Required Storage Space Research Articles

RANCANG BANGUN SISTEM DIGITALISASI PENGARSIPAN (E-SIP) BERBASIS WEBSITE

An organization can never be separated from administrative activities. Archivesare an activity that cannot be separated from administrative activities. Archives playan important role for organizations because archives are used to store and maintainimportant records or documents related to the organization's activities. Apart fromthat, archives also help organizations to avoid the risk of losing information ordocuments that could be considered valuable. By having well-organized archives,organizations will find it easier to access the information they need. In the past,archives were managed manually, such as notebooks and documents which werestill in the form of paper, which required storage space and required time in the search process. Therefore, nowadays many organizations choose to switch to digitalarchiving, which is an easier and faster process than manual archiving. Archivesmanagement at BRIDA East Java Province has not been carried out using a digitalsystem. To minimize risks in archival storage, it is better to change the manualrecords management system to a digital archive. The preparation of this finalassignment used qualitative research methods. Qualitative research usually collectsdata by conducting observations, interviews, and documenting research activities.Because archiving in BRIDA East Java Province is still done manually, in thisresearch a website-based design idea called Electronic Archives (E-SIP) wasdesigned to simplify the digital archiving process. With the creation of this design,it is hoped that it will facilitate the management of archives at BRIDA East JavaProvince and that archiving can be carried out digital.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

Investigating cross-docking criteria for raw materials with hybrid MCDM

The cross-docking process involves unloading raw materials from incoming vehicles and immediately loading them onto outgoing vehicles. This can greatly improve supply chain operations and expedite transportation to the factory. By minimizing warehouse storage expenses, space requirements, and inventory management, the process can be streamlined effectively. In industries where production profiles need to be responsive to market demands, the location of the cross-docking facility and transportation planning are crucial. This article aims to investigate the criteria for selecting a cross-docking location, using a case study of Arta Profile Company in Ardabil. The research population includes all industrial engineers and production planners in the company, with 10 industry experts selected for this study. The analytic network process (ANP) and grey complex proportional assessment (COPRAS-G) methods were used to compare and prioritize cross-docking locations. Additionally, the fuzzy DEMATEL method was used to determine the strength of connections between different locations. The COPRAS-G approach helps decision-makers make informed decisions by evaluating different options and their importance. The industry experts identified 28 criteria for this study. The results from the network analysis method showed that the most important criterion is the vehicle routing problem (VRP), with a value of 0.46. According to the ANP technique, strategic decision-making is the best option. The fuzzy DEMATEL method revealed that distribution centers are the most important factor, followed by reduced delivery/distribution operations, and finally, the cost of late or early delivery.

Verifiable privacy-preserving semantic retrieval scheme in the edge computing

Edge computing, with its characteristics of low latency and low transmission costs, addresses the storage and computation challenges arising from the surge in network edge traffic. It enables users to leverage nearby edge servers for data outsourcing and retrieval. However, data outsourcing poses risks to data privacy. Although searchable encryption is proposed to secure search of outsourced data, existing schemes generally cannot meet the requirements of semantic search, and they also exhibit security risks and incur high search costs. In addition, edge servers may engage in malicious activities such as data tampering or forgery. Therefore, we propose a verifiable privacy-preserving semantic retrieval scheme named VPSR suitable for edge computing environments. We utilize the Doc2Vec method to extract text feature vectors and then convert them into matrix form to reduce storage space requirements for indexes, queries, and keys. We encrypt matrices using an improved secure k-nearest neighbor (kNN) algorithm based on learning with errors (LWE) and calculate text similarity by solving the Hadamard product between matrices. Additionally, we design an aggregable signature scheme and offload part of the result verification tasks to edge servers. Security and performance analysis results demonstrate that the VPSR scheme is suitable for edge computing environments with high encryption and search efficiency and low storage cost while ensuring security.

Optimized Design of Direct Digital Frequency Synthesizer Based on Hermite Interpolation

To address the issue of suboptimal spectral purity in Direct Digital Frequency Synthesis (DDFS) within resource-constrained environments, this paper proposes an optimized DDFS technique based on cubic Hermite interpolation. Initially, a DDFS hardware architecture is implemented on a Field-Programmable Gate Array (FPGA); subsequently, essential interpolation parameters are extracted by combining the derivative relations of sine and cosine functions with a dual-port Read-Only Memory (ROM) structure using the cubic Hermite interpolation method to reconstruct high-fidelity target waveforms. This approach effectively mitigates spurious issues caused by amplitude quantization during the DDFS digitalization process while reducing data node storage units. Moreover, this paper introduces single-quadrant ROM compression technology to further diminish the required storage space. Experimental results indicate that, compared to traditional DDFS methods, the optimization scheme proposed in this work achieves a ROM resource compression ratio of 1792:1 and a 14-bit output Spurious-Free Dynamic Range (SFDR) of −88.134 dBc, effectively enhancing amplitude quantization precision and significantly lowering spurious levels. This significantly improves amplitude quantization precision and reduces spurious levels. The proposed scheme demonstrates notable advantages in both spectral performance and resource utilization efficiency, making it highly suitable for resource-constrained embedded systems and high-performance applications such as radar and communication systems.

Space‐efficient data structures for the inference of subsumption and disjointness relations

AbstractConventional database systems function as static data repositories, storing vast amounts of facts and offering efficient query processing capabilities. The sheer volume of data these systems store has a direct impact on their scalability, both in terms of storage space and query processing time. Deductive database systems, on the other hand, require far less storage space since they derive new knowledge by applying inference rules. The challenge is how to efficiently obtain the required derivations, compared to having them in explicit form. In this study, we concentrate on a set of predefined inference rules for subsumption and disjointness relations, including their negations. We use compact data structures to store the facts and provide algorithms to support each type of relation, minimizing even further the storage space requirements. Our experimental findings demonstrate the feasibility of this approach, which not only saves space but is often faster than a baseline that uses well‐known graph traversal algorithms implemented on top of a traditional adjacency list representation to derive the relations.

Optical information hiding system with pixel-free expansion visual cryptography

Abstract We propose an optical information hiding system with pixel-free expansion visual cryptography (PEVC). In the optical concealment process, initially, a PEVC scheme is utilized to encode the secret image. This approach generates visual keys of the same size as the secret image, addressing the pixel expansion issue inherent in visual cryptography encoding schemes. As a result, it significantly reduces both storage space requirements and the network bandwidth occupied during the transmission process and exhibits a higher hidden capacity. Furthermore, PEVC is combined with the optical phase retrieval algorithm for hiding, embedding the visual keys into the phase keys. In this process, wavelength and diffraction distance are introduced as keys, enhancing the security of the system. The phase keys can be fabricated into diffractive optical elements for physical preservation and transmission in tangible form. Simulation experiments and optical experimental results indicate that the system is applicable in practical scenarios and possesses excellent security and exhibits a higher hidden capacity.

Double-image encryption and authentication scheme based on compressed sensing and double random phase encoding

To enhance the security of image information, a double-image encryption and authentication scheme combining compressive sensing (CS) and double random phase encoding (DRPE) is proposed. First, two plaintext images are taken as real and imaginary parts to form a complex-valued image, which is then encoded using DRPE. Next, extract the phase of the encrypted complex-valued image and encode it into authentication information. Simultaneously, the complex-valued image is sampled using the measurement matrix, which is optimized by Schmidt orthogonalization, and then quantized to form a compressed image. Subsequently, the authentication information is embedded into the compressed image, and by applying the permutation and diffusion algorithm to it, the ciphertext image is obtained. At the receiving end, the ciphertext is decrypted using inverse permutation and inverse diffusion algorithms, and the authentication information is decoded to acquire the authentication image. Finally, synchronous authentication of the two reconstructed images is achieved using a nonlinear cross-correlation method. Additionally, the keys in the proposed scheme are generated using high-dimensional chaotic systems, effectively reducing the required storage space and transmission bandwidth. Simulation results demonstrate that the proposed scheme has high image reconstruction performance at different compression ratios and possesses outstanding security and authentication capabilities.

A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma

Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.

Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking

The global multi-object tracking (MOT) system can consider interaction, occlusion, and other ``visual blur'' scenarios to ensure effective object tracking in long videos. Among them, graph-based tracking-by-detection paradigms achieve surprising performance. However, their fully-connected nature poses storage space requirements that challenge algorithm handling long videos. Currently, commonly used methods are still generated trajectories by building one-forward associations across frames. Such matches produced under the guidance of first-order similarity information may not be optimal from a longer-time perspective. Moreover, they often lack an end-to-end scheme for correcting mismatches. This paper proposes the Composite Node Message Passing Network (CoNo-Link), a multi-scene generalized framework for modeling ultra-long frames information for association. CoNo-Link's solution is a low-storage overhead method for building constrained connected graphs. In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction, improving the graph neural network's feature representation capability. Specifically, we formulate the graph-building problem as a top-k selection task for some reliable objects or trajectories. Our model can learn better predictions on longer-time scales by adding composite nodes. As a result, our method outperforms the state-of-the-art in several commonly used datasets.

Penerapan Algoritma Additive Code Dalam Mengkompresi Record Database

The problem of storage space requirements in compressed record database applications is one of the main problems that can affect the performance of the compressed record database application itself. This is due to the large number of data records stored in the database. One solution that can be done to overcome this problem is to compress the database file. The compression process in this study was carried out based on the Additive Code algorithm by building a desktop-based database record compression application. The initial process carried out to implement this method is to prepare the SQL file to be compressed, the compression process takes place when the SQL file has finished uploading, then pressing the compression button on the database record compression application and the decompression process takes place when downloading the compressed SQL file and pressing the decompress button. on database record compression applications. Based on the results of the tests carried out, it was obtained that the Ratio of Compression (RC) was 1.33, the Compression Ratio (Cr) was reduced by 75%, Redundancy (Rd) was reduced by 25% and Space Saving (Ss) was reduced by 25%. These results indicate that the storage space requirements for database compression applications record databases more optimally than before the compression process was applied

Co-densification of rice straw and cow dung in different food-to-microorganism ratios for biogas production

Agricultural residues such as rice straw (RS) are desirable raw materials for biogas generation. However, the recalcitrant nature of RS hinders biogas production, and its low bulk density increases storage space requirements, transportation needs, and overall costs. These challenges could be resolved by pretreatment and pelletization. In this study, various thermal pretreatments were performed, and the best conditions (temperature and time) were identified. Also, rice straw and cow dung pellets (RCP) at different food-to-microorganism (F/M) ratios (0.5–2.5) were prepared. Parameters such as bulk density, moisture absorption, and drop shatter tests were conducted to evaluate the physical properties. Finally, the biochemical methane potential (BMP) study of the best RCP with varying total solids (TS: 4–12%) content was investigated. The results indicate that hot air oven pretreatment (for 60 min at 120 °C) resulted in maximum solubilization. In physical characteristics, RCP with an F/M ratio of 2.5 pellets was ideal. The bulk density of RCP 2.5 was found to be around 25 times that of the raw. Also, the TS 8% yielded maximum biomethane (279 mL/g-VSconsumed) as compared to other TS contents. Overall, this study will propel the growth of bioenergy while simultaneously tackling the pressing issues related to RS management.

Data cube-based storage optimization for resource-constrained edge computing

In the evolving landscape of the digital era, edge computing emerges as an essential paradigm, especially critical for low-latency, real-time applications and Internet of Things (IoT) environments. Despite its advantages, edge computing faces severe limitations in storage capabilities and is fraught with reliability issues due to its resource-constrained nature and exposure to challenging conditions. To address these challenges, this work presents a tailored storage mechanism for edge computing, focusing on space efficiency and data reliability. Our method comprises three key steps: relation factorization, column clustering, and erasure encoding with compression. We successfully reduce the required storage space by deconstructing complex database tables and optimizing data organization within these sub-tables. We further add a layer of reliability through erasure encoding. Comprehensive experiments on TPC-H datasets substantiate our approach, demonstrating storage savings of up to 38.35% and time efficiency improvements by 3.96x in certain cases. Furthermore, our clustering technique shows a potential for additional storage reduction up to 40.41%.

PROJECTION OF SALT WAREHOUSE CAPACITY FOR SALT PRODUCTION CENTRE AREAS IN PASURUAN REGENCY, INDONESIA

Salt is one of the strategic commodities of Indonesia and East Java is the main producing area. Pasuruan Regency contributes 38.5% of East Java’s salt production. Salt farmers in the Pasuruan Regency face various obstacles such as warehousing capacity. The inadequate storage encourages them to sell their harvests without considering prices. Therefore, an analysis of warehouse capacity is very important for developing a salt business. This study examines salt warehousing capacity in the Pasuruan Regency. The methods used in this study were analysis of the Storage Space Requirement following the Indonesian National Standard (SNI) for the Salt Warehouse (SNI 8446: 2017) and the ARIMA method to predict the salt price. This study projects that salt production in Pasuruan Regency will continue to increase with an average annual growth of 13%. The production will reach 18,093 tonnes, equivalent to 482,476 sacks of salt in 2030. Hence, the need for storage space for salt in Pasuruan Regency in 2030 is 18,093 m2. According to the results of this study, improving the capacity and quality of salt warehouses is fundamental to develop a salt business in Pasuruan Regency.

Simulasi Proses Picking Order dengan Metode Dedicated Storage Menggunakan Software Fleksim (Studi Kasus PT XYZ)

PT XYZ is a manufacturing company that produces food and drinks by providing quality products that are halal, high quality and safe for consumption by the public. In producing products, companies must provide several adequate facilities to support production activities, one of which is warehouse facilities. Currently, warehouses where finished food products are stored are stored randomly or placed in empty areas without paying attention to product entry and exit activities. This condition causes picking activities to take a long time and makes stock monitoring difficult. The aim of this research is to produce an appropriate goods storage design so that it can increase warehouse effectiveness. This research uses a special storage method. Where a special storage policy or method is permanently placed on each item. The number of product storage locations must meet the maximum storage space requirements and a simulation is carried out using FlexSim software. The research results show that designing goods storage using the dedicated storage method results in a reduction in the distance traveled for the collection process reaching 52%.

Bilingual video captioning model for enhanced video retrieval

Many video platforms rely on the descriptions that uploaders provide for video retrieval. However, this reliance may cause inaccuracies. Although deep learning-based video captioning can resolve this problem, it has some limitations: (1) traditional keyframe extraction techniques do not consider video length/content, resulting in low accuracy, high storage requirements, and long processing times; (2) Arabic language support in video captioning is not extensive. This study proposes a new video captioning approach that uses an efficient keyframe extraction method and supports both Arabic and English. The proposed keyframe extraction technique uses time- and content-based approaches for better quality captions, fewer storage space requirements, and faster processing. The English and Arabic models use a sequence-to-sequence framework with long short-term memory in both the encoder and decoder. Both models were evaluated on caption quality using four metrics: bilingual evaluation understudy (BLEU), metric for evaluation of translation with explicit ORdering (METEOR), recall-oriented understudy of gisting evaluation (ROUGE-L), and consensus-based image description evaluation (CIDE-r). They were also evaluated using cosine similarity to determine their suitability for video retrieval. The results demonstrated that the English model performed better with regards to caption quality and video retrieval. In terms of BLEU, METEOR, ROUGE-L, and CIDE-r, the English model scored 47.18, 30.46, 62.07, and 59.98, respectively, whereas the Arabic model scored 21.65, 36.30, 44.897, and 45.52, respectively. According to the video retrieval, the English and Arabic models successfully retrieved 67% and 40% of the videos, respectively, with 20% similarity. These models have potential applications in storytelling, sports commentaries, and video surveillance.

A New Method for Intelligent Multimedia Compression Based on Discrete Hartley Matrix

Multimedia data (video, audio, images) require storage space and transmission bandwidth when sent through social media networking. Despite rapid advances in the capabilities of digital communication systems, the high data size and data transfer bandwidth continue to exceed the capabilities of available technology, especially among social media users. The recent growth of multimedia-based web applications such as WhatsApp, Telegram, and Messenger has created a need for more efficient ways to compress media data. This is because the transmission speed of networks for multimedia data is relatively slow. In addition, there is a specific size for sending files via email or social networks, because much high-definition multimedia information can reach the Giga Byte size. Moreover, most smart cameras have high imaging resolution, which increases the bit rate of multimedia files of video, audio, and image. Therefore, the goal of data compression is to represent media (video, audio, images, etc.) as accurately as possible with the minimum number of bits (bit rate). Traditional data compression methods are complex for users. They require a high processing power for media data. This shows that most of the existing algorithms have loss in data during the process of compressing and decompressing data, with a high bitrate for media data (video, audio, and image). Therefore, this work describes a new method for media compression systems by discrete Hartley matrix (128) to get a high speed and low bit rate for compressing multimedia data. Finally, the results show that the proposed algorithm has a high-performance speed with a low bit rate for compression data, without losing any part of data (video, sound, and image). Furthermore, the majority of users of social media are satisfied with the data compression interactive system, with high performance and effectiveness in compressing multimedia data. This, in turn, will make it easier for users to easily send their files of video, audio, and images via social media networks.

Enhanced Privacy Preservation and Data Storage in Blockchain-Based Electric Vehicle Network

Electric and autonomous vehicles (EV and AV) are spreading quickly on the road and producing extensive amounts of data that must be stored and retrieved securely. The ultimate goal of collecting these data is to feed machine learning models and make predictions to improve the traffic system, increase road safety, and save lives. Existing systems impose the problem of privacy leakage, storage space, and operating cost. They are causing panic among data owners and restricting them from sharing their data. This work presents a proposed solution that utilizes blockchain technology to tackle the aforementioned challenges. The proposed approach incorporates differential privacy and data compression methods to address privacy leaks and data storage space difficulties. Additionally, it aims to enhance the resistance of machine learning models against privacy attacks. By leveraging the features of blockchain, data stored on the blockchain becomes temper proof and protected against a single point of failure. Simulation results and numerical analysis show that the proposed methodology reduces data storage space requirements and improves privacy preservation.

Prediction of Medical Record Storage Requirement Using Forecasting Technique

Abstract Context: Medical records storage is a valuable and statutory requirement, but for any hospital, it is always most challenging. Many studies on the quality of medical records have been available but very few regarding storage space. Aims: This study uses a forecasting technique to measure the storage space requirement for medical records. Settings and Design: The study is a record-based study using the moving average forecasting technique in a tertiary care 900-bed public hospital in eastern India. Subjects and Methods: The data of the medical record files were collected from 2014 to 2022 years and used the moving average forecasting technique to get the forecasting till 2040. Accordingly, average racks and shelves and floor space requirements were calculated based on forecasted data. Statistical Analysis Used: Simple statistics and moving average forecasting technique was used using Microsoft Excel. Results: The forecasted number of medical record generation data was calculated using moving average forecasting techniques till 2040. From 200 racks and 1168 shelves, it was calculated to be an average of 113 ± 35 records per shelve, 678 ± 35 records per rack, and 3.75 square feet of space per rack is required. Accordingly, cumulative records, eligible for destruction after the retention period, effective cumulative records, racks, and floor space required were calculated. Conclusions: Proper storage of medical records is essential for optimal patient care and medical legal aspects but is a challenge in most organizations. The study findings conclude the forecasted space requirement for medical records and choose a strategic plan accordingly for efficient management of the medical records.

Digitization, and Coding, with Optimizing, of Iraqi Personnel Home Addresses forward, minimizing Storage Space, and Processing time

Individual Home Address is considered as an important sub set of attributes, and never find personnel record, whether a person is student, patient, or employee, etc. without having a home address. It is required to be recorded, registered, and documented in all personnel information systems, whether the system is manual or computerized. This research concern on the Coding, and Digitization of home address of Iraqi personnel in order to enhancing storage efficiency and reduce processing time. The conventional method of maintaining paper-based records for personnel home addresses has proven to be costly, and timeconsuming, and in most cases inefficient. The proposed solution involves the conversion of physical addresses into digital formats, allowing for streamlined data storage and faster retrieval, and update processes. The work starting by indicate the current challenges related to the paper-based address systems, including the demands on physical storage space and the delays incurred during information retrieval. Subsequently, it explores the potential benefits of digitizing home addresses, such as reduced storage requirements, improved data accessibility, and enhanced overall organizational efficiency. To implement this digitization process, the research investigates various technological solutions, including geographic information systems (GIS), databases, and data management protocols. The study also addresses potential concerns related to data security and privacy, proposing measures to safeguard sensitive information. The proposed solution for Digitization, Coding, with Optimizing the Personnel Home Address provide at least 85% of Optimizing Factor for each individual personnel record required storage space and processing time, this solution subsequently leed to better daily decision-making process in most business organizations.