nils-herrmann/clean_scirepeval huggingface dataset:
A 2.4 GHz RF CMOS up-conversion mixer for wireless sensor networks nodes

Mixer is one of the main modules of a transmitter system, and its performance will heavily impact the functionality of the entire transmitter. Gilbert cell structure can achieve high degree of RF (Radio Frequency) and LO (Local Oscillator) isolation, thus loosening filtering output requirements. This design employs a resonant LC network as the load to improve gain, increase the output voltage swing, and inhibit the high-order harmonic generation. It uses feedback to improve the linearity of the mixer. The current-reuse bleeding double-balanced mixer operates at 2.4GHz. And the up-conversion mixer uses an intermediate frequency (IF) input frequency of 1.2MHz, a local oscillator (LO) of frequency of 2.4GHz and a RF output frequency of 2.4012GHz. Experimental test chip is realized. Measured results demonstrate −2.01dB conversion gain, and −13dBm input compression point with 11.9mA total current with a 1.8V supply. The current in Gilbert Cell is 2.9mA and the current in buffer is 9mA.
A research on integrated application of RFID-based lean manufacturing

The discrete manufacturing workshops usually suffer from a bottleneck of capturing and collection of real-time field information, which is important for improving reactivity capability of production system, during they organize production adopting JIT (Just-In-Time) mode. As a rapid, real-time, accurate information collection tools, RFID (Radio Frequency Identification) improved JIT production to a great extent. This paper proposes an approach of Kanban management based on RFID, and designs the framework of RFID-based Kanban management information system. A prototype system is finished in detail eventually.
Cooperative sensing in cognitive networks under malicious attack

This paper considers cooperative sensing in cognitive networks under Spectrum Sensing Data Falsification attack (SSDF) in which malicious users can intentionally send false sensing information. One effective method to deal with the SSDF attack is the q-out-of-m scheme, where the sensing decision is based on q sensing reports out of m polled nodes. The major limitation with the q-out-of-m scheme is its high computational complexity due to exhaustive search. In this paper, we prove that for a fixed percentage of malicious users, the detection accuracy increases almost exponentially as the network size increases. Motivated by this observation, as well as the linear relationship between the scheme parameters and the network size, we propose a simple but accurate approach that significantly reduces the complexity of the q-out-of-m scheme. The proposed approach can easily be applied to the large scale networks, which can be much more reliable under malicious attacks.
An SDN_based management framework for IoT devices

The ever evolving technology of the Internet of Things(IoT) requires ubiquitous connectivity to billions of heterogeneous devices such as sensors, cameras, RFID devices, etc. However, due to the heterogeneity of devices and access protocols, IoT networks are becoming enormous and complex, which makes the management extremely difficult. Based on Machine-to-Machine(M2M) technics and the programmability feature of the network, brought by the Software Defined Network(SDN), devices in a network can be treated as objects, thus decoupling the control plane from the data plane. In this paper, we proposed a framework for managing the devices and configuring the network dynamically based on SDN. Finally, we apply the framework in our project and get a successful result.
Estimating the standard deviation from extreme Gaussian values

We derive an estimator for the standard deviation of a Gaussian random variable that uses the maximum of two observations on the random variables. The process is repeated until the variance of the estimator or the degree of confidence reaches a predetermined level. The estimator is unbiased and consistent, and its variance is only marginally larger than the standard square root of the sum of the squares estimator. Moreover the computation of an estimate requires only a sequence of comparisons of two numbers followed by an addition.
Physical model of image formation in multiple-image radiography

We have recently proposed a new X-ray imaging method, called multiple-image radiography (MIR), which simultaneously produces images of absorption, refraction, and ultra-small-angle scatter, while rejecting higher-angle scatter. This paper presents a theoretical model of the relevant X-ray propagation mechanisms, thereby explaining why MIR works. Specifically, beam propagation is computed through a stratified scattering medium in an ultra-small-angle regime. This analysis demonstrates that the MIR images are linear with object thickness, which is a requirement for computed tomography by standard reconstruction methods.
Multiple-image computed tomography

We have recently proposed and investigated a planar imaging method called multiple-image radiography (MIR) that concurrently produces three two-dimensional images that reveal information about the ultra-small-angle scattering and refractive index properties of the object, in addition to an almost scatter-free radiographic image that depicts the projected absorption properties of the object. In this work, we develop and implement experimentally a computed tomography (CT) version of MIR, referred to as multiple-image CT (MICT), that produces three volumetric images of these object properties. The appropriateness of a linear imaging model is validated experimentally by use of phantom studies. The MICT method is employed for reconstruction of images of two biological phantoms using measurement data produced by a synchrotron light source.
Motion-compensated reconstruction of gated cardiac SPECT images using a deformable mesh model

We propose an algorithm for iterative, motion-compensated reconstruction of cardiac-gated SPECT. Dose limitations in SPECT lead to high level of noise in the projection data and further in the reconstructed images. Several reconstruction techniques have been reported to mitigate for the noise effects but they process each time frame individually and do not account for data temporal correlation. Advanced methods that allow for motion-compensated noise reduction use uniformly sampled pixels grid to represent images. Here we present a motion-compensated 4D reconstruction algorithm using content adaptive deformable mesh model (which is based on a deformable non-uniform sampling grid) for cardiac-gated SPECT. The proposed method tracks myocardial motion and utilizes the estimated motion to apply a cardiac-motion compensated temporal smoothing constraint during reconstruction. The temporal constraint is enforced between iterations of mesh based maximum-likelihood expectation-maximization algorithm. Specifically, temporal filtering is applied, in mesh domain, along the motion trajectory between iterations. The motion trajectory is estimation using our previously reported deformable mesh motion estimation technique. Visual comparisons as well as quantitative evaluation show that the proposed method achieves better noise reduction compared to several clinically used methods.
Tomographic image reconstruction based on a content-adaptive mesh model

In this paper, we explore the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first represented by a mesh model, an efficient image description based on nonuniform sampling. In the CAMM, image samples (represented as mesh nodes) are placed most densely in image regions having fine detail. Tomographic image reconstruction in the mesh domain is performed by maximum-likelihood (ML) or maximum a posteriori (MAP) estimation of the nodal values from the measured data. A CAMM greatly reduces the number of unknown parameters to be determined, leading to improved image quality and reduced computation time. We demonstrated the method in our experiments using simulated gated single photon emission computed tomography (SPECT) cardiac-perfusion images. A channelized Hotelling observer (CHO) was used to evaluate the detectability of perfusion defects in the reconstructed images, a task-based measure of image quality. A minimum description length (MDL) criterion was also used to evaluate the effect of the representation size. In our application, both MDL and CHO suggested that the optimal number of mesh nodes is roughly five to seven times smaller than the number of projection bins. When compared to several commonly used methods for image reconstruction, the proposed approach achieved the best performance, in terms of defect detection and computation time. The research described in this paper establishes a foundation for future development of a (four-dimensional) space-time reconstruction framework for image sequences in which a built-in deformable mesh model is used to track the image motion.
Tomographic image reconstruction using content-adaptive mesh modeling

We propose the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a CAMM can greatly alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it can lead to development of efficient numerical reconstruction algorithms. Finally, it can be useful for motion-tracking of image sequences. The proposed methods are tested using simulated gated cardiac-perfusion SPECT images. Our results indicate that, among the methods tested, the proposed approach achieves the best performance in terms of image quality and computation time, and can also reduce the memory requirement.
Limited-angle tomography for multiple-image radiography

Multiple-image radiography (MIR) is an analyzer-based phase sensitive X-ray imaging method, which is a potential alternative for conventional radiography. MIR simultaneously generates three planar images containing information about attenuation, refraction and scattering properties of the object. These three planar images are linear projections of the corresponding properties inside the object. The linearity of the projections allows reconstruction of volumetric images by computed tomography (CT) methods. In this work, we apply limited-angle tomography to MIR images of a human thumb and compare the reconstructed slices near the focal plane of the imaging system with computed tomography (CT-MIR) reconstructions of the same data set. We also perform a task-based evaluation for Limited-Angle Tomography MIR (LAT- MIR) to investigate a sufficient range for data acquisition procedure. Specifically, we use template matching for detecting the cartilage in refraction image and measure the signal-to-noise ratio as detectability metric.
Spatially Adaptive Temporal Smoothing for Reconstruction of Dynamic Image Sequences

In this paper, we propose a method for spatio-temporal reconstruction of dynamic image sequences. In a method we proposed previously, temporal smoothing in a Karhunen-Loegraveve (KL) or principal components (PC) transform domain was used prior to reconstruction to reduce the effect of noise. Unlike the Bayesian priors that are usually used in image reconstruction, temporal KL smoothing is a data-driven approach that takes advantage of the fact that the desired part of the data is characterized by strong interframe correlations, whereas the noise is uncorrelated. A potential disadvantage of KL-based methods is that they typically use a pooled estimate of the signal covariance matrix, thus assuming that all pixels obey similar time functions. In this paper, we investigate the possibility of making the temporal smoothing adapt spatially to local characteristics in the projection data. This can improve the noise performance of the temporal smoothing, while lessening the possibility of signal distortion. Computer simulation results are used to evaluate the technique for dynamic imaging applications in brain and tumor imaging
EVALUATION OF MODEL BASED PARAMETRIC IMAGE ESTIMATION IN MIR

Multiple-image radiography (MIR), a new X-ray phase sensitive imaging method, can simultaneously produce parametric images of absorption, refraction, and ultra-small-angle scatter. In the past it has been shown that these three parametric images, when compared to conventional X-ray methods (e.g. mammograms), have excellent diagnostic abilities, thanks to an X-ray phase-contrast mechanism and higher-angle scatter rejections. In this paper we present an evaluation of a newly proposed parametric image estimation method based on a physical model of the image formation in MIR. The proposed method employs the fact that the angular intensity profile (AIP) of the measured X-ray, after interaction with the object, can be modeled as a convolution of a Gaussian curve and the intrinsic imaging system AIP. Further, the new estimation method utilizes an iterative conjugate gradients algorithm that, in combination with the model, offers improvement in parametric image estimation accuracy even in the presence of the imaging noise.
Frechet distance for model observer training data selection

In medical imaging, it has become widely accepted that image quality should be assessed using a task-based approach in which, for example, one evaluates human observer detection accuracy for a specific diagnostic task. These evaluations should be integral part of an imaging system optimization and testing. However, human observer studies with expert readers are costly and time-demanding. Consequently, model observers (MO) have been used as surrogates to predict human diagnostic performance. MOs use features derived from the images to accomplish these predictions. Some types of MOs require a set of data evaluated by humans for model tuning. In this work we present a methodology for tuning data selection. This selection is based on the Frechet distance between image-feature distributions. Specifically, in our experiments we show that MO, based on the Relevance Vector Machine (RVM), trained with the selected small subset of data has excellent performance in predicting human observer for diagnostic tasks.
Image sequence segmentation based on a similarity metric

In this paper we present a new approach for clustering of time-sequence imaging data. The clustering metric used is the normalized cross-correlation, also known as similarity. The main advantage of this metric over the more-traditional Euclidean distance, is that it depends on the signal's shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties, the expectation-maximization (EM framework is used to derive two iterative clustering algorithms. In numerical experiments based on a simulated dynamic PET brain study, the proposed method achieved better performance than several existing clustering methods.
Iterative method for parametric image estimation in MIR

A new X-ray imaging method called multiple-image radiography (MIR), can simultaneously produce parametric images of absorption, refraction, and ultra-small-angle scatter, while rejecting higher-angle scatter. These parametric images where shown to have a superb diagnostic abilities compared to some conventional X-ray methods (e.g. mammograms). In this paper we present a new parametric image estimation method based on physical model of image formation in MIR. Presented method uses the fact that the angular profile of the measured X-ray, after interaction with the object, can be modeled as a convolution of a Gaussian curve and intrinsic imaging system angular profile. Further, the new approach uses an iterative conjugate gradients algorithm which in combination with the model, offers improvement in parametric image estimation accuracy.
Motion-compensated 4D processing of gated SPECT perfusion studies

In this paper ire present two 4D motion-compensated reconstruction algorithms for gated cardiac SPECT. The algorithms are based on a deformable content-adaptive mesh model, a compact image representation that is well suited for motion tracking of non-rigid objects like the heart. In this paper we introduce a reconstruction method using inter-iteration smoothing, in which motion estimation, reconstruction, and smoothing are all performed in the mesh domain. We also describe a pixel-based post-reconstruction temporal smoothing approach. In these methods, we modify our previous approaches to account for apparent brightening due to wall thickening (tin effect of finite resolution). Our experiments show that the new algorithms produce image sequences that closely match the expected intensity variations at the spatial resolutions of interest. In gated SPECT, these variations are a useful and important clinical indicator of wall thickening, so it is beneficial to preserve them. In our experiments, the proposed methods outperform those that do not take into account the brightening effect.
FOUR-DIMENSIONAL RECONSTRUCTION OF GATED CARDIAC SPECT WITH ATTENUATION AND SCATTER COMPENSATION

In our recent work we proposed a motion-compensated approach for four-dimensional (4D) reconstruction of gated cardiac images in single photon emission tomography (SPECT), and demonstrated that motion-compensated temporal smoothing can be effective for noise reduction in the reconstructed images. In this paper we further develop our proposed 4D approach by considering attenuation and scattering which are two important degradation factors in SPECT imaging. Our simulation results demonstrate that with proper attenuation and scatter compensation, the 4D method can lead to improved reconstruction over independent reconstruction with spatial smoothing only. Our evaluation study was conducted by simulating Tc99m labeled sestamibi imaging based on the NURBS-based cardiac-torso (NCAT) phantom
Numerical observer for cardiac motion assessment using a linear discriminant

In this work, we aim to develop a numerical observer for cardiac motion assessment in cardiac-gated single photon emission computed tomography (SPECT) imaging. In clinical imaging, diagnostic performance of a human observer for a specific diagnostic task is the gold standard of image quality assessment. However human studies are impractical and costly therefore the use of a numerical observer, a mathematical model that emulates human observer, is nowadays a common practice. To this end, the channelized Hotelling observer is a numerical observer used to assess image quality in myocardial perfusion defect detection tasks. However, cardiac motion, another important diagnostic feature for cardiac imaging, has not been used to evaluate image quality. Thus, we propose a new numerical observer aiming to predicting human observers' performance in detection of cardiac motion defects. The proposed observer uses regional features extracted by our previously introduced and evaluated motion estimation technique, which relies on a deformable mesh model. Next, these features are used in a linear discriminant model in order to predict human performance. Note that this work does not rely on a channelized model of the human visual system for feature extraction as the CHO does. Instead we use a deformable mesh model capturing the relevant regional motion information. For evaluation, we have used a simulated cardiac motion defect and performed comparison with a human observer study for two types of post-reconstruction filtering strategies. Observers' comparison suggests that the proposed methods can accurately predict human detection performance in assessment of cardiac motion defect.
Noise and sampling analysis for multiple-image radiography

We have recently proposed a new X-ray imaging method called multiple-image radiography (MIR), which produces three images that separately depict the effects of X-ray absorption, X-ray refraction, and ultra-small-angle X-ray scattering. Because MIR is a relatively recent development, many of its basic properties have not yet been determined. In this paper we investigate the noise properties of MIR by deriving the Cramer-Rao lower bounds (CRLB) for the three MIR images. Next using this bound an evaluation of the uniform angular sampling acquisition procedure is given
Mesh model based projection operator for emission tomography

In this paper, we presented a framework for calculation of a projection operator for emission tomographic reconstruction, using content-adaptive mesh model (CAMM) for image representation, which can incorporate major image degradation models, namely attenuation and parallel hole collimator response. The CAMM is an efficient image description based on non-uniform sampling and linear interpolation. The data acquisition process, e.g. forward projection, is calculated using a ray-casting algorithm. We tested, successfully, the proposed projection operator model using a simulation and a maximum-likelihood expectation maximization reconstruction algorithm. The research described in this paper establishes a methodology for future development of 4D (deformable) CAMM reconstruction.
Task-based evaluation of diffraction-enhanced imaging

Herein we present a quantitative analysis of diffraction enhanced imaging (DEI) by evaluating object detectability. DEI is an X-ray imaging method that simultaneously produces absorption and refraction images of an object. In recent years, it has become widely accepted that task-based measures of image quality, such as the detectability of subtle image features, are the ultimate test of image quality. It is widely believed that the DEI refraction images measures soft tissue more sensitively than the DEI absorption image. In this paper, we use a modern task-based approach to evaluate this assertion. Specifically, we use the log-likelihood ration test to devise a statistically optimal detector, usually refer to as "ideal observer", and use the area under the receiver operating characteristic (ROC) curve as a detectability metric. Using this method, we study the detectability of a blood vessel in lung tissue at 18 keV. We find that the refraction image is better than the absorption image if the blood vessel is smaller than 0.4 cm, but that the absorption image is better for blood vessels larger than 0.4 cm. In future work, we will consider whether it is generally true that small objects are more detectable by refraction imaging while large objects are more detectable by absorption imaging.
Estimation of myocardium deformation by simultaneous use of tagged and untagged gated cardiac MRI

In this manuscript we describe a method to estimate myocardium deformation that simultaneously uses both tagged and untagged cardiac-gated MRI image sequences. We show that this approach is more accurate and robust than traditional methods that usually use only tagged cardiac-gated MRI data. Estimation of the deformation field is based on a deformable mesh model (DMM) that incorporates a myocardium surface model. The DMM is well suited for motion tracking and analysis of organs undergoing non-rigid deformation. Previously, we successfully utilized a DMM for heart motion tracking in cardiac SPECT and tagged MRI. Promising preliminary results are presented.