Data Acquisition Paradigm Research Articles

Time-Synchronized MRI-Assessment of Respiratory Apparatus Subsystems—A Feasibility Study

The thorax (TH), the thoracic diaphragm (TD), and the abdominal wall (AW) are three sub-systems of the respiratory apparatus whose displacement motion has been well studied with the use of magnetic resonance imaging (MRI). Another sub-system, which has however received less research attention with respect to breathing, is the pelvic floor (PF). In particular, there is no study that has investigated the displacement of all four sub-systems simultaneously. Addressing this issue, it was the purpose of this feasibility study to establish a data acquisition paradigm for time-synchronous quantitative analysis of dynamic MRI data from these four major contributors to respiration and phonation (TH, TD, AW, and PF). Three healthy females were asked to breathe in and out forcefully while being recorded in a 1.5-Tesla whole body MR-scanner. Spanning a sequence of 15.12 seconds, 40 MRI data frames were acquired. Each data frame contained two slices, simultaneously documenting the mid-sagittal (TH, TD, PF) and transversal (AW) planes. The displacement motion of the four anatomical structures of interest was documented using kymographic analysis, resulting in time-varying calibrated structure displacement data. After computing the fundamental frequency of the cyclical breathing motion, the phase offsets of the TH, PF, and AW with respect to the TD were computed. Data analysis revealed three fundamentally different displacement patterns. Total structure displacement was in the range of 0.94 cm (TH) to 4.27cm (TD). Phase delays of up to 90∘ (i.e., a quarter of a breathing cycle) between different structures were found. Motion offsets in the range of -28.30∘ to 14.90∘ were computed for the PF with respect to the TD. The diversity of results in only three investigated participants suggests a variety of possible breathing strategies, warranting further research.

Towards a privacy-preserving smart contract-based data aggregation and quality-driven incentive mechanism for mobile crowdsensing

The crowd's power, combined with the sensing capabilities of smart mobile de-vices, has resulted in the emergence of a revolutionary data acquisition paradigm known as Mobile Crowdsensing. In exchange for rewards, mobile users collect and share location-specific data values. However, most existing crowdsensing systems built on traditional centralized architectures are highly prone to attacks, intrusions, single point of failure, manipulations, and low reliability. Recently, decentralized blockchain technologies are being applied in mobile crowdsensing systems to ensure workers' privacy, data privacy, and the quality of sensed data at a low service fee. By leveraging blockchain technology, this paper inherits the advantages of the public blockchain without the need for any trusted third-parties. We propose a smart contract-based privacy-preserving data aggregation and quality assessment protocol to infer reliable aggregated results and estimate data quality, wherein, we design a fair quality-driven incentive mechanism to distribute rewards based on the data quality. The protocol ensures a secure, cost-optimal, and reliable aggregation and estimation of the sensed data quality on the public blockchain without disclosing the sensed data's and participants' privacy. We adopt Interplanetary File Systems to offset the blockchain's expensive storage costs. Experiments were conducted using real-world datasets which were implemented on a full-stack on-chain and off-chain decentralized application on the Ethereum blockchain. The experimental results demonstrate our design is highly efficient in achieving privacy-preserving data aggregation and significantly reduces on-chain computation costs.

Integrated ANN-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm

Travel time is a major information in support of Transportation System Management and Operations (TSMO). Accurate travel time information extracted from probe data sources, which have been evolved in recently years, has led to the considerable research into travel time prediction modeling on freeways. However, little research has been reported on dealing with predicting signalized arterial travel time. It is mainly because of the natures of interrupted traffic flows along a signalized corridor or dynamic urban network, wherein varying signal timing schemes, crossing traffic, and dynamic route choices make arterial travel time estimations much more challenging than freeways. This paper presents an innovative methodological framework that can be used to integrate the exponential smoothing technique, Artificial Neural Network (ANN) techniques and Bayes algorithms for predicting travel time along a signalized corridor. The data source obtained along the US-27 corridor in the State of Ohio, was used to test two types of the developed models, i.e., hourly instantaneous travel time prediction model and realized (or experienced) route travel time prediction model. The study results indicate that the developed models can effectively capture the travel time patterns by combining an exponential smoothing base profile and an ANN-Bayes-trained dynamic profile. In this way, more sensitive changes can be identified to a variation aroused by non-recurring congestion. The testing results with real-world travel time datasets indicates a good performance of the predicted travel time models. The most errors range from −0.608 to 0.485, which are much lower than the threshold standard deviation if 2.24 and close to 6% of mean travel time value of 9.08 min. Overall, the study results suggest that the proposed methods are capable of capturing traffic patterns even when an incident occurs by comparing with conventional methods.

Spatiotemporal Urban Inference and Prediction in Sparse Mobile CrowdSensing: a Graph Neural Network Approach

Mobile CrowdSensing (MCS) has recently become a promising data acquisition paradigm, which recruits a large number of users to collect data from the target sensing areas. Obviously, with the increase of sensing scale and the decrease of sensing granularity, traditional MCS cannot fully cover the required sensing areas especially the inaccessible areas. As a variant, Sparse MCS can utilize the spatiotemporal correlations in sensing data to infer the whole sensing map only by sensing a few subareas. However, in many real-world scenarios, such as traffic congestion prediction or parking occupancy detection, inferring the current unsensed data may not be the final goal. By comparison, it is more important to get the future information through the sparse sensed data. In this paper, we turn attention from inferring the current unsensed data to predicting the future unknown data and propose an urban inference and prediction framework in Sparse MCS. To deal with the sparse sensed data, we first present a bipartite-graph-based matrix completion algorithm with spatiotemporal constraints to accurately recover the current full map. Then, by exploiting spatiotemporal correlations based on the inferred full map, we present a Graph Convolutional Networks (GCN) with spatiotemporal attention to predict the future maps. Furthermore, we design a spatiotemporal iterative method to repeatedly update the spatiotemporal attentions and constraints, in order to connect the urban inference and prediction to improve the accuracy of the whole framework. Extensive experiments have been conducted on two types of typical urban sensing tasks with four real-world data sets, which verify the effectiveness of our proposed algorithms in improving the inference and prediction accuracy with the sparse sensed data.

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities.

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.

Technology Focus: Digital Data Acquisition (January 2021)

COVID-19 has significantly accelerated the adoption of digital technologies across all industries, and the oil and gas industry has been no exception. Consequently, interest in digital data acquisition, the backbone of all digital transformation work flows, also has increased significantly. This can clearly be seen in the multifold increase in the number of SPE papers on this topic since last year. This feature will continue to focus on technologies to improve data accessibility and data acquisition, as well as entirely new data sources and their applications. The papers chosen this year include real-time remote monitoring of steam traps and corrosion using wireless sensors, enabling faster and easier access to relevant subsurface information through deep learning of unstructured documents, and automation of real-time drilling work flows through digital transformation technologies. While not reflected in these papers, a related emerging technology that has the potential to transform the data acquisition paradigm and that is garnering much attention, however, is edge computing. As the saying goes, if you cannot bring the data to the model, take the model to the data. One of the main difficulties in faster adoption of digital transformation in oil and gas has been access to reliable real-time data that can be converted to real-time decisions. This is the case because of the remote and geographically distributed nature of most oil and gas assets and legacy outdated and piecemeal information-technology (IT) infrastructures, making it difficult to provide models with reliable, standardized data in a timely manner. Edge-computing frameworks eliminate scale and capacity constraints and bypass limitations of current IT infrastructures, truly enabling operationalization of models for real-time decision making. Edge computing, together with machine learning and artificial intelligence, will be the real enablers of digital transformation.

An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring

Thanks to the improvement of technologies such as Internet of Things, bio-sensing and data mining, smart wearable technologies have recently received increasing attention for teenagers’ sport and health monitoring. Despite the powerful data-acquisition ability of the current wearable products on the market, they still suffer performance deficiency in valuable knowledge extraction due to the lack of accurate computational model and in-depth data analysis. Based on this, this paper proposes a machine learning based physical fitness evaluation model oriented to wearable running monitoring for teenagers, in which a variant of the gradient boosting machine (GBM) combined with advanced feature selection and Bayesian hyper-parameter optimization is employed to build a physical fitness evaluation model. To begin with, we design a special experimental paradigm for data acquisition based on a conventional running activity, in which a group of teenagers’ photoplethysmography (PPG) signals in different testing stages are collected by a set of smartbands developed by ourselves. Next, PPG signals are processed in four steps which match with the four modules in the proposed model including signal preprocessing, physiological data estimation, feature engineering and classification modules. Firstly, the signal preprocessing module aims for suppressing noise and removing baseline drift in PPG signals by using a smoothness prior approach (SPA) and a median filter (MF), respectively. Secondly, the physiological data estimation module achieves conversion from PPG signals to physiological data such as heart rate (HR) and blood oxygen saturation (SpO2). Thirdly, the feature engineering module extracts from the physiological data a group of key features closely related to physical fitness statuses, and then implements a novel advanced feature selection scheme by using Pearson correlation and importance score ranking based sequential forward search (PC-ISR-SFS). Fourthly, the classification module utilizes an extreme gradient boosting (XGBoost) algorithm for classification of each teenager's physical fitness level, in which hyper-parameters are adaptively tuned with Bayesian optimization. Experimental results demonstrate that not only does the proposed model achieve higher evaluation accuracy than the existing reference models, but it also provides a promising solution to future physical fitness evaluation for teenagers through a machine-learning-model based intelligent computing instead of traditional empirical-model based manual calculation.

Graph Algorithms for Topology Identification Using Power Grid Probing

To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load nonstationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world applications, a signal of interest is typically sparse not in the canonical basis but in a certain transform domain, such as wavelets or the finite difference. The theory of compressed sensing was extended to the analysis sparsity model, but known extensions are limited to the specific choices of sensing matrix and sparsifying transform. In this paper, we propose a unified theory for robust recovery of sparse signals in a general transform domain by convex programming. In particular, our results apply to the general acquisition and sparsity models and show how the number of measurements for recovery depends on properties of measurement and sparsifying transforms. Moreover, we also provide extensions of our results to the scenarios where the atoms in the transform have varying incoherence parameters and the unknown signal exhibits a structured sparsity pattern. In particular, for the partial Fourier recovery of sparse signals over a circulant transform, our main results suggest a uniformly random sampling. Numerical results demonstrate that the variable density random sampling by our main results provides a superior recovery performance over the known sampling strategies.

Visible-wavelength iris/periocular imaging and recognition surveillance environments

Visual surveillance cameras have been massively deployed in public urban environments over the recent years, as a crime prevention and law enforcement solution. This fact raised the interest in developing automata to infer useful information from such crowded scenes (from abnormal behavior detection to human identification). In order to cover wide outdoor areas, one interesting possibility is to combine wide-angle and pan–tilt–zoom (PTZ) cameras in a master–slave configuration. The use of fish-eye lenses allows the master camera to maximize the coverage area while the PTZ acts as a foveal sensor, providing high-resolution images of the interest regions. This paper addresses the feasibility of using this type of data acquisition paradigm for imaging iris/periocular data with enough discriminating power to be used for biometric recognition purposes.

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of “cloning” chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant “one MS/MS spectrum - one peptide” paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.

Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications

Compressed sensing (CS) is a powerful new data acquisition paradigm that seeks to accurately reconstruct unknown sparse signals from very few (relative to the target signal dimension) random projections. The specific objective of this study is to save wireless sensor energy by using CS to simultaneously reduce data sampling rates, on-board storage requirements, and communication data payloads. For field-deployed low power wireless sensors that are often operated with limited energy sources, reduced communication translates directly into reduced power consumption and improved operational reliability. In this study, acceleration data from a multi-girder steel-concrete deck composite bridge are processed for the extraction of mode shapes. A wireless sensor node previously designed to perform traditional uniform, Nyquist rate sampling is modified to perform asynchronous, effectively sub-Nyquist rate sampling. The sub-Nyquist data are transmitted off-site to a computational server for reconstruction using the CoSaMP matching pursuit recovery algorithm and further processed for extraction of the structure’s mode shapes. The mode shape metric used for reconstruction quality is the modal assurance criterion (MAC), an indicator of the consistency between CS and traditional Nyquist acquired mode shapes. A comprehensive investigation of modal accuracy from a dense set of acceleration response data reveals that MAC values above 0.90 are obtained for the first four modes of a bridge structure when at least 20% of the original signal is sampled using the CS framework. Reduced data collection, storage and communication requirements are found to lead to substantial reductions in the energy requirements of wireless sensor networks at the expense of modal accuracy. Specifically, total energy reductions of 10–60% can be obtained for a sensor network with 10–100 sensor nodes, respectively. The reduced energy requirements of the CS sensor nodes are shown to directly result in improved battery life and communication reliability.

Implementation and Performance Evaluation of Simultaneous PET/MR Whole-Body Imaging with Continuous Table Motion

In current PET/MR systems, the data acquisition paradigm is based on a multistation examination, imaging the patient from hip to head. This strategy has potential limitations, especially in terms of workflow and PET acquisition efficiency. In this work, the technical implementation of simultaneous PET and MR data acquisition with continuous table motion (CTM) is presented. PET and MR data acquired with CTM are evaluated in terms of image quality with respect to table motion speed. Phantom, volunteer, and patient data were acquired on an integrated whole-body PET/MR system. Phantom experiments were used to systematically quantify image quality parameters including signal-to-noise ratio, geometric distortions, and artifacts in PET and MR scans as a function of different table speeds. Volunteer scans (n = 4) allowed evaluation of CTM MR protocols in a realistic setting, and patient scans (n = 3) were obtained to validate the technique in a clinical workflow. In phantoms, PET image quality, signal-to-noise ratio, geometry, and artifact behavior were found not to be influenced by continuous table motion over the evaluated table motion speeds from 0.8 to 4.6 mm/s. This also holds true for PET patient data where acquisitions were performed with varying table speeds of up to 46 mm/s. For MR, in most scans image quality of CTM scans was found to be comparable to the identical sequence acquired in multistation mode; however, some sequence features (e.g., signal intensity normalization) with impact on MR contrast are currently missing for CTM MR sequences. Data acquisition with continuous table motion in the PET/MR versus multistation acquisition scheme provides data of comparable quality in both PET and MR. This new acquisition paradigm potentially can provide a higher flexibility in simultaneous PET and MR whole-body data acquisition and facilitate examination planning and PET/MR hybrid imaging workflow.

Oblique Pursuits for Compressed Sensing

Compressed sensing is a new data acquisition paradigm enabling universal, simple, and reduced-cost acquisition, by exploiting a sparse signal model. Most notably, recovery of the signal by computationally efficient algorithms is guaranteed for certain randomized acquisition systems. However, there is a discrepancy between the theoretical guarantees and practical applications. In applications, including Fourier imaging in various modalities, the measurements are acquired by inner products with vectors selected randomly (sampled) from a frame. Currently available guarantees are derived using the so-called restricted isometry property (RIP), which has only been shown to hold under ideal assumptions. For example, the sampling from the frame needs to be independent and identically distributed with the uniform distribution, and the frame must be tight. In practice though, one or more of the ideal assumptions are typically violated and none of the RIP-based guarantees applies. Motivated by this discrepancy, we propose two related changes in the existing framework: 1) a generalized RIP called the restricted biorthogonality property (RBOP); and 2) correspondingly modified versions of existing greedy pursuit algorithms, which we call oblique pursuits. Oblique pursuits are guaranteed using the RBOP without requiring ideal assumptions; hence, the guarantees apply to practical acquisition schemes. Numerical results show that oblique pursuits also perform competitively with, or sometimes better than their conventional counterparts.

RBF 커널과 다중 클래스 SVM을 이용한 생리적 반응 기반 감정 인식 기술

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

MDSplus evolution continues

The MDSplus data system has been in operation on several fusion machines since 1991 and it is currently in use at over 30 sites spread over 5 continents. A consequence is the extensive feedback provided by the MDSplus user community for bug fixes and improvements and therefore the evolution of MDSplus is keeping pace with the evolution in data acquisition and management techniques. In particular, the recent evolution of MDSplus has been driven by the change in the paradigm for data acquisition in long lasting plasma discharges, where a sustained data stream is transferred from the acquisition devices into the database. Several new features are currently available or are being implemented in MDSplus. The features already implemented include a comprehensive Object-Oriented interface to the system, the python support for data acquisition devices and the full integration in EPICS. Work is in progress for the integration of multiple protocols and security systems in remote data access, a new high level data view layer and a new version of the jScope tool for online visualization and the optimized visualization of very large signals.

Developing implantable neuroprosthetics: a new model in pig.

A new model has been established in the domestic pig for neural prosthetic device development and testing. To this end, we report on a complete neural prosthetic developmental system using a wireless sensor as the implant, a pig as the animal model, and a novel data acquisition paradigm for actuator control. A new type of stereotactic frame with clinically-inspired fixations pins that place the pig brain in standard surgical plane was developed and tested with success during the implantation of the microsystem. The microsystem implanted was an ultra-low power (12.5 mW) 16-channel intracortical/epicranial device transmitting broadband (40 kS/s) data over a wireless infrared telemetric link. Pigs were implanted and neural data was collected over a period of 5 weeks, clearly showing single unit spiking activity.

Increased Selectivity, Analytical Precision, and Throughput in Targeted Proteomics

Proteomics is gradually complementing large shotgun qualitative studies with hypothesis-driven quantitative experiments. Targeted analyses performed on triple quadrupole instruments in selected reaction monitoring mode are characterized by a high degree of selectivity and low limit of detection; however, the concurrent analysis of multiple analytes occurs at the expense of sensitivity because of reduced dwell time and/or selectivity due to limitation to a few transitions. A new data acquisition paradigm is presented in which selected reaction monitoring is performed in two ways to simultaneously quantify and confirm the identity of the targeted peptides. A first set of primary transitions is continuously monitored during a predetermined elution time window to precisely quantify each peptide. In addition, a set of six to eight transitions is acquired in a data-dependent event, triggered when all the primary transitions exceed a preset threshold. These additional transitions are used to generate composite tandem mass spectra to formally confirm the identity of the targeted peptides. This technique was applied to analyze the tryptic digest of a yeast lysate to demonstrate the performance of the technique. We showed a limit of detection down to tens of attomoles injected and a throughput exceeding 6000 transitions in one 60-min experiment. The technique was integrated into a linear work flow, including experimental design, data acquisition, and data evaluation, enabling large scale proteomic studies.

Experiments With Sensor Motes and Java-DSP

Distributed wireless sensor networks (WSNs) are being proposed for various applications including defense, security, and smart stages. The introduction of hardware wireless sensors in a signal processing education setting can serve as a paradigm for data acquisition, collaborative signal processing, or simply as a platform for obtaining, processing, and analyzing real-life real-time data. In this paper, a software interface that enables the Java-digital signal processing (J-DSP) visual programming environment to communicate in a two-way manner with a wireless sensor network is presented. This interface was developed by writing nesC (an extension to the C programming language for sensors) code that enables J-DSP to issue commands to multiple wireless sensor motes, activate specific transducers, and analyze data using any of the existing J-DSP signal processing functions in real time. A series of exercises were developed and disseminated to provide hardware experiences to signals and systems and signal processing undergraduate students. The hardware with the J-DSP software has been used for two semesters in the senior level digital signal processing (DSP) course at Arizona State University. The interface, the exercises, and their assessment (instruments and results) are described in the paper.

Free-Response Receiver Operating Characteristic Evaluation of Lossy JPEG2000 and Object-based Set Partitioning in Hierarchical Trees Compression of Digitized Mammograms

To assess the effects of two irreversible wavelet-based compression algorithms--Joint Photographic Experts Group (JPEG) 2000 and object-based set partitioning in hierarchical trees (SPIHT)--on the detection of clusters of microcalcifications and masses on digitized mammograms. The use of the images in this retrospective image-collection study was approved by the institutional review board, and patient informed consent was not required. One hundred twelve mammographic images (28 with one or two clusters of microcalcifications, 19 with one mass, 17 with both abnormal findings, and 48 with normal findings) obtained in 60 women who ranged in age from 25 to 79 years were digitized and compressed at 40:1 and 80:1 by using the JPEG2000 and object-based SPIHT methods. Five experienced radiologists were asked to locate and rate clusters of microcalcifications and masses on the original and compressed images in a free-response receiver operating characteristic (FROC) data acquisition paradigm. Observer performance was evaluated with the jackknife FROC method. The mean FROC figures of merit for detecting clusters of microcalcifications, masses, and both radiographic findings on uncompressed images were 0.80, 0.81, and 0.72, respectively. With object-based SPIHT 80:1 compression, the corresponding values were larger than the values for uncompressed images by 0.005, 0.009, and -0.005, respectively. The 95% confidence interval for the differences in figures of merit between compressed and uncompressed images was -0.039, 0.033 for the microcalcification finding; -0.055, 0.034 for the mass finding; and -0.039, 0.030 for both findings. Because each of these confidence intervals includes zero, no significant difference in detection accuracy between uncompressed and object-based SPIHT 80:1 compression was observed at a P value of 5%. The F test of the null hypothesis that all of the modes (uncompressed and four compressed modes) were equivalent yielded the following results: F = 0.255, P = .903 for the microcalcification finding; F = 0.340, P = .848 for the mass finding; and F = 0.122, P = .975 for both findings. To within the accuracy of these measurements, lossy compression of digital mammographic data at 80:1 with JPEG2000 or the object-based SPIHT algorithm can be performed without decreasing the rate of detection of clusters of microcalcifications and masses.