Format Conversion Research Articles

Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling, replacement of missing values, and normalization. Besides, four ML models such as Logistic Regression (LR), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) are used for classification. In addition to the above, RF, LR, KNN and SVM classifiers are integrated to perform the final classification in which a voting classifier is also used. In order to investigate the proficiency of the proposed model, the authors conducted extensive set of simulations and the results were examined under distinct aspects. Particularly, the ensemble model has outperformed the classical ML models with a precision of 94%, recall of 94%, accuracy of 94.24%, and F-score of 94%.

Zero-watermarking Algorithm for Audio and Video Matching Verification

<abstract> <p>For the needs of tamper-proof detection and copyright identification of audio and video matching, this paper proposes a zero-watermark algorithm that can be used for audio and video matching verification. The algorithm segments audio and video in smaller time units, generates a video frame feature matrix based on NSCT, DCT, and SVD, and generates a sound watermark based on methods such as DWT and K-means. The zero watermark combines video, audio and copyright information. The experimental results show that the zero watermark generated by this algorithm can not only realize highly accurate matching detection and positioning of audio and video, but also well resist common single attack and combination attacks such as noise, scaling, rotation, frame attack and format conversion, which has good robustness.</p> </abstract>

Establishment and Application of Stochastic Mesoscopic Concrete Model

Concrete is a kind of complex multiphase composite material, and the spatial distribution of each component of concrete is random, which has an impact on its physical and mechanical properties. This paper presents algorithms for generating two‐dimensional (2D) and three‐dimensional (3D) arbitrarily graded stochastic concrete aggregate models, graphical format conversion methods, and finite element applications. Aggregate shapes include circular, ellipsoidal, convex polygon, ellipsoidal, and spherical. These models take into account the randomness of appearance, position distribution, and particle size distribution, including the interface transition zone, and the aggregate content can reach a high level. An efficient interference algorithm of ellipsoidal aggregate is proposed, which can reasonably control the thickness of the mortar layer around the aggregate. The corresponding program is compiled by MATLAB to realize the generation of random mesoscopic concrete aggregate geometric model. A graphic format conversion method is provided, which greatly extends the applicability and generality of the random aggregation model. The usefulness of the method in this paper was verified by the mechanical analysis of random aggregate model specimens with different volume fractions and the chloride transport properties of different aggregate shape specimens. In addition, the results of the two cases show that the random aggregate geometry and aggregate content have a great impact on the physical and mechanical properties of concrete, and the randomness of the spatial distribution of aggregates makes the chloride ion transport path and the elastic modulus and Poisson’s ratio of concrete be random. The results of this paper can provide an important research foundation for the study of mesoscopic physical and mechanical properties, gradation theory, and durability of concrete.

Bending the Curve of HD Maps Production for Autonomous Vehicle Applications in Taiwan

Mapping technologies have improved over time, and autonomous driving techniques have advanced substantially over recent decades. High-definition (HD) maps are key for autonomous driving because of their accurate and rich interpretations of road scenes. HD maps provide information about road features, such as lane lines, centerlines, traffic signs, and traffic lights, to help autonomous vehicles navigate safely. HD maps have three major challenges: the standardization of the format of HD maps, conversion between map formats, and lack of techniques for automated HD map generation. These issues influence the costs of HD maps. Therefore, this study proposes strategies to overcome these challenges as well as control the cost with the support of the Ministry of the Interior in Taiwan. We established relevant HD map standards and guidelines to standardize the HD map production procedure. Additionally, we contribute to developing semi-automated HD map production tool to enhance the efficiency of HD map production. Another contribution is to develop HD map format conversion tool to satisfy the map requirement for different end-user. This project not only promotes the development of the Taiwanese autonomous driving industry but also increases its international competitiveness.

Means and Methods of Efficiency Estimation of Video Stream Transmission Based on GigE Vision Technology Using Application Processor

The paper investigates the possibility of efficient implementation of a GigE Vision compatible video stream source on a computing platform based on a system-on-a-chip with general-purpose ARM processor cores. In particular, to implement the aforementioned video source, a proprietary prototype of a GigE Vision compatible camera was developed based on the Raspberry Pi 4 single-board computer. This computing platform was chosen due to its widespread use and wide community support. The software part of the camera is implemented using the Video4Linux and Aravis libraries. The first library is used for the primary image capturing from a video sensor connected to a single board computer. The second library is intended for forming and transmission of video stream frames compatible with GigE Vision technology over the network. To estimate the delays in the transmission of a video stream over an Ethernet channel, a methodology based on the Precise Time Protocol (PTP) has been proposed and applied. During the experiments, it was found that the software implementation of a GigE Vision compatible camera on single-board computers with general-purpose processor cores is quite promising. Without additional optimization, such an implementation can be successfully used to transmit small frames (with a resolution of up to 640 × 480 pixels), giving a delay less than 10 ms. At the same time, some additional optimizations may be required to transmit larger frames. Namely, a MTU (maximum transmission unit) size value plays the crucial role in latency formation. Thus, to implement a faster camera, it is necessary to select a platform that supports the largest possible MTU (unfortunately, it turned out that it is not possible with Raspberry Pi 4, as it supports relatively small MTU size of up to 2000 bytes). In addition, the image format conversion procedure can noticeably affect the delay. Therefore, it is highly desirable to avoid any frame processing on the transmitter side and, if it is possible, to broadcast raw images. If the conversion of the frame format is necessary, the platform should be chosen so that there are free computing cores on it, which will permit to distribute all necessary frame conversions between these cores using parallelization techniques.

Investigation of modulation format conversion for optical channel deaggregation in elastic optical network

Our research investigates an optical channel deaggregation technique based on modulation format conversion that may be used in elastic optical networks (EONs) to achieve high spectral efficiency, flexibility, and high data rate. The modulation format conversion technique from 8-ary phase shift keying (8-PSK) to two simultaneous quadrature phase shift keying (QPSK) at 50 Gb / s has been simulated and investigated. 8-PSK and the converted QPSK signals are investigated and compared in terms of bit error rate (BER) and quality factor (Q-factor), and it is demonstrated that they substantially reduce the complexity of transmission and reception process, at high data rates in EON. Additionally, this approach is further exploited to tailor a protection scheme for network survivability. Channel deaggregation, which divides the channel into working and protection paths, is used to implement a dedicated path protection scheme in EON. To evaluate the effect of various network conditions such as traffic load, modulation format, and BER on EON survivability, network simulations are performed on the ARPANET network.

NP-MRD: the Natural Products Magnetic Resonance Database.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems

Recently, studies applying deep learning technology to recognize the machining feature of three-dimensional (3D) computer-aided design (CAD) models are increasing. Since the direct utilization of boundary representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.

The Application of Order Scheduling Algorithm for Food Delivery Logistics Based on Historical Data

With the improvement of China’s social and economic development, the requirements for delivery efficiency of foreign logistics orders are becoming more and more stringent. Big data technology is the current direction of vigorous development. With the development of the Chinese era, in order to better promote the development of China’s urbanization. With the continuous innovation of big data technology, various industries are undergoing industrial upgrading and format conversion. This article mainly discusses the order scheduling algorithm of takeaway logistics based on historical data. Through in-depth research and analysis of the algorithm, the researched scheduling algorithm is reasonably applied to the delivery industry of takeaway logistics, and the scheduling algorithm of big data technology is used for accurate calculation. The supply-demand relationship between food delivery and customers in historical data and the optimal path of actual logistics delivery provide algorithmic support for the selection of time and path of food delivery. Making good use of big data technology can not only accurately predict customers’ habits of ordering food, but also play a role in optimizing delivery. With this technology, the automation level of food delivery can be effectively improved, and foreign media can be delivered to customers in a more timely manner. In its hands, it highlights the application value of big data technology and promotes the development of China’s modern logistics industry, making it possible to develop in the direction of intelligence and automation. The experimental results show that the food delivery industry has great convenience and higher efficiency for ordering and transportation of food delivery under the condition of combining big data technology.

FASTAFS: file system virtualisation of random access compressed FASTA files

BackgroundThe FASTA file format, used to store polymeric sequence data, has become a bioinformatics file standard used for decades. The relatively large files require additional files, beyond the scope of the original format, to identify sequences and to provide random access. Multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration.ResultsWe designed a linux based toolkit that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem by using filesystem in userspace. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using bit encodings plus Zstandard (zstd). The toolkit, FASTAFS, can track all its system-wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. The file compression ratios were comparable but not superior to other state of the art archival tools, despite the innovative random access feature implemented in FASTAFS.ConclusionsFASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers format conversion without the need to rewrite code into different programming languages while preserving compatibility.

All optical multi‐functional signal processing scheme for D8PSK and DQPSK format using four‐wave mixing in semiconductor optical amplifiers

AbstractIn this article, a multifunctional optical processing unit for differential eight phase‐shift‐keying (D8PSK) and differential quadrature phase‐shift‐keying (DQPSK) based on four‐wave mixing (FWM) effects in semiconductor optical amplifier (SOA) is proposed. All‐optical wavelength conversion, D8PSK‐to‐DQPSK format conversion and signal encryption of 10‐Gbaud DQPSK and D8PSK optical signals are achieved simultaneously by simulation. Detailed theoretical analysis and simulation results are conducted to verify the feasibility of the scheme. The Q factor of the electrical signals and the bit error rate recovered from wavelength and format converted components are approximated to be around 6, and 10−9, which indicates the feasibility of the scheme. For the encrypted signal, the FWM effect of another SOA is utilized to decrypt the encrypted signal, and the encryption function of 10‐Gbaud DQPSK and D8PSK optical signals is also verified.

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

Commutative encryption and watermarking (CEW) is an emerging method that combines encryption technology with digital watermarking technology. It has the dual capability of secure transmission and copyright protection. However, the existing CEW methods for vector maps have good robustness in resisting geometric attacks but poor resistance to vertex attacks (e.g., addition, deletion, etc.). To solve this problem, here we propose a novel invariant-based CEW algorithm for vector maps, which consists of permutation-based encryption scheme and coordinates-based watermarking scheme. In the encryption scheme, the encryption key is generated via the Gaussian distribution method combined with the SHA-512 hash method; then, the double random position permutation strategy is applied to the vector map encryption. In watermarking embedding scheme, the original watermark image is scrambled via logistic chaotic encryption before embedding, and the coordinates of all the vertices are normalized. Then, the scrambled watermark image is embedded into the normalized coordinates. Results show that: proposed method is more robust to conventional attacks (e.g., vertex addition and deletion, reordering and data format conversion) and geometric attacks (e.g., scaling and translation). In addition, compared with the existing CEW methods for vector maps, the proposed method has higher security and stronger robustness against vertex attacks.

FluidSMR: Adaptive Management for Hybrid SMR Drives

Hybrid Shingled Magnetic Recording (H-SMR) drives are the most recently developed SMR drives, which allow dynamic conversion of the recording format between Conventional Magnetic Recording (CMR) and SMR on a single disk drive. We identify the unique opportunities of H-SMR drives to manage the tradeoffs between performance and capacity, including the possibility of adjusting the SMR area capacity based on storage usage and the flexibility of dynamic data swapping between the CMR area and SMR area. We design and implement FluidSMR, an adaptive management scheme for hybrid SMR Drives, to fully utilize H-SMR drives under different workloads and capacity usages. FluidSMR has a two-phase allocation scheme to support a growing usage of the H-SMR drive. The scheme can intelligently determine the sizes of the CMR and the SMR space in an H-SMR drive based on the dynamic changing of workloads. Moreover, FluidSMR uses a cache in the CMR region, managed by a proposed loop-back log policy, to reduce the overhead of updates to the SMR region. Evaluations using enterprise traces demonstrate that FluidSMR outperforms baseline schemes in various workloads by decreasing the average I/O latency and effectively reducing/controlling the performance impact of the format conversion between CMR and SMR.

Medical Imaging Diagnosis of Anterior Cruciate Ligament Injury Based on Intelligent Finite-Element Algorithm.

A medical imaging method based on an intelligent finite-element algorithm was proposed to diagnose anterior cruciate ligament injury modeling better. CT three-dimensional finite-element modeling was used to predict the fixation points of the anterior cruciate ligament (ACL) femoral tunnel. In this study, 19 subjects were selected, including 11 males and 8 females. There were seven cases of the left knee and 12 cases of the right knee; all patients had sports injuries. The anatomical structure of a patient's knee was transformed into a three-dimensional model using finite-element analysis software for segmentation. The models of the tibial plateau and lateral femoral condyle were retained. The results showed that the Lysholm score difference (D) between 6 months after surgery and 1 day before surgery was used as the dependent variable in the three-dimensional finite-element model of knee joint established by the software. Pearson's correlation analysis was performed, and the difference (P < 0.05) was statistically significant. The original image of the Dicom format obtained through CT scan is preprocessed in Mimics without any format conversion, which avoids the loss of information, saves more time, and reduces the workload. The definition of “threshold” is used to complete the extraction of bone contour and realize automation. The speed and accuracy of modeling are improved.

Exploration of the efficacy of radiomics applied to left ventricular tomograms obtained from D-SPECT MPI for the auxiliary diagnosis of myocardial ischemia in CAD.

To explore the feasibility and efficacy of radiomics with left ventricular tomograms obtained from D-SPECT myocardial perfusion imaging (MPI) for auxiliary diagnosis of myocardial ischemia in coronary artery disease (CAD). The images of 103 patients with CAD myocardial ischemia between September 2020 and April 2021 were retrospectively selected. After information desensitization processing, format conversion, annotation using the Labelme tool on an open-source platform, lesion classification, and establishment of a database, the images were cropped for analysis. The ResNet18 model was used to automate two steps (classification and segmentation) with five randomization, training and validation steps. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, Youden's index, agreement rate, and kappa value were calculated as evaluation indexes of the classification results for each training-validation step; then, receiver operating characteristics (ROC) curves were drawn, and the areas under the curve (AUCs) were calculated. The Dice coefficient, intersection over union, and Hausdorff distance were calculated as evaluation indexes of the segmentation results for each training-validation step; then, the predicted images were exported. Under the existing conditions, the radiomics model used in this study had an AUC above 0.95 in identifying the presence or absence of myocardial ischemia; in the prediction of the extent of myocardial ischemia, its evaluation index distribution is also close to that of the gold standard. Radiomics can be feasibly applied to left ventricular tomograms obtained from D-SPECT MPI for auxiliary diagnosis. With more in-depth research and the development of technology, adding this method to the existing auxiliary diagnosis will likely further improve the diagnostic accuracy and efficiency, and patients will therefore benefit.

Taxonomy Compilation &amp;amp; Curation Within R

Research projects in ecology or biodiversity either start with an area of study or a target species list. Working with these species lists or taxonomic lists is not as straightforward as it seems. The taxonomic names that are considered to be “standard,” are surprisingly dynamic. Over time, the names keep changing with ongoing research and advancements in taxonomy. Additionally, they undergo all sorts of reorganization, such as one species being split into multiple species and/or subspecies, the grouping of multiple species into a single species, and the reclassification of species from one genus to another. Compiling a consistent target species list can be very time consuming and tricky. However it is the initial step in most research projects and needs to be completed in order to continue the research. Advancements in biodiversity informatics are helping simplify and automate some of these tasks. There are several web services that provide taxonomic data with either a taxonomic or a geographic focus. An increasing number of experts are opening access to their carefully curated taxonomic lists. Even with the help of these services, a lot of time needs to be spent to create a working list of names that can be linked to data such as Global Biodiversity Information Facility (GBIF) mediated occurrence data. The package “taxotools” (Barve 2021) provides basic taxonomic list processing functions within the R programming environment (R Core Team 2021). Even though it is a work in progress, the functions available so far are applicable to diverse projects. The tools available can be categorized into the following broad areas: Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) &amp;amp; GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) &amp;amp; GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Detailed function listings under each category are listed in Table 1. This package has been effectively used in several biodiversity studies and projects like Map of Life, ButterflyNet, Terrestrial Parasite Tracker etc. It has been successfully tested on a masterlist constructed with ~1M names from World Flora Online and performs well. The package is available on The Comprehensive R Archive Network (CRAN) [https://CRAN.R-project.org/package=taxotools] and the developmental release is on GitHub [https://github.com/vijaybarve/taxotools].

Low latency high throughput Montgomery modular multiplier for RSA cryptosystem

With the advancements of communication technology, security threats are also increasing. RSA is a robust cryptosystem to protect classified information. The efficiency of RSA cryptosystem depends on the efficient execution of Montgomery modular multiplication. A low latency and throughput efficient Montgomery modular multiplier is proposed in this paper. The input and final output of this multiplier are binary, but intermediate input and output are carry-saved i.e. sum and carry bits are stored in separate registers. Sum and carry bits are obtained from the addition of operands using carry-save adder (CSA). Montgomery multipliers require quotient calculation for subsequent iteration. The proposed multiplier computes two subsequent quotients in parallel with carry-save addition. Output of carry-save addition is right shifted by two bits to obtain the next-to-next intermediate output. This calculation also requires integer multiples of the inputs. Computation of next output is skipped. Format conversion of output is performed using a carry look-ahead unit (CLU). CLU along with one cycle of carry save addition is also utilized for pre-computation of integer multiples of inputs. The proposed multiplier is implemented on NEXYS4DDR and VIRTEX VII FPGA and the following result is obtained. NEXYS4DDR: Area (LUT + REG): 38903, Delay: 5.84 ns, Cycles: 1158, Latency: 6.76 µs, Throughput: 302.96 Mbps. VIRTEX VII: Area (LUT): 16447, Delay: 1.62 ns, Cycles: 1158, Latency: 1.88 µs, Throughput: 1089.4 Mbps. Results verify the improved area, latency and throughput performance of the proposed work.

Accuracy assessment and performance analysis of raster to vector conversions on LULC data – India

Purpose The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors. Design/methodology/approach The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation. Findings The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested. Research limitations/implications Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category. Practical implications Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed. Originality/value This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.

Evaluation of 360° Image Projection Formats; Comparing Format Conversion Distortion Using Objective Quality Metrics.

Currently available 360° cameras normally capture several images covering a scene in all directions around a shooting point. The captured images are spherical in nature and are mapped to a two-dimensional plane using various projection methods. Many projection formats have been proposed for 360° videos. However, standards for a quality assessment of 360° images are limited. In this paper, various projection formats are compared to explore the problem of distortion caused by a mapping operation, which has been a considerable challenge in recent approaches. The performances of various projection formats, including equi-rectangular, equal-area, cylindrical, cube-map, and their modified versions, are evaluated based on the conversion causing the least amount of distortion when the format is changed. The evaluation is conducted using sample images selected based on several attributes that determine the perceptual image quality. The evaluation results based on the objective quality metrics have proved that the hybrid equi-angular cube-map format is the most appropriate solution as a common format in 360° image services for where format conversions are frequently demanded. This study presents findings ranking these formats that are useful for identifying the best image format for a future standard.

SlicerMorph: An open and extensible platform to retrieve, visualize and analyse 3D morphology

Abstract Large‐scale digitization projects such as #ScanAllFishes and oVert are generating high‐resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyse them. Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, and acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research. We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python‐scriptable open‐source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open‐access 3D models or import users own 3D volumes, to annotate 3D curve and patch‐based landmarks, generate landmark templates, conduct geometric morphometric analyses of 3D organismal form using both landmark‐driven and landmark‐free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology. Our goal is to establish a community of organismal biologists centred around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.