Required Storage Space Research Articles

A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

Neural networks (NNs) have been widely adopted in various application domains, ranging from image and video recognition to natural language processing. Recent studies reveal that deeper NNs with more parameters greatly enhance the output accuracy. However, complex NNs incur intensive memory accesses. Since the weights of even a single layer can exceed the on-chip storage capacity, the data usually need to be partitioned. Compression can effectively reduce the storage space requirements. However, there is no research considering the partition of the spare matrix. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Finally, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original; additionally, the throughput of the three networks is increased to an average of 1.66 times.

An improved mask-RCNN algorithm for UAV TIR video stream target detection

In emergency rescue operations, unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) sensors are essential to obtain ground information during nighttime operations. However, existing target detection algorithms mainly consider the detection accuracy but not the processing speed and storage space requirements. Furthermore, current neural network target detection algorithms primarily focus on conventional RGB images and are not optimized for UAV-based TIR video stream data. This paper proposes an improved Mask-RCNN algorithm for target detection in UAV TIR video streams to address current research deficiencies. First, MobileNetV3, which is used to process RGB images, is applied to process TIR data for outdoor emergency rescue operations, significantly increasing the time efficiency of the algorithm. Second, prior knowledge such as the projection model of the airborne camera and the target temperature characteristics in the UAV TIR video stream is utilized to filter the detecting results instead of pre-detection temperature masks. Compared with the original Mask-RCNN algorithm, the improved algorithm increases the processing speed, reduces the storage space requirements, and provides detection performances equal or slightly superior to that before the improvement.

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review.

The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.

A clustering approach for EOS lumping — Using evolutionary-based metaheuristic optimization algorithms

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems.

An Architecture for Distributed Electronic Documents Storage in Decentralized Blockchain B2B Applications

This paper investigates the problem of distributed storage of electronic documents (both metadata and files) in decentralized blockchain-based b2b systems (DApps). The need to reduce the cost of implementing such systems and the insufficient elaboration of the issue of storing big data in DLT are considered. An approach for building such systems is proposed, which allows optimizing the size of the required storage (by using Erasure coding) and simultaneously providing secure data storage in geographically distributed systems of a company, or within a consortium of companies. The novelty of this solution is that we are the first who combine enterprise DLT with distributed file storage, in which the availability of files is controlled. The results of our experiment demonstrate that the speed of the described DApp is comparable to known b2c torrent projects, and subsequently justify the choice of Hyperledger Fabric and Ethereum Enterprise for its use. Obtained test results show that public blockchain networks are not suitable for creating such a b2b system. The proposed system solves the main challenges of distributed data storage by grouping data into clusters and managing them with a load balancer, while preventing data tempering using a blockchain network. The considered DApps storage methodology easily scales horizontally in terms of distributed file storage and can be deployed on cloud computing technologies, while minimizing the required storage space. We compare this approach with known methods of file storage in distributed systems, including central storage, torrents, IPFS, and Storj. The reliability of this approach is calculated and the result is compared to traditional solutions based on full backup.

Deterministic compressed sensing LFM radar for range-Doppler estimation of multiple moving targets

In this paper, delay and Doppler parameters of multiple moving targets are estimated by exploring Compressed Sensing (CS) which enables the reconstruction of a sparse signal from a small set of measurements. No Side lobes appear with CS and hence no false alarms can occur. Also closely spaced targets could be detected with high resolution than traditional Matched Filter (MF). Deterministic DFT measurement matrix is employed, that is multiplied with the received echo signal and down sampled to obtain compressed measurements. For this deterministic sub-sampling is done by choosing first m rows rather than random selection of rows (random sub-sampling), so that matrix can be regenerated during reconstruction without the necessity for storage. Reconstruction algorithm Step Wise regression using False Discovery Rate (SWFDR) stopping rule could effectively optimize noise that leads to reduction in storage space and high rate ADC requirement drastically even in the presence of noise.

Resource Capacity Requirement for Multi-Terminal Cooperation in Container Ports

Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time-shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans-shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans-shipment rate is 85% and transferring rate is 75% for QC intensity; trans-shipment rate is 90% and transferring rate is 60% for storage capacity; and trans-shipment rate is 80% and transferring rate is 75% for gate congestion.

Parallel computing of fuzzy integrals: Performance and test

Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor.

Analysis of Challenges in Modern Network Forensic Framework

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

Symbolic and Numeric Kernel Division for Graphics Processing Unit-Based Finite Element Analysis Assembly of Regular Meshes With Modified Sparse Storage Formats

Abstract This paper presents an efficient strategy to perform the assembly stage of finite element analysis (FEA) on general purpose graphics processing units (GPUs). This strategy involves dividing the assembly task using symbolic and numeric kernels, and thereby reducing the complexity of the standard single-kernel assembly approach. Two sparse storage formats based on the proposed strategy are also developed by modifying the existing sparse storage formats with the intention of removing the degrees-of-freedom-based redundancies in the global matrix. The inherent problem of race condition is resolved through the implementation of coloring and atomics. The proposed strategy is compared with the state-of-the-art GPU-based and central processing unit (CPU)-based assembly techniques. These comparisons reveal a significant number of benefits in terms of reducing storage space requirements and execution time and increasing performance (GFLOPS). Moreover, using the proposed strategy, it is found that the coloring method is more effective compared to the atomics-based method for the existing as well as the modified storage formats.

A lossless compression method for multi-component medical images based on big data mining

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.

Research on Defect Classification and Detection Technology of Image Processing Collected by Computer

The paper uses median filter computer algorithms to detect and classify machine component image defects using image processing, pattern recognition and machine vision theories. In the experiment, automatic extraction of defect images and minimization of defect images are completed to reduce processing volume and storage space requirements, and automatically determine defect categories. The article processes the collected defect images, and the experimental results prove that the method can correctly realize the detection of track surface defects and has certain applicability.

Heterogeneous porous scaffold generation using trivariate B-spline solids and triply periodic minimal surfaces

A porous scaffold is a three-dimensional network structure composed of a large number of pores, and triply periodic minimal surfaces (TPMSs) are one of the conventional tools for designing porous scaffolds. However, discontinuity, incompleteness, and high storage space requirements are the three main shortcomings of porous scaffold design using TPMSs. In this study, we developed an effective method for heterogeneous porous scaffold generation to overcome the abovementioned shortcomings of porous scaffold design. The input of the proposed method is a trivariate B-spline solid with a cubic parametric domain. The proposed method first constructs a threshold distribution field (TDF) in the cubic parametric domain, and then produces a continuous and complete TPMS within it. Finally, by mapping the TPMS in the parametric domain to the trivariate B-spline solid, a continuous and complete porous scaffold is generated. Moreover, we defined a new storage space-saving file format based on the TDF to store porous scaffolds. The experimental results presented in this paper demonstrate the effectiveness and efficiency of the method using a trivariate B-spline solid, as well as the superior space-saving of the proposed storage format.

Accuracy and reliability of linear measurements on digital models derived from CBCT and 3D laser scanned impressions

Dental study models are essential records for orthodontic diagnosis, planning and treatment evaluations. As plaster models are prone to damage and loss, require storage space and inefficient to retrieve and transfer, digital models are been used to eliminate drawbacks of plaster model regarded as potential replacement of traditional plaster models. Aim: To compare the accuracy and reliability of linear measurements on digital models derived from Cone Beam Computed Tomography (CBCT) and three-dimensional (3D) laser scanned impressions with conventional plaster models. Objectives: i. To compare the accuracy and reliability of measurements made on Plaster and CBCT derived digital models. ii. To compare the accuracy and reliability of measurements made on plaster and 3- dimensional laser scanned impression models. iii. To compare the accuracy and reliability of measurements made on CBCT models and 3D laser scanned impression models. Materials and Methods: CBCT and plaster models will be prepared from 12 patients. Rubber base impressions will be scanned using 3D dental laser scanner. Landmarks will be marked and the linear measurements of all the three models will be compared using 3D software. Results: The derived data was subjected to ANOVA test, Post Hoc Tukey test, Intraclass correlation coefficients (ICCs) and Method Error to evaluate the reliability of repeated measurements. No significant difference was found between plaster models, CBCT models and impression scanned laser models. Conclusion: Plaster models, CBCT models and models obtained from laser scan of impressions are three different diagnostic records. Each have their own advantages and disadvantages. The present study shows that models obtained from laser scanned impressions are comparable to plaster models. The accuracy is adequate for initial diagnosis and treatment planning in clinical orthodontics Keywords: Digital models, Cone beam computed tomography, Laser scan, Meshlab, DIC

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

A 5-parameter complexity classification of the two-stage flow shop scheduling problem with job dependent storage requirements

The paper is concerned with the two-machine scheduling problem where each job is to be processed on the first-stage machine and after that on the second-stage machine. In order to be processed, each job requires storage space that it seizes at the start of its processing on the first-stage machine and releases only at the completion of processing on the second-stage machine. The storage space is limited and its consumption varies from job to job. The goal is to minimise the time needed for the completion of all jobs. All instances of the considered scheduling problem are classified by means of five parameters. This leads to the sixty four families of instances. For each family, the paper establishes its computational complexity and, in the case of polynomial-time solvability, presents a polynomial-time algorithm, constructing an optimal schedule.

The Effect of Duration of Use of Depomedroxyprogesterone Acetate on the Thickness of the Vaginal Epithelium of Mice

BACKGROUND: Depomedroxyprogesterone acetate (DMPA) is one of the most effective contraceptive methods currently widely used. Injectable contraceptives, including DMPA, are growing in popularity because of their ease of use, effectiveness and affordability. The use of DMPA also has advantages such as not interfering with sexual relations, does not need male participation, does not require storage space, and is easy to obtain and helps increase body weight.
 AIM: The aim of the study was to analyze the effect of the duration of use of DMPA on the thickness of the vaginal epithelium of mice.
 METHODS: The research design used in this study was true experimental with a post-test only control group design approach. This research was carried out treatment at a dose of 0.39 mg/mouse/day DMPA exposure in mice. The phenomenon observed in this study was the thickness of the vaginal epithelium of mice. This study used mice with the completely randomized design method and was carried out at the Pharmacology Laboratory and the Anatomical Pathology Laboratory, FKH Unsyiah. The collection of data technique was conducted by observation with a light microscope to calculate the average thickness of the vaginal epithelium of mice injected with DMPA in the four groups. The data analysis technique was carried out in five stages, namely, the normality test of the sample data with the Shapiro–Wilk test, the homogeneity test of variance with the Levene test, and comparing the thickness of the vaginal epithelium in each of the two treatment groups using the independent t-test.
 RESULTS: The finding revised that there was a significant difference in the thickness of the vaginal epithelium in the two treatment groups 10 days and 15 days of DMPA exposure which was indicated by a p = 0.003 (p < 0.05). DMPA exposure levels cause thinning of the vaginal epithelial thickness. The average thickness of vaginal epithelium exposure to DMPA for 10 days in the control group was 182536.33 ± 5773.465. In the treatment group, exposure to DMPA for 10 days, the thickness of the vaginal epithelium was 168322.33 ± 7611.300. The mean vaginal epithelial thickness in the 15-day DMPA exposure group in the control group was 177242.83 ± 9689.558. In the 15-day DMPA exposure treatment group, the thickness of the vaginal epithelium was 138674.83 ± 17176.124.
 CONCLUSION: There was a difference in the mean vaginal epithelial thickness between the control group and 10 days of DMPA exposure. There was a difference in the mean vaginal epithelial thickness between the control group and 15 days of DMPA exposure. The longer the exposure to DMPA, the thinner the thickness of the vaginal epithelium.

Luminance approximated vector quantization algorithm to retain better image quality of the decompressed image

Some compressed images using Vector Quantization algorithm suffers from blocking artifacts which degrades the visual appeal of the image. Present study proposes a hybrid vector quantization method applicable on de-correlated color model. As luminance channel carries image information and loss of image information results in degradation of the visual appeal of an image, so aim of this study is focused on retaining more image information during compression process. For luminance channel compression, a new four level quantization based compression method is developed. Luminance channel is partitioned into smaller blocks. Then for each block, four level quantization is applied which local to the current block only. This results many level luminance value effectively for the whole image. It helps to retain better information. Chrominance channels are compressed using conventional Vector Quantization. This hybrid compression method improves visual quality of the decompressed image reasonably compared to VQ. The proposed method is applied on many standard images found in literature and images of UCIDv.2 color image database. Results are analyzed in terms of Peak Signal to Noise Ratio, Structure Similarity Index and space requirement reduction for compressed image using the method. Experimental results show that proposed method retains better quality of image in terms of PSNR and SSIM than Vector Quantization and Modified Vector Quantization. This method reduces storage space requirement for the compressed images in the range of 84% to 89%.

Compensation for the non‐uniformity correction of multi‐channel TDI CCD under variable gain

The compensation for non-uniformity correction of multi-channel time delay integration charge coupled device (TDI CCD) under variable gain is presented. The mathematical model of photoelectric conversion is established first, and, then, a two-step non-uniformity correction method for multi-channel TDI CCD is proposed. Based on this method, a compensation algorithm for non-uniformity correction under variable gain is elaborated in detail. The experimental results show that the proposed algorithm can always maintain the photo response non-uniformity (PRNU) of the corrected images better than 1.14% when the programmable gain amplification (PGA) gain varies within the range of 1–3, compared to the conventional method of degrading to 2.91%. Meanwhile, a compression method for correction coefficients under multiple integral stages is proposed, which can reduce the storage space requirement by 50% at the cost of the PRNU being degraded by a maximum of 0.19% at all integral stages.

SW‐LZMA: Parallel Implementation of LZMA Based on SW26010 Many‐Core Processor

With the development of high‐performance computing and big data applications, the scale of data transmitted, stored, and processed by high‐performance computing cluster systems is increasing explosively. Efficient compression of large‐scale data and reducing the space required for data storage and transmission is one of the keys to improving the performance of high‐performance computing cluster systems. In this paper, we present SW‐LZMA, a parallel design and optimization of LZMA based on the Sunway 26010 heterogeneous many‐core processor. Combined with the characteristics of SW26010 processors, we analyse the storage space requirements, memory access characteristics, and hotspot functions of the LZMA algorithm and implement the thread‐level parallelism of the LZMA algorithm based on Athread interface. Furthermore, we make a fine‐grained layout of LDM address space to achieve DMA double buffer cyclic sliding window algorithm, which optimizes the performance of SW‐LZMA. The experimental results show that compared with the serial baseline implementation of LZMA, the parallel LZMA algorithm obtains a maximum speedup ratio of 4.1 times using the Silesia corpus benchmark, while on the large‐scale data set, speedup is 5.3 times.