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Abstract Given today's higher demand for online transmission of radiographic images, clinicians and regulatory agencies should be given the evidence they need to guide them in choosing the best image file format to be adopted. To this end, the present scoping review aims to explore, map, and evaluate the literature, with the object of reporting the influence of image file formats on dental diagnostic tasks by assessing intraoral radiographic images. This scoping review complies with PRISMA-ScR. It was customized to assess the risk of bias of the included studies, and was registered on the Open Science Framework platform. The data extraction protocol was developed based on the PCC acronym. An electronic search was conducted in six databases (Pubmed, Web of Science, Scopus, Embase, Lilacs, Cochrane) in December 2023. Original articles were screened, having observational, diagnostic accuracy, and consisting of in vivo or ex vivo laboratory studies investigating the influence of file formats on different diagnostic tasks in dentistry. Eighteen studies, published between the years 1996 and 2022, were included. The following data were extracted from the selected articles: article title, authors' citation, publication date, country, diagnostic task, image file formats tested, compression level, and main conclusion. The most widely investigated diagnostic task was caries lesions (n = 10), led by root resorptions (n = 3), root fractures (n = 2), periapical lesions (n = 2), and periodontal disease (n = 1). The most commonly used radiographic techniques were periapical (n = 12) and bitewing (n = 6). The most frequently investigated image file formats were JPEG (all studies) and TIFF (n = 10 studies). BMP, PNG, and JPEG2000 were also included in 7, 3 and 3 studies, respectively. No studies included the DICOM file format. In regard to the subjective assessment of the several dental diagnostic tasks, the studies mostly showed that the influence of the file formats was not significant (n = 10/55.5%). As for the quality assessment of the included papers, more than 70% of the studies featured a low risk of bias. Current evidence on image file formats and dental radiographic diagnosis is reliable. Any image file format can be used without impairing diagnostic accuracy.
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This paper presents a technique, based on the matrix pencil method (MPM), for the compression of underwater acoustic signals produced by boat engines. The compressed signal, represented by its complex resonance expansion, is intended to be sent over a low-bit-rate wireless communication channel. We demonstrate that the method can provide data compression greater than 60%, ensuring a correlation greater than 93% between the reconstructed and the original signal, at a sampling frequency of 2.2 kHz. Once the signal was reconstituted, a localization process was carried out with the time reversal method (TR) using information from four different sensors in a simulation environment. This process sought to achieve the identification of the position of the ship using only passive sensors, considering two different sensor arrangements.
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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.
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This research proposes a high-performance algorithm for the compression rate of electrical power quality signals, using wavelet transformation. To manage the massive amount of data the telecommunications networks are constantly acquiring it is necessary to study techniques for data compression, which will save bandwidth and reduce costs extensively by avoiding having massive data storage facilities. First biorthogonal wavelet level six transform is applied, however after compression, the reconstructed signal will have a different amplitude and it will be shifted when compared to the original one. Then, normalization is used (for amplitude correction between the original signal and reconstructed one) by multiplying the reconstructed signal by the result of the division between the original signal maximum magnitude and the reconstructed signal maximum magnitude. Thirdly, the ripple in the reconstructed signal is eliminated by applying a moving average filter. Finally, the shifting is corrected by finding the difference between the maximum points in a cycle of the original signal and the reconstructed one. After the compression algorithm was performed the best rates are 99.803% for compression rate, RTE 99.9479%, NMSE 0.000434, and Cross-Correlation 0.999925. Finally, this works presents two new performance criteria, compression time and recovery time, both of them in a real scenario will determinate how fast the algorithm can perform.
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OBJECTIVES: To evaluate the influence of different image file formats of digital radiographic images on the diagnosis of external (ERR) and internal root resorption (IRR). MATERIALS AND METHODS: Thirty-four human teeth were selected. For ERR, 20 teeth were used (10 control and 10 with simulated ERR), and for IRR, 14 teeth were used (before and after IRR simulation). Digital periapical radiographs were acquired using the Digora Toto system and exported in four different image file formats: TIFF, BMP, PNG, and JPEG, totaling 192 radiographs. Five examiners evaluated the images using the JPEGView software and scored the detection of ERR or IRR on a 5-point scale. Sensitivity, specificity, accuracy, and the area under the ROC curve were obtained for the diagnosis of ERR and IRR in the different image file formats. Two-way ANOVA compared the diagnostic values between the file formats and the Kappa test assessed intra- and inter-examiner agreement. The significance level was set at 5% (α = 0.05). RESULTS: The mean values of intra-examiner agreement were substantial (0.740) for ERR and almost perfect (0.836) for IRR and, inter-examiner was fair (0.263) and moderate (0.421), respectively. No statistically significant differences were found among the different file formats for the diagnostic values of ERR and IRR. CONCLUSION: The file format of digital radiographs does not influence the diagnosis of ERR and IRR. CLINICAL RELEVANCE: Digital radiographic images may be susceptible to computational factors; however, they can be stored in multiple file formats without affecting the diagnosis of dental root resorptions.
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Reabsorção da Raiz , Humanos , Radiografia Dentária Digital , Reabsorção da Raiz/diagnóstico por imagemRESUMO
The traditional way to enhance signal-to-noise ratio (SNR) of nuclear magnetic resonance (NMR) signals is to increase the number of scans. However, this procedure increases the measuring time that can be prohibitive for some applications. Therefore, we have tested the use of several post-acquisition digital filters to enhance SNR up to one order of magnitude in time domain NMR (TD-NMR) relaxation measurements. The procedures were studied using continuous wave free precession (CWFP-T1 ) signals, acquired with very low flip angles that contain six times more noise than the Carr-Purcell-Meiboom-Gill (CPMG) signal of the same sample and experimental time. Linear (LI) and logarithmic (LO) data compression, low-pass infinity impulse response (LP), Savitzky-Golay (SG), and wavelet transform (WA) post-acquisition filters enhanced the SNR of the CWFP-T1 signals by at least six times. The best filters were LO, SG, and WA that have high enhancement in SNR without significant distortions in the ILT relaxation distribution data. Therefore, it was demonstrated that these post-acquisition digital filters could be a useful way to denoise CWFP-T1 , as well as CPMG noisy signals, and consequently reducing the experimental time. It was also demonstrated that filtered CWFP-T1 method has the potential to be a rapid and nondestructive method to measure fat content in beef and certainly in other meat samples.
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ABSTRACT The digital data stored in the cloud requires much space due to copy of the same data. It can be reduced by dedupilcation, eliminating the copy of the repeated data in the cloud provided services. Identifying common checkoff data both files storing them only once. Deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication has many security problems so more than one encryption is required to authenticate data. We have developed a solution that provides both data security and space efficiency in server storage and distributed content checksum storage systems. Here we adopt a method called interactive Message-Locked Encryption with Convergent Encryption (iMLEwCE). In this iMLEwCE the data is encrypted firstly then the cipher text is again encrypted. Block-level deduplication is used to reduce the storage space. Encryption keys are generated in a consistent configuration of data dependency from the chunk data. The identical chunks will always encrypt to the same cipher text. The keys configuration cannot be deduced by the hacker from the encrypted chunk data. So the information is protected from cloud server. This paper focuses on reducing the storage space and providing security in online cloud deduplication.
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La medicina moderna es una actividad cada vez más compleja, basada en la información proveniente de múltiples fuentes: historias clínicas, dictáfonos e imágenes y vídeos provenientes de múltiples dispositivos. Las imágenes médicas constituyen una de las fuentes de mayor importancia, por cuanto ofrecen un apoyo integral del acto médico: el diagnóstico y el seguimiento. Sin embargo, la cantidad de información generada por los dispositivos de adquisición de imágenes sobrepasa rápidamente la disponibilidad de almacenamiento que tienen los servicios de radiología, lo cual genera costos adicionales en equipos de cómputo con mayor capacidad de almacenamiento. Además, la tendencia actual de desarrollo de aplicaciones en la "nube de cómputo", tiene limitaciones por cuanto, aunque el almacenamiento es virtual y está disponible desde cualquier sitio, la conexión se hace a través de internet. En estos dos casos, el uso óptimo de la información requiere necesariamente de algoritmos de compresión potentes y adaptados a las necesidades de la actividad médica. En este artículo se presenta una revisión de las técnicas de compresión más utilizadas para el almacenamiento de imágenes, así como un análisis crítico de éstas desde el punto de vista de su uso en ambientes clínicos.
Modern medicine is an increasingly complex activity , based on the evidence ; it consists of information from multiple sources : medical record text , sound recordings , images and videos generated by a large number of devices . Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up . However , the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services , generating additional costs in devices with greater storage capacity . Besides , the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet . In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs . In this paper we present a review of compression techniques used for image storage , and a critical analysis of them from the point of view of their use in clinical settings.