor is fully functional and can detect vehicles approaching and can produce corresponding alarms, and can now be implemented. As an additional feature, the device has been imbedded with a Global Positioning System (GPS) Tracker that activates whenever an accident happens to the user/rider. The GPS Tracker would extract the device’s location and the system would automatically send the data to appropriate authorities using its Global System for Mobile Communications (GSM) shield through text messaging. The researchers recommend that the device should be marketed directly to motorcycle companies; its GPS system should be submitted for further testing, while the image processing should be trained in computers with RAM size of 8 GB and should be tested paired with an infrared lamp to maximize its use in night time traveling conditions.

A Low-Error, Memory-Based Fast Binary Logarithmic Converter

This brief presents a memory based fast binary logarithmic converter based on a piecewise linear approximation technique. The proposed method is simple and arithmetic operation-less, which achieves 10−4 to 10−3 maximum absolute error (MAE) while maintaining a high speed. The approach partitions the logarithmic curve of the fractional component into $2^{L}$ uniform regions and a block RAM (size $2^{L} \times $ bits) stores the approximate value of each sub-region. For any number, most significant $L$ bits of the fractional component address the memory location of the logarithmic converter. The hardware synthesis result, implemented with 26 bits fractional precision on Virtex-6 field-programmable gate array device, shows 75% improvement in MAE and 27% decrease in critical path delay compared to the current state-of-the-art techniques in the worst-case scenario. In Otsu’s image thresholding algorithm, the proposed logarithmic converter with 3.08 ( $=2^{8} \times 12$ ) kbits memory size adequately meets the accuracy requirement for improved image segmentation.