Reduce Storage Space Research Articles

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

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.

PathoTracker: an online analytical metagenomic platform for Klebsiella pneumoniae feature identification and outbreak alerting

Clinical metagenomics (CMg) Nanopore sequencing can facilitate infectious disease diagnosis. In China, sub-lineages ST11-KL64 and ST11-KL47 Carbapenem-resistant Klebsiella pneumoniae (CRKP) are widely prevalent. We propose PathoTracker, a specially compiled database and arranged method for strain feature identification in CMg samples and CRKP traceability. A database targeting high-prevalence horizontal gene transfer in CRKP strains and a ST11-only database for distinguishing two sub-lineages in China were created. To make the database user-friendly, facilitate immediate downstream strain feature identification from raw Nanopore metagenomic data, and avoid the need for phylogenetic analysis from scratch, we developed data analysis methods. The methods included pre-performed phylogenetic analysis, gene-isolate-cluster index and multilevel pan-genome database and reduced storage space by 10-fold and random-access memory by 52-fold compared with normal methods. PathoTracker can provide accurate and fast strain-level analysis for CMg data after 1 h Nanopore sequencing, allowing early warning of outbreaks. A user-friendly page (http://PathoTracker.pku.edu.cn/) was developed to facilitate online analysis, including strain-level feature, species identifications and phylogenetic analyses. PathoTracker proposed in this study will aid in the downstream analysis of CMg.

A study on partial pivot ACA boundary element method for elasticity problems

The boundary element method (BEM) employing the partial pivot adaptive cross approximation (PACA) algorithm has been observed to experience convergence failures and reduced solution accuracy when solving elasticity problems, especially at large scales. To address this issue, this paper proposes an improved algorithm for both 2D and 3D elasticity problems.Our investigations revealed that when the normal of an element is parallel to the coordinate axes and the element is in the same plane as the source point, zero entries with regular distributions will appear in the coefficient matrix. This results in the premature satisfaction of the convergence criterion of the PACA algorithm, leading to the omission of some rows and columns of the coefficient matrix. To address this issue, this paper proposes improvements to the PACA algorithm through the Coordinate Rotation Method (CRM) and the Component Traversal Method (CTM), and validates the effectiveness of these two improved methods.Furthermore, we investigate reasons behind the decrease in accuracy when employing the CTM to solve large-scale problems. We attribute this to the oscillatory nature of the estimated relative error and propose the modified PACA (M-PACA) algorithm. M-PACA not only maintains the advantages of the PACA algorithm in reducing storage space and accelerating coefficient matrix generation but also addresses its limitations, ensuring more accurate and reliable solutions for elasticity problems.

Goat Performance of Peanut Goat with Supplementation of Feed Wafers Containing Lamtoro Taramba Leaves at Sumbawa Global Farm

Wafer feed is a form of compacted feed to reduce storage space. The ingredients used consist of lamtoro leaves, bran, molasses which are formulated according to the nutritional needs of the feed. This study aims to determine the best level of feed wafers made from lamtoro leaves given to kacang goats. The experimental design used a completely randomized design (CRD) with 3 treatments 4 replicates P1 = 10% feed containing lamtoro leaves, P2 = 12% feed containing lamtoro leaves, P3 = 14% feed containing lamtoro leaves. The results showed that the production performance of kacang goats had a significant effect (P<0.05) on increasing ADG, feed consumption, feed efficiency for kacang goats. It was concluded that the level of giving lamtoro leaf wafers of 12% was the best level

Physicochemical properties and flow behavior of natural sweetener and colorant from foam‐mat dried grape powder

AbstractThe effects of whey protein concentrate, glycerol monostearate, methyl cellulose at a concentration of 1‐3% and gum arabic on the expansion, density and stability of the foam as well as the physicochemical properties of the powder were investigated. The findings identified that the simultaneous addition of foaming and stabilizer agents noticeably affected the quality of the foam. The most stable foam occurred at 3% WPC and 3% MC with density 0.374, expansion 134.4, and stability 95.6. The dried grape powder with a higher proportion of GMS and WPC exhibited the most brightness. The higher level of foaming agents demonstrated lower moisture content, aw, cohesiveness and higher hygroscopicity, flow ability and wettability compared to the non‐foamed one. The study demonstrated the potential use of grape powder as a hygroscopic ingredient in an instant food product, Furthermore, it highlights the importance of selecting appropriate foaming agents to achieve desired properties in the products.Practical applicationsConsidering today's hectic lifestyles and people's increasing awareness of nutrient consumption, consumers prefer ready‐to‐eat and readily available foods that are also health‐oriented. Foam mat drying is an economical alternative to drum, spray, and freeze drying in the production of fruit powders. This process can be used for large‐scale production, because of its suitability for all types of juices, rapid drying at lower temperature, retention of nutritional quality, easy reconstitution, and cost‐effective for producing easily juice powders. Fruit juice powders obtained through this process have high economic potentials over their liquid counterparts such as reduced volume or weight, reduced storage space, simpler handling and transportation, and much longer shelf life. Thus, by conducting this comparative study, valuable insights can be gained into the potential use of foam‐mat dried grape syrup powder as a desirable natural sweetener, colorant, and hygroscopic ingredient for ready meals.

Optimization and inventory management under stochastic demand using metaheuristic algorithm.

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.

Spectrum Situation Awareness for Space-Air-Ground Integrated Networks Based on Tensor Computing.

The spectrum situation awareness problem in space-air-ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space-air-ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.

Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture.

Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation-all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively.

A contrast sensitivity model of the human visual system in modern conditions for presenting video content.

Digital video incurs many distortions during processing, compression, storage, and transmission, which can reduce perceived video quality. Developing adaptive video transmission methods that provide increased bandwidth and reduced storage space while preserving visual quality requires quality metrics that accurately describe how people perceive distortion. A severe problem for developing new video quality metrics is the limited data on how the early human visual system simultaneously processes spatial and temporal information. The problem is exacerbated by the fact that the few data collected in the middle of the last century do not consider current display equipment and are subject to medical intervention during collection, which does not guarantee a proper description of the conditions under which media content is currently consumed. In this paper, the 27840 thresholds of the visibility of spatio-temporal sinusoidal variations necessary to determine the artefacts that a human perceives were measured by a new method using different spatial sizes and temporal modulation rates. A multidimensional model of human contrast sensitivity in modern conditions of video content presentation is proposed based on new large-scale data obtained during the experiment. We demonstrate that the presented visibility model has a distinct advantage in predicting subjective video quality by testing with video quality metrics and including our and other visibility models against three publicly available video datasets.

Classification of Resampled Pediatric Epilepsy EEG Data Using Artificial Neural Networks with Discrete Fourier Transforms

Epilepsy is a neurological disorder commonly observed in children. Currently, electroencephalography (EEG) is widely used as the most important diagnostic method for epilepsy in medical practice. The diagnosis of epilepsy in pediatric patients is challenging due to their high level of activity and incomplete brain development. In this study, data sampled at 256 Hz were obtained from patients between the ages of 7–12, collected by Boston Children’s Hospital. First, the image intervals that contain seizure waves were identified in the datasets, and the discrete-time Fourier transform (DFT) was applied. The amplitude-frequency features of the frequency spectrum in seizure and nonseizure states were obtained, and patients were classified for seizure detection using a multilayer perceptron (MLP) based on an artificial neural network (ANN) architecture. In the next step, the EEG signals were resampled at low frequencies, and the same analyses were repeated to minimise the disadvantages of limiting factors such as storage space and processing power, resulting in reduced storage space usage and more efficient performance.

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Models

The implementation of joint source-channel coding (JSCC) schemes using deep learning has accelerated the development of semantic communication research. Existing JSCC schemes based on deep learning (DL) are trained on a fixed signal-to-noise ratio (SNR); however, these trained models are not designed for scenarios in which the SNR is dynamic. Therefore, a novel semantic adaptive model for semantic communication—called joint source-channel coding with adaptive models (AMJSCC)—that has a semantic adaptive model selection (SAMS) module is proposed. The joint source-channel encoding (JSCE) model and the joint source-channel decoding (JSCD) model adapt according to both real-time channel conditions and system available computational power resources. Furthermore, residual networks with different layers are investigated to further improve the accuracy of information recovery. Simulation results demonstrate that our model can achieve higher recovery similarity and is more robust and adaptive to the SNR and communication resources. Meanwhile, compared to the state-of-the-art deep JSCC methods, it reduces storage space and communication resource consumption.

Storage Space Reduction of Biometric Iris Databases by Successive Images Differences and Quadtree Decomposition

Storage Space Reduction of Biometric Iris Databases by Successive Images Differences and Quadtree Decomposition

GsNeRF: Fast novel view synthesis of dynamic radiance fields

Synthesis of new views in dynamic 3D scenes is a challenging task in 3D vision. However, most current approaches rely on radiance fields built upon multi-view camera systems for dynamic scenes, which are expensive and time-consuming, and depend on implicit representations based on Neural Radiance Fields . In this study, we introduced a new representation method for dynamic scenes called GsNeRF, which allows for fast reconstruction of objects under motion by moving a single camera in the scene, and it can render high-quality views from arbitrary time frames and camera poses. We utilize five grids to represent the dynamic scene, employing tensor decomposition for each grid to reduce storage space usage. Since the primary task of the entire model is to optimize these planes, we enforce spatio-temporal continuity of these planes through a smoothness loss. GsNeRF is combined with a miniature MLP to regress color outputs and trained using volume rendering. Through testing on a series of synthetic and real datasets, our method reduces the training time by over 100 times compared to implicit methods and achieves better rendering quality compared to explicit methods. Our approach achieves a balance between memory usage, speed, and quality overall.

Similarity Graph-correlation Reconstruction Network for unsupervised cross-modal hashing

Existing cross-modal hash retrieval methods can simultaneously enhance retrieval speed and reduce storage space. However, these methods face a major challenge in determining the similarity metric between two modalities. Specifically, the accuracy of intra-modal and inter-modal similarity measurements is inadequate, and the large gap between modalities leads to semantic bias. In this paper, we propose a Similarity Graph-correlation Reconstruction Network (SGRN) for unsupervised cross-modal hashing. Particularly, the local relation graph rebasing module is used to filter out graph nodes with weak similarity and associate graph nodes with strong similarity, resulting in fine-grained intra-modal similarity relation graphs. The global relation graph reconstruction module is further strengthens cross-modal correlation and implements fine-grained similarity alignment between modalities. In addition, in order to bridge the modal gap, we combine the similarity representation of real-valued and hash features to design the intra-modal and inter-modal training strategies. SGRN conducted extensive experiments on two cross-modal retrieval datasets, and the experimental results effectively validated the superiority of the proposed method and significantly improved the retrieval performance.

Technology improvement obtaining powdered dried honey

The object of the study is the production of powdered dried honey. The limited use of honey in the food industry is due to its physicochemical properties. Viscosity and stickiness create problems during its dosing, mixing, storage and transportation. Honey in powder form has a high commercial potential. The great advantages of using dry honey are the reduction of storage space, ease of processing and dosing. Honey is characterized by organoleptic and physico-chemical parameters affecting the drying process and the quality of the final product. The resulting powdered dry honey, in compliance with the drying modes recommended by us, has retained all its useful biological properties. The water content, as well as the dry matter content in fresh and dried honey met the criteria for the composition of honey. Consequently, reducing the water content during the drying process significantly contributes to increasing the stability of honey during storage. The developed technology provides for reducing the drying temperature to 50 °C, which also has a positive effect on the nutritional value of the final product It is established that the maximum proportion of frozen moisture in the sublimation process is observed at temperatures from minus 30 °C to minus 40 °C, depending on the types of honey, and an increase in the drying temperature above 40 °C shortens the duration of the drying process, but may affect the quality of the product. Powdered honey is in demand in the food, pharmaceutical and cosmetic industries, due to increased dosing accuracy due to the flowability of dried honey. However, it should be borne in mind that dried honey is very hygroscopic due to the presence of sugars and the amorphous state after drying

Lightweight Geometric Compression Encoding for Additive Manufacturing

In additive manufacturing (AM), 3D geometric models often contain some repetitive structures. To represent model topology more effectively and reduce storage space, this paper introduces a data structure using the repetition encoding framework and the hash encoding framework. First, we identify repeated meshes within the model and establish transformation groups. Then, we cluster the identical transformation groups to optimize storage space usage by utilizing the geometric relationships among facets. Finally, efficient data storage is achieved using the hash encoding framework. We devise three compatible encodings for prevalent additive manufacturing file formats based on these frameworks. We compare the encoding process with three commercial software solutions regarding the reconstruction efficiency and the storage space requirement. Experimental results exhibit that our proposed methods reduce file sizes by over 70% while preserving model topology information.

Quantization to speedup approximate nearest neighbor search

The quantization-based approaches not only are the effective methods for solving the problems of approximate nearest neighbor search, but also effectively reduce storage space. However, many quantization-based approaches usually employ fixed nprobes to the search process for each query. This will lead to extra query consumption. Additionally, we observed that as the number of points in each cluster center of product quantization increases, the query cost also increases. To address this issue, we propose an acceleration strategy based on the IVF-HNSW framework to further speed up the query process. This strategy involves introducing an adaptive termination condition for queries and reducing the number of data points accessed by building HNSW results. Through extensive experiments, we have shown that our proposed method significantly accelerates the nearest neighbor search process.

Enabling efficient and secure health data sharing for Healthcare IoT systems

The Healthcare Internet of Things (HIoT) system enables remote monitor patient health state and feedback on medical treatments timely. The success of HIoT relies on how to share health data securely and efficiently. Existing solutions either fail to ensure the security or incur significant overheads. In this paper, we put forward an efficient and secure health data sharing scheme for HIoT systems, named ESDS, to solve this dilemma. In ESDS, we first signcrypt the health data collected by IoT devices to protect patient privacy and ensure data authenticity. Next, we aggregate N signatures for N pieces of different health data into a short signature, which significantly reduces storage space and transmission size, i.e., from O(N) to O(1). Then, we apply local verifiability to verify one piece of health data under the aggregated signature. With this property, a doctor only needs one auxiliary message generated by the patient to check the target message without obtaining whole health data, which improves system verification efficiency. Extensive security analysis and performance evaluation show that ESDS is secure and efficient.

Understanding CO2 mineralization and associated storage space changes in illite using molecular dynamics simulation and experiments

Clay minerals have the potential to capture anthropogenic CO2 emissions permanently and safely. Understanding the kinetics of cation leaching and carbonate formation, as well as changes in clay structure, has resource and environmental implications. However, metadynamics mechanism and relevant structural changes in representative clay minerals exposed to supercritical carbon dioxide (scCO2) are rarely studied in current research. In this work, ReaxFF molecular dynamics simulation in combination with scCO2‒H2O‒illite experiments at 10 MPa and 333 K were carried out to investigate the mechanisms of mineralization and structure alteration under geological conditions. The results show that interlayer K+ cations were leached out due to surface non-bridging oxygen protonation, subsequently bonding with HCO3− and forming K2CO3 molecules at the surface. Upon analyzing the chemical and structural results of experiments, carbonate precipitation and accumulation reduce storage space and modify the composition of illite, but the octahedral and tetrahedral sheets of the illite are structurally stable. The efficiency of mineralization is typically dominated by the exposed surface, where sufficient cations can be provided to enhance interactions at the illite/liquid interface. In comparison to a decrease in plane porosity of 27.3%, the mineralization degree with values between 15.22% and 33.12% is comparable and acceptable. These findings present a non-structural mechanism in clay minerals that might have critical influence on CO2 geo-sequestration in shale gas reservoirs.