Head Data Research Articles

Data Transmission Direction Based Routing Algorithm for Improving Network Performance of IoT Systems

With the continuous development of wireless communication technology, the Internet of Things (IoT) is being used in a wide range of fields. The IoT collects and exchanges large amounts of data with objects, either tangible or intangible, such as sensors or physical devices, connected to the Internet. Wireless sensor networks (WSNs) are components of IoT systems. WSNs are used in various IoT systems, such as monitoring, tracking, and detection systems, to extract relevant information and deliver it to users. WSNs consist of sensor nodes with low power, low cost, and multiple functions. Because sensor nodes have limited resources, such as power and memory, a reduction in the energy efficiency of the sensor nodes in WSNs will lead to a decrease in wireless network performance and an increase in packet loss, which affects IoT system performance. Therefore, this study aimed to find an energy-efficient routing method that extends the lifetime of WSNs by minimizing the battery use of sensor nodes to improve the network performance of IoT systems. Conserving energy from sensor nodes and increasing network throughput in WSNs involves having protocols. The low-energy adaptive clustering hierarchy (LEACH ) protocol is a well-known hierarchical routing protocol in WSNs that constructs clusters and transmits data. LEACH increases energy efficiency by transmitting data from sensor nodes to the base station (BS) through the cluster head. It is widely adopted in the WSN network field, and many protocols are being studied to improve cluster header selection and data transmission to increase the energy efficiency of sensor nodes. In this study, we attempted to improve energy efficiency by removing unnecessary energy from LEACH. In LEACH, when the sensor node is located between the BS and the cluster head, the sensor node transmits data to the cluster head in the opposite direction of the BS. The data sent to the cluster head are transmitted in the direction of the BS. Thus, transmission in the opposite direction consumes unnecessary energy and affects the WSN performance of IoT systems. In this study, we propose a D-LEACH (direction-based LEACH) protocol based on the received signal strength indicator (RSSI) that improves the efficiency of transmission energy considering the data transmission direction of sensor nodes. D-LEACH aims to balance the energy of the sensor nodes and improve the performance of WSNs in the IoT system by reducing unnecessary energy consumption caused by reverse transmission considering the data transmission direction of the sensor nodes. In the course of the paper, we refer to the routing protocol of WSNs to improve network performance and describe LEACH. We also explain the D-LEACH protocol proposed in this paper and confirm the performance improvement of WSNs in an IoT system through simulation.

Robust video encryption for H.264 compressed bitstream based on cross-coupled chaotic cipher

In order to realize the secure transmission of the H.264 compressed video bitstream, a detailed analysis of the structure for video bitstream is performed. The parameter set and frame header data related to video decoding are retained, and the slice data payload in the network abstraction layer unit is selected as the encryption object. The video format is compliant under the premise of real time and security. The novel cross-coupled chaotic system is designed as the key stream generation component, and the key stream is closely related to the plaintext through the synchronization vector mechanism. When part of the ciphertext stream is lost, the subsequent units can still be decrypted normally, which solves the problem of robustness encryption and decryption for video stream based on chaotic cipher. Detailed performance testing and security analysis data show that the proposed scheme has obvious advantages over existing research in terms of time overhead, compression ratio, anti-chosen-plaintext attack and robustness.

GrassPlot v. 2.00 – first update on the database of multi-scale plant diversity in Palaearctic grasslands

GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). Following a previous Long Database Report (Dengler et al. 2018, Phytocoenologia 48, 331–347), we provide here the first update on content and functionality of GrassPlot. The current version (GrassPlot v. 2.00) contains a total of 190,673 plots of different grain sizes across 28,171 independent plots, with 4,654 nested-plot series including at least four grain sizes. The database has improved its content as well as its functionality, including addition and harmonization of header data (land use, information on nestedness, structure and ecology) and preparation of species composition data. Currently, GrassPlot data are intensively used for broad-scale analyses of different aspects of alpha and beta diversity in grassland ecosystems.

The Google Street View Wi-Fi Scandal and Its Repercussions for Privacy Regulation

Between 2008 and 2010, Google secretly collected Wi-Fi header data from residential and business Wi-Fi access points throughout the world. The collection also included details of personal communications commonly known as ‘payload data’. A number of regulatory investigations ensued from this global privacy scandal. Some privacy authorities, including the Australian Privacy Commissioner, sanctioned Google for the collection of payload data. However, the header data collection was largely overlooked. Those authorities that investigated the collection of Wi-Fi header data concluded that Google breached relevant privacy laws. As a result, some jurisdictions now classify Wi-Fi header data as personal information whereas others do not. The collection of Wi-Fi header data gives rise to complex policy and privacy considerations as this data is an important asset of new Location-Based Services. Consequently, it is important to revisit the Google scandal to investigate whether Google’s collection of Australian Wi-Fi header data breached the Privacy Act 1988 (Cth). Our analysis reveals that Google is likely to have breached the Act, which raises important questions about the regulatory actions conducted in Australia and the effi cacy of the Act’s application in the face of continuing and rapid technological development.

A semi-structured information semantic annotation method for Web pages

There is a large amount of semi-structured information on Web pages. Comprehensive and accurate annotation of Web page information with uniform semantics can enhance the use value of information and provide support for Web site information integration. According to the characteristics of semi-structured information on Web pages, a semantic annotation method based on header recognition and data item classification is proposed. Firstly, a description model is constructed for the domain to be annotated. Secondly, header recognition is used to annotate data items on extracted pages. For those data items fail to be annotated by header recognition, feature vectors are constructed based on the feature sets in the domain description model and semantics of those data items are annotated by the classification results of back-propagation neural network. The proposed method is tested on 19,657 data items in the domain of agricultural product price and 8089 data items in the domain of recruitment information. The annotation precision is 97.39% and 95.67% respectively, and the annotation recall is 95.41% and 95.67%, respectively. These results show that the proposed method can annotate semi-structured information on Web pages accurately and completely.

High-throughput image labeling and quality control for clinical trials using machine learning

<p class="abstract"><strong>Background:</strong> Manually importing and analyzing image data can be time-consuming, prone to human error, and costly for large clinical trial datasets. This can lead to delays in quality control (QC) feedback to imaging sites and in obtaining data analysis results. Herein we describe the creation and application of a high-throughput review process for import, classification, labeling and QC of large multimodal clinical trial image datasets.</p><p class="abstract"><strong>Methods:</strong> Automated methods were used to remove patient identifying information, extract image header data, and filter image data for usability. A convolutional neural net was applied to estimate anatomy for CT images. Internal scores were assigned for each image series to identify the optimal series for labeling and reading of each anatomical region. Image QC reports were automatically generated for all patients.</p><p class="abstract"><strong>Results:</strong> In combined studies for which 204,492 series were received, 27,841 series were identified as usable and 13,415 series were labeled. Using this high-throughput method, total work-hours required per time point were reduced by an approximate factor of ten when compared to traditional review and labeling methods. Our anatomic classification system identified 95.7% of image series correctly, with the remaining series being manually corrected before labeling and analysis.</p><p class="abstract"><strong>Conclusions: </strong>A high-throughput image analysis pipeline was implemented in a large combined dataset of clinical trial image series. This pipeline can be applied across other studies and modalities for fast image data characterization, labeling and QC.</p>

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra™). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra™. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

Compression Header Analyzer Intrusion Detection System (CHA - IDS) for 6LoWPAN Communication Protocol

Prior 6LoWPAN intrusion detection system (IDS) utilized several features to detect various malicious activities. However, these IDS methods only detect specific attack but fails when the attacks are combined. In this paper, we propose an IDS known as compression header analyzer intrusion detection system (CHA-IDS) that analyzes 6LoWPAN compression header data to mitigate the individual and combination routing attacks. CHA-IDS is a multi-agent system framework that capture and manage raw data for data collection, analysis, and system actions. The proposed CHA-IDS utilize best first and greedy stepwise with correlation-based feature selection to determine only significant features needed for the intrusion detection. These features are then tested using six machine learning algorithms to find the best classification method that able to distinguish between an attack and non-attack and then from the best classification method, we devise a rule to be implemented in Tmote Sky. To ensure the reliability of our proposed method, we evaluate the CHA-IDS with three types of combination attacks known as hello flood, sinkhole, and wormhole. We also compare our results in term of accuracy of detection, energy overhead, and memory consumption with the prior 6LoWPAN-IDS implementation such as SVELTE and Pongle’s IDS. The results show that CHA-IDS performs better than the aforementioned methods with 99% true positive rate and consumed low energy overhead and memory that fit in constrained device such Tmote Sky.

Investigation of dental cone-beam CT pixel data and a modified method for conversion to Hounsfield unit (HU)

To investigate the relationship in dental cone-beam CT (CBCT) between the manufacturer-reported image pixel data and a modified conversion to CT number densities in Hounsfield unit (HU). A standardized CT phantom was imaged using typical clinical parameters on CBCT from three manufacturers (Carestream 9300®, Carestream Health, Rochester, NY; J Morita 3D Accutomo®, J. Morita Mfg. Corp., Kyoto, Japan; and Planmeca Promax 3D®, Planmeca Helsinki, OY, Finland). Reconstructed axial slices were evaluated using regions of interest to ascertain the mean pixel value in five materials in the phantom. The Digital Imaging and Communications in Medicine data were also evaluated to determine if raw pixel data had been adjusted during the image reconstruction. A modified version of the existing manual HU conversion technique was applied, and the resultant slope and y-intercept were used to scale the pixel values ultimately to HU for all images. The DICOM header data show that a modified rescale y-intercept was applied to both the Carestream and Planmeca image data yielding manufacturer-produced results in HU. The Morita pixel data were unmodified and report in shades of grey or grey values (GV). The Carestream manufacturer-derived HU measurements showed good correlation in air (-1000 HU), but all other materials ranged from 2.6 to 13.5 σ from the specified phantom value. Results in the modified conversion technique images fell within 1.0-2.4 σ from the specified phantom values. While more studies are needed to test for regularity, this study suggests that our modified technique could be a means of getting more accurate quantitative data from dental CBCTs.

Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging

Patients with lung tumors or inflammatory lung disease could greatly benefit in terms of treatment and follow-up by PET/CT quantitative imaging, namely measurements of metabolic tumor volume (MTV), standardized uptake values (SUVs) and total lesion glycolysis (TLG). The purpose of this study was the development of an unsupervised or partially supervised algorithm using standard image processing tools for measuring MTV, SUV, and TLG from lung PET/CT scans. Automated metabolic lesion volume and metabolic parameter measurements were achieved through a 5 step algorithm: (i) The segmentation of the lung areas on the CT slices, (ii) the registration of the CT segmented lung regions on the PET images to define the anatomical boundaries of the lungs on the functional data, (iii) the segmentation of the regions of interest (ROIs) on the PET images based on adaptive thresholding and clinical criteria, (iv) the estimation of the number of pixels and pixel intensities in the PET slices of the segmented ROIs, (v) the estimation of MTV, SUVs, and TLG from the previous step and DICOM header data. Whole body PET/CT scans of patients with sarcoidosis were used for training and testing the algorithm. Lung area segmentation on the CT slices was better achieved with semi-supervised techniques that reduced false positive detections significantly. Lung segmentation results agreed with the lung volumes published in the literature while the agreement between experts and algorithm in the segmentation of the lesions was around 88%. Segmentation results depended on the image resolution selected for processing. The clinical parameters, SUV (either mean or max or peak) and TLG estimated by the segmented ROIs and DICOM header data provided a way to correlate imaging data to clinical and demographic data. In conclusion, automated MTV, SUV, and TLG measurements offer powerful analysis tools in PET/CT imaging of the lungs. Custom-made algorithms are often a better approach than the manufacturer’s general analysis software at much lower cost. Relatively simple processing techniques could lead to customized, unsupervised or partially supervised methods that can successfully perform the desirable analysis and adapt to the specific disease requirements.

A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification

In recent years, camera identification methods have attracted attention in the field of digital forensics. The existing camera identification methods use features, such as the Exif header data and image noise, that indicate the characteristics of the camera. Of them, photo-response non-uniformity (PRNU) noise contains the unique features of an image sensor and is different for each individual camera. A camera identification method using the PRNU noise should have high identification ability, and a camera identification method using the pairwise magnitude relations of the clustered PRNU noise was previously proposed. In general, identification accuracy is estimated from test data sets, such as the Dresden image database. However, identification accuracy can be evaluated only with respect to the range of images within a database in the conventional evaluation method. A more detailed accuracy evaluation method is required for practical use. Furthermore, studies have not yet reported a false acceptance rate (FAR) evaluation method for the clustered PRNU pair-based camera identification capable of guaranteeing a low FAR (e.g., $\rm {FAR}=10^{-9}$ ). In this paper, we proposed a new pixel clustering method that guarantees An FAR for camera identification using pairs of clustered PRNU noise, and evaluate its FAR based on a probability calculation of a mathematical model. In addition, we investigate the appropriate cluster size by using the Shapiro–Wilk test for an FAR evaluation. We show that it is possible to reliably calculate the FAR of a clustered PRNU noise pair-based camera identification method by using the proposed evaluation method. To demonstrate the validity of our calculations, we compare the actual identification result with the result of the proposed calculation. In this case, we used 16 958 query images from the Dresden image database, which is a benchmark data set. The results of our evaluation indicate that this identification method maintains a false rejection rate of less than 5% (10%) for 5 (8) of the 10 tested cameras even for FAR $ = 10^{-9}$ .

A Novel Framework for Intrusion Detection Using Distributed Collaboration Detection Scheme in Packet Header Data

A Novel Framework for Intrusion Detection Using Distributed Collaboration Detection Scheme in Packet Header Data

INPAC: INdependent PAss Coding algorithm for robust image data transmission through low SNR channels

A new strategy of data representation called INdependent PAss Coding (INPAC) is proposed in this paper for robust and reliable transmission of embedded zero wavelet (EZW) bit stream through noisy channels. Error handling capacity of the EZW is improved by incorporating the proposed INPAC algorithm to EZW algorithm. It suppress the effect of noise occurred in one pass on to another by restricting the data of corrupted pass to mix with the data of uncorrupted pass. For the robust and reliable reconstruction, it is essential to have some of the most important header data without error. This kind of data representation achieves a better quality of reconstruction with a slight increase in overhead of maximum 32 B. To evaluate the modified INPAC–EZW algorithm, the results are compared mathematically and perceptually (visual) with the robust block-based EZW in AWGN channel environment with 4- and 16-QAM modulation techniques and by considering various SNR values. Mathematical parameters peak signal-to-noise ratio (PSNR), compression ratio (CR) and structural similarity index (SSIM) are used to evaluate the performance of the modified algorithm. By considering the aforementioned channel conditions, the average percentage increase in PSNR is observed to be 26.97%, average improvement in CR is observed to be 1.129% and average percentage increase in SSIM is observed to be 12.4%.

MP3 Partial Encryption for DRM

Partial or selective encryption is a well-known concept in multimedia security. It aims to achieve a level of security of multimedia encryption comparable to common encryption by encrypting only a relevant subset of the complete stream or file. The prime benefit of partial encryption is better performance due to fewer encryption operations. In addition, partially encrypted media data can often be parsed as well as unencrypted media if no header data is encrypted. The focus of partial encryption evaluation has almost always been the level of security that can be achieved. In this work, we discuss another aspect: when partial encryption of MP3 files is used in a DRM scenario, how many resources can be saved by it? As DRM usually is attacked by key sniffing or analogue recording, the security of the encryption itself is of lesser importance as long as it provides a sufficient hindrance to access the media data.

Neuro-fuzzy-based clustering of DDoS attack detection in the network

The detection system developed for wired networks cannot be deployed in wireless networks due to the difference between the two types of networks. The data transmission of the wired network is a standard physical routing. However, the data stream routing of wireless network are based on radio signals with a variety of problems of evolution. The attacker's packet header data are acknowledged with a port number, option field parameters and IP address. The anomalies detection is carried out at a regular interval to monitor the traffic analysed through statistical variance. The change detection detects the statistical variance of the traffic volume. The results obtained from the proposed system are compared with the existing attack detection systems with the propagation delay metric. It shows a reduction of nearly 10% and an improvement of 13% average throughput.

The HST/WFC3 Quicklook Project: A User Interface to Hubble Space Telescope Wide Field Camera 3 Data

AbstractThe Hubble Space Telescope’s Wide Field Camera 3 (WFC3) instrument, comprised of two detectors, UVIS (Ultraviolet-Visible) and IR (Infrared), has been acquiring ~ 50-100 images daily since its installation in 2009. The WFC3 Quicklook project provides a means for instrument analysts to store, calibrate, monitor, and interact with these data through the various Quicklook systems: (1) a ~ 175 TB filesystem, which stores the entire WFC3 archive on disk, (2) a MySQL database, which stores image header data, (3) a Python-based automation platform, which currently executes 22 unique calibration/monitoring scripts, (4) a Python-based code library, which provides system functionality such as logging, downloading tools, database connection objects, and filesystem management, and (5) a Python/Flask-based web interface to the Quicklook system. The Quicklook project has enabled large-scale WFC3 analyses and calibrations, such as the monitoring of the health and stability of the WFC3 instrument, the measurement of ~ 20 million WFC3/UVIS Point Spread Functions (PSFs), the creation of WFC3/IR persistence calibration products, and many others.

The Alaska Arctic Vegetation Archive (AVA-AK)

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. We present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis.

Computation of datum shift parameters between two navigation datasets

Geophysical surveys require precise navigation data which is usually acquired through real-time kinematic GPS. The acquired data needs to be transformed from the GPS reference datum to the local datum, by computing datum shift parameters using control points in the survey area. Oil and gas industry archives contain data from seismic surveys, conducted over the years by various data acquisition crews using different datum shift parameters. Sometimes these parameters are not documented in the navigation data headers. This leads to geo-referencing problems when combining data from different surveys. This paper reviews the transformation models and presents a simple approach for computing Molodensky-based datum shift parameters, by identifying common points between two surveys on a map. A real example is included to show the successful implementation of this method.

Security of Multi-frame DICOM Images Using XOR Encryption Approach

Transferring medical images using networks is subjected to a wide variety of security risks. Hence, there is a need of a robust and secure mechanism to exchange medical images over the Internet. The Digital Image and Communication in Medicine (DICOM) standard provides attributes for the header data confidentiality but not for the pixel image data. In this paper, a simple and effective encryption approach for pixel data is provided for multi-frame DICOM medical images. The main goal of the proposed approach is to reduce the encryption and decryption time of these images, using Advanced Encryption Standard (AES) where only one image is encrypted and XOR cipher for encrypting the remaining multi-frame DICOM images. The proposed algorithm is evaluated using computational time, normalized correlation, entropy, Peak-Signal-to-Noise-Ratio (PSNR) and histogram analysis. The results show that the proposed approach can reduce the encryption and decryption time and is able to ensure image confidentiality.

Two-way text messaging: an interactive mobile learning environment in higher education

Short messaging service (SMS) is perhaps the most popular mobile technology prevalent among students in higher education due to its ubiquitous nature and the capability of two-way communication. However, a major limitation in two-way text messaging is sending back a part of received data with the reply message. This limitation results in users of a mobile learning environment being unable to reply back to the correct destination. This article presents a two-way text messaging system that can be integrated into a learning management system (LMS) to provide an interactive learning experience to the user community. Initially, a database is integrated into the LMS that holds message information such as recipient’s phone number, message body and user data header. A specific port associated with the SMS is used to conceal and exchange data of a particular course unit. Subsequently, software in the student’s mobile device captures this message and sends back the reply message to the appropriate course unit allowing both teachers and students to view messages sent and replies received pertaining to a particular course. Results indicate the educational impact of the proposed system in improving the learning environment and benefits it offers to the community in a campus-wide implementation. Keywords: SMS; UDH; PDU; LMS; concealed data (Published: 10 November 2016) Citation: Research in Learning Technology 2016, 24 : 31818 - http://dx.doi.org/10.3402/rlt.v24.31818