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Objectives To compare contrast-to-noise ratios (CNRs) and iodine discrimination thresholds on iodine maps derived from dual energy CT (DECT) and subtraction CT (SCT). Methods A contrast-detail phantom experiment was performed with 2 to 15 mm diameter tubes containing water or iodinated contrast concentrations ranging from 0.5 mg/mL to 20 mg/mL. DECT scans were acquired at 100 kVp and at 140 kVp+Sn filtration. SCT scans were acquired at 100 kVp. Iodine maps were created by material decomposition (DECT) or by subtraction of water scans from iodine scans (SCT). Matched exposure levels varied from 8 to 15 mGy. Iodine discrimination thresholds (Cr) and response times were determined by eight observers. Results The adjusted mean CNR was 1.9 times higher for SCT than for DECT. Exposure level had no effect on CNR. All observers discriminated all details ≥10 mm at 12 and 15 mGy. For sub-centimetre details, the lowest calculated Cr was ≤ 0.50 mg/mL for SCT and 0.64 mg/mL for DECT. The smallest detail was discriminated at ≥4.4 mg/mL with SCT and at ≥7.4 mg/mL with DECT. Response times were lower for SCT than DECT. Conclusions SCT results in higher CNR and reduced iodine discrimination thresholds compared to DECT for sub-centimetre details. Key Points • Subtraction CT iodine maps exhibit higher CNR than dual-energy iodine maps • Lower iodine concentrations can be discriminated for sub-cm details with SCT • Response times are lower using SCT compared to dual-energy CT Electronic supplementary material The online version of this article (10.1007/s00330-018-5496-x) contains supplementary material, which is available to authorized users.

BACKGROUND: In epilepsy patients, SISCOM or subtraction ictal single photon emission computed tomography co-registered to magnetic resonance imaging has become a routinely used, non-invasive technique to localize the ictal onset zone (IOZ). Thresholding of clusters with a predefined number of standard deviations from normality (z-score) is generally accepted to localize the IOZ. In this study, we aimed to assess the robustness of this parameter in a group of patients with well-characterized drug-resistant epilepsy in whom the exact location of the IOZ was known after successful epilepsy surgery. Eighty patients underwent preoperative SISCOM and were seizure free in a postoperative period of minimum 1 year. SISCOMs with z-threshold 2 and 1.5 were analyzed by two experienced readers separately, blinded from the clinical ground truth data. Their reported location of the IOZ was compared with the operative resection zone. Furthermore, confidence scores of the SISCOM IOZ were compared for the two thresholds. RESULTS: Visual reporting with a z-score threshold of 1.5 and 2 showed no statistically significant difference in localizing correspondence with the ground truth (70 vs. 72% respectively, p = 0.17). Interrater agreement was moderate (κ = 0.65) at the threshold of 1.5, but high (κ = 0.84) at a threshold of 2, where also reviewers were significantly more confident (p

In 3D digital subtraction angiography, the propagation of iodine-based contrast agent in cerebral vessels implies a delayed enhancement of soft tissue, i.e. parenchyma, which causes inconsistencies across the acquired projection images that impair the quality of the reconstructed volumes. In order to cope with this issue, we perform an estimation of contrast-enhanced parenchyma in projection images. The estimation is based on a vessel segmentation and an iterative interpolation of segmented vessel pixels. The estimated parenchyma is subsequently separated from the projection images. Thus, only contrast-enhanced vessels remain and data inconsistencies due to late-enhancing parenchyma will be reduced. The method is applied to two datasets of cerebral vasculatures. The image series are compared prior and post to parenchyma subtraction. Reconstructed volumes show minor noise in background voxels. An average increase of 37% in signal-to-noise ratio is achieved.

The learning of subtraction is essential for elementary students. However, common subtraction misconceptions hinder mathematics underachievers from learning subtraction. Existing remedial instruction focuses on teacher-oriented or self-learning approaches. However, we think that the tutor-based approach with virtual manipulatives can provide students with an opportunity to correct their misconceptions. In this study, a simple tutoring method based on misconception diagnosis is proposed, and supportive virtual manipulatives are prepared for voluntary tutors to use. First, the tutor identifies the tutee’s misconception by conducting a diagnostic test. Then, the tutor applies corresponding virtual manipulatives to help the tutee to understand the concept. For the first case manipulatives from the National Laboratory of Virtual Manipulatives (NLVM’s) are used, and the tutor-tutee interaction patterns are identified by sequential analysis. An enhanced virtual manipulative was then developed according to the aforementioned analysis, and applied to the second case to investigate its performance. Evaluation results indicate that despite the need for further improvement, the tutoring method with the enhanced virtual manipulative is effective for correcting subtraction misconceptions. Finally, recommendations for future research are proposed.

In paper the method of a estimating of frequency of a harmonic trend in a measurement signal are presented. The method is based on calculation of “quasispectrum” by zero-crossing number and estimation of maxima quasispectrum’s component. Results of research of the proposed method on models of measuring signals with harmonic trends are submitted. It is shown, that the proposed method allows estimating frequency of a harmonic trend with a small error. The practical realization of the method is simple and does not require any appreciable computational costs, since only arithmetic operations of summation and subtraction are used.

Brain single photon emission computed tomography (SPECT) is a functional imaging test that can provide a map of cerebral blood flow (CBF) changes during the interictal or ictal period in patients with epilepsy and other neurological disorders. The radiotracer for brain SPECT is rapidly taken up by the brain within 30–60 s after its injection. The radiotracer injection during seizure activity (ictal SPECT) can show a snapshot of CBF changes during seizure. The sensitivity of ictal SPECT is high in temporal lobe epilepsy, but relatively lower in extratemporal epilepsy. SISCOM is a subtraction technique between ictal and interictal SPECT images with coregistration on brain MRI. SISCOM significantly improved the sensitivity of ictal SPECT and can further localize epileptic focus. But according to the time of radiotracer injection, an epileptic focus can be associated with ictal hyperperfusion or hypoperfusion or mixed one. Thus, ictal SPECT and SISCOM should be interpreted carefully with clinical information, features, and duration of seizure, ictal EEG pattern at the injection time. SISCOM is also useful for localizing brain structure generating specific feature of seizures and studying propagation pathways of epileptic seizures and pathomechanism of other neurological disorders and sleep disorders.

In this paper, we propose a hybrid algorithm aimed at optimally synthesizing reversible Toffoli circuits in terms of the quantum cost for 4-bit and 5-bit reversible benchmarks. The hybrid algorithm alternates a variable-length evolutionary process with a heuristic factor subtraction algorithm based on Positive Polarity Reed Muller (PPRM) expansion. Further more, the variable length evolutionary algorithm employs a new constraint solving method, which introduces a trade-off factor to control a pair of contradictions: the decreasing of constraint violation and the increasing of quantum cost. The experimental results show that the hybrid algorithm outperforms existing combinations of a definite synthesis approach and a post-optimization method on some commonly used 4-bit and 5-bit benchmarks in point of quantum cost, and obtain some better results than the best known ones.

In this chapter, a problem of blind source separation for speech applications operated under real acoustic environments is addressed. In particular, we focus on a blind spatial subtraction array (BSSA) consisting of a noise estimator based on independent component analysis (ICA) for efficient speech enhancement. First, it is theoretically and experimentally pointed out that ICA is proficient in noise estimation rather than in speech estimation under a nonpoint-source noise condition. Next, motivated by the above-mentioned fact, we introduce a structure-generalized parametric BSSA, which consists of an ICA-based noise estimator and post-filtering based on generalized spectral subtraction. In addition, we perform its theoretical analysis via higher-order statistics. Comparing a parametric BSSA and a parametric channelwise BSSA, we reveal that a channelwise BSSA structure is recommended for listening but a conventional BSSA is more suitable for speech recognition.

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Abstract Corrective osteotomies are used to treat sagittal and coronal imbalance of the spine in patients with a variety of spinal deformities. It is important to be able to recognize the type and underlying cause of the deformity so that the most appropriate osteotomy can be chosen. The Smith-Petersen osteotomy is relatively simple compared with the other osteotomies and can typically be used to treat type-1 deformities. Also, curves that have a relatively smooth kyphosis instead of a sharp angular kyphosis can be treated with a Smith-Petersen osteotomy. Multiple Smith-Petersen osteotomies can be used to achieve the necessary amount of correction. Pedicle subtraction osteotomy is typically used in patients with greater imbalances in the sagittal plane of the spine and when a minimum of 30 of correction is needed. Vertebral column resection is reserved for deformities, such as those in both the sagittal and the coronal plane, that are not amenable to treatment with either a Smith-Petersen osteotomy or a pedicle subtraction osteotomy, or a combination of the two. Recent results have shown high patient satisfaction rates and good functional outcomes after spinal osteotomies done to treat a variety of disorders. As the level of complexity of the osteotomy increases, so does the potential for complications. E.C. Palou Department Hospital Vall d’Hebron, Autonomous University of Barcelona, Barcelona, Spain e-mail: ecaceres@vhebron.net

In this paper we propose an unsupervised approach based on low-rank representation (LRR) for change detection in remote sensing images. Given a pair of remote sensing images obtained from the same area but in different time, the subtraction and logarithm ratio operators are firstly applied to obtain two difference images. Meanwhile the sparse part generated by LRR is also employed for acquiring another difference image, which can detect the change information. Afterwards, LRR is used again to obtain the low-rank part of these three difference images which can reflect the common characteristics. Finally k-means is performed on the low-rank part and thus the final result of change detection can be gained. Experimental results show the effectiveness and feasibility of the proposed method.

Residue Number Systems have probed their potential for computation-intensive applications, especially those related to signal processing. Their main advantage is the absence of carry propagation between channels in addition, subtraction and multiplication. Thus, high-performance systems may be built for applications involving only these operations using Residue Number Systems. On the other hand, modular operation associated to these Residue Number Systems are those to be implemented for Galois Fields GF(p), which are the objective of the following chapters.

SPECT/CT optimizes interpretation and diagnostic confidence, thereby enriching the nuclear physician's role in guiding clinical management of endocrine tumors. SPECT/CT has become an increasingly important tool in the preoperative evaluation of hyperparathyroidism. In order to optimize sensitivity and anatomic precision, the optimal protocol can combine dual tracer pinhole imaging with subtraction and SPECT/CT. SPECT/CT is also an essential tool before reoperative parathyroid surgery for persistent or recurrent hyperparathyroidism, whatever the setting. Its role in the work-up for benign thyroid disorders is currently much more limited.

Arterial Spin Labeling (ASL) is a non-invasive technique for generating perfusion images of the brain. Following an alternating labeling/control acquisition sequence, the small magnetization difference between labeled and non-labeled images is usually detected by performing image subtraction. In order to increase the Signal to Noise Ratio (SNR) a large number of trials is needed to observe these signal differences. In this work, the magnetization difference estimation problem is formulated in a Bayesian framework, where spatio-temporal priors are used to deal with the ill-posed nature of the estimation task. The a priori assumption that no drastic signal variations are expected along the same tissue, except at the organ boundaries, is modeled by Gibbs distribution functions. To evaluate the performance of the proposed algorithm, the results obtained using synthetic data were compared against the two most common subtraction methods usually discribed in the literature. The results are very encouraging. A real data set is used to illustrate the application of the method and the results are consistant with the traditional methods.

We have developed an interactive environment for learning by posing arithmetic word problems that can be solved by either an addition or subtraction. Through experimental use of the environment on desktop computers, we have confirmed that problem posing with the environment is useful for arithmetic learning. In this paper, as the next step, we implemented the environment on media tablets connected by wireless LAN. Because of this implementation, we have realized development of environment for using usual classroom, visualization of the student's learning performance and suggestion of teaching a method of problem posing. Through this practice, we have confirmed that the first grade students were able to pose problems in the environment, and the teaching and learning by using environment were accepted by the teacher and students as the effective teaching.

It was confirmed that linear attenuation coefficient and effective atomic number of objects could be identified by X-ray 2D subtraction images discriminated by different energy. The differences between theoretical and experimental values of linear attenuation coefficient of each object from Xray subtraction images were 1.42% for Ti, 3.73% for Fe, and - 0.41% for Cu. The effective atomic number of each object from X-ray subtraction images was obtained Z=21.8 for Ti (Z=22), Z=25.8 for Fe (Z=26), and Z=16.1 for Cu (Z=29). The reproducibility of the energy corrected values of the linear attenuation coefficients and effective atomic number of objects from X-ray subtraction images was good except Z for Cu.

Cerebral aneurysm (CA) has been emerging as one of the life threatening diseases which have developed a deep concern amongst the neurologists in recent years. To be specific, it shows devastating characteristic due to the formation of abnormal bulging of artery in human brain followed by its rupture. Therefore detection of this abnormality prior to the rupture becomes inevitably essential to save our lives to a great extent. This paper throws enough light in detecting cerebral aneurysm of various sizes by combining the operations of spatial filtering and thresholding in an elegant way. A number of Digital Subtraction Angiogram (DSA) images, affected by cerebral aneurysm of various magnitudes, have been taken into consideration in this connection. Finally, the affected area has been marked with red colour to make it more prominent than the other parts of the image.

In this paper we discuss Observer/Interpreter P systems, i.e., a model of computation inspired by the possibility of tracking and detecting fluorescent proteins in living cells and interpreting the results by visualizing molecular events in real time. In this regard, we define Observer/Interpreter P systems as a couple of two independent systems: a P system with symbol objects and multiset rewriting rules and a finite state machine able to perform an operation (addition/subtraction) on a register. We investigate the computational power of the model when different features are taken into account.

In the electroencephalography (EEG) data simultaneously acquired with the functional magnetic resonance imaging (fMRI) data, the removal of the residual magnetic resonance (MR) gradient artifacts has been a challenging issue. To remove gradient artifacts generated from switching MR gradient field, average artifact subtraction (AAS) has been widely used. After applying the AAS method, however, residual MR gradient artifacts still remained in corrected EEG data. In this study, we proposed a novel method to remove the residual MR gradient artifacts (GAs) using random segmentation based principal component analysis (rsPCA). The performance of rsPCA was compared to that of the independent component analysis (ICA) method using data acquired from a motor imagery task. The results indicated that rsPCA could suppress further the residual MR gradient artifacts remained from the AAS step compared to the ICA method.

The EEG acquired simultaneously with functional magnetic resonance imaging (fMRI) is distorted by a number of artefacts related to the presence of strong magnetic fields. In order to allow for a useful interpretation of the EEG data, it is necessary to reduce these artefacts. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. Due to their repetitive nature, such artefacts can be reduced by subtraction of the respective template retrieved by averaging across cycles. In this paper, we investigate additional artefacts related to the MR environment and propose a method for the reduction of the vibration artefact caused by the cryo-cooler compression pumps system. Data were collected from the EEG cap placed on an MR head phantom, in order to characterise the MR environment related artefacts. Since the vibration artefact was found to be repetitive, a template subtraction method was developed for its reduction, and this was then adjusted to meet the specific requirements of patient data. The developed methodology successfully reduced the vibration artefact by about 90% in five EEG-fMRI datasets collected from two epilepsy patients.

Residue Number System (RNS), which originates from the Chinese Remainder Theorem, offers a promising future in VLSI because of its carry-free operations in addition, subtraction and multiplication. This property of RNS is very helpful to reduce the complexity of calculation in many applications. A residue number system represents a large integer using a set of smaller integers, called residues. But the area overhead, cost and speed not only depend on this word length, but also the selection of moduli, which is a very crucial step for residue system. This parameter determines bit efficiency, area, frequency etc. In this paper we propose a new moduli set selection technique to improve bit efficiency which can be used to construct a residue system for digital signal processing environment. Subsequently, it is theoretically proved and illustrated using examples, that the proposed solution gives better results than the schemes reported in the literature. The novelty of the architecture is shown by comparison the different schemes reported in the literature.

Computed tomography angiography (CTA) data sets without hardware based bone subtraction have the disadvantage of containing the bone structures which particularly overlap with vessel intensities; therefore vessel segmentation is hampered. Segmentation methods developed for CTA without bones can not handle these data sets and manual cerebral vessel segmentation is not realizable in clinical routines. Therefore, an automatic intensity based cerebral bone removal with subsequent edge based level set vessel segmentation method is presented in this work.

This paper describes a methodology developed for calibrating optical detector for light engineering, especially for devices used at low level light, including auroral imager, star sensor, astronomical camera and similar optical instruments. In order to know the physical meaning of optical sensor output, calibration is the first and most important process in a complete analysis of observed data. It is found that optical sensors, like CCDs, are not perfectly linear systems as they were assumed. After bias frame subtraction, the number of ADU counts is not exactly proportional to the number of incident photons. A key component of this paper is non-linearity correction. One of current applications using this method is auroral imager which is used for measuring aurora, high-altitude clouds, and other atmospheric optical objects light intensity, which is the first step to complete an optical object tomography simulation.

It has recently been shown, that hyper-arithmetical sets can be represented as the unique solutions of language equations over sets of natural numbers with operations of addition, subtraction and union. It is shown that the same expressive power, under a certain encoding, can be achieved by systems of just two equations, X+A=B and (X+X)+C=(X−X)+D, without using union. It follows that the problems concerning the solutions of systems of the general form are as hard as the same problems restricted to these systems with two equations, it is known that the question for solution existence is $\Sigma^1_1$ complete.

This paper proposes a concept of imaginary extreme numbers, which are like traditional complex number a + bi but with i = \(\sqrt{-1}\) being replaced by e = 1/0, and defines usual operations such as addition, subtraction, and division on the numbers. It applies the concept to representing linear equations in knowledge-based systems. It proves that the combination of linear equations via Dempster’s rule is equivalent to solving a system of simultaneous equations or finding a least-square estimate when they are overdetermined.

Image structure noise sometimes is a key factor influencing the image quality of hardcopies and widely exists in presswork using halftone technology. This paper describes an algorithm for measuring image structure noise, including moire and screen dot effect. In order to objectively meter the degree of structure noise, L* spectrum of colorful flat-area patch is mainly utilized to represent the structure noise of images, according to the subtraction principle and frequency prediction algorithm of moire. With human evaluation and rating, a linear model of structure noise is regressed by moire index and screen dot index. Finally the high correlation between human rating and metrics proves the effectiveness of the model.

CEDM is a radiological technique based on the use of digital mammography equipment and the injection of an iodinated contrast medium to enhance the visualization of tissues of interest. In previous works, our group has proposed a formalism for the use of dual-energy temporal CEDM, based on weighted subtraction of images, that has been applied with success to phantom data. This methodology requires the selection of ROIs by a radiologist, to determine the weight factors. In this work, we propose an alternative that improves the contrast in clinical images resulting from dual-energy temporal CEDM subtraction, while freeing the method from ambiguities due to the ROI selection by a radiologist. The new subtraction algorithm is based on the use of weight factors calculated pixel-by-pixel. The main result after evaluation of the methodology on images of 10 patients randomly chosen is a substantial improvement of the contrast (˜5 times), reaching values that are similar to those obtained with single energy subtraction.

Several dynamic XML labeling schemes have been proposed to efficiently process updating in dynamic XML data. In this paper, we focus on one class of these schemes which are using strings of lexicographical order to support dynamic XML. We point out the problems of existing encodings which include memory inefficiencies when initial labeling and achieving labels of sub-optimal size when dynamic labeling. A Full-Tree-based(FT) encoding technique is proposed to overcome these problems. We bring the concept of self-increase into strings, and it assures our encoding technique labeling for initial XML with efficient memory usage, which make it possible to process large XML with limited memory. Moreover, the concept of subtraction is generalized into lexicographical order for the guarantee of achieving new labels with optimal size when XML frequently updates, which has the advantages of both reducing the storage cost and optimizing query performance. Experimental results confirm that our FT technique provides a new method which is intuitional and efficient for dynamic label schemes.

Counting Bloom Filter based on the counter-array structure has the shortcoming of counter overflow and less space-efficient. To address these shortcomings, we propose a dynamic structure for Counting Bloom Filter which dynamically changes the counter size according to the number of inserted elements. Hence it not only makes a better use of memory space but also eliminates counter overflow. We put up with the methods of addition and subtraction bit by bit while inserting and deleting elements to effectively reduce the times of memory access. In this way, an effective tradeoff can be achieved between counter access speed and space efficiency. Besides, to reduce excessive memory allocation/deallocation cost caused by consecutively changing counter size, we propose a configurable delayed shrinking algorithm which can appropriately delay the counter size shrinking based on user’s configuration. The experiment results show that our dynamic structure could meet the needs of most application scenarios.

The last two decades have seen tremendous effort on the development of high-level algorithms for the multiplierless design of constant multiplications, i.e., using only addition, subtraction, and shift operations. Among the different types of constant multiplications, the multiplication of a constant matrix by an input vector, i.e., the constant matrix-vector multiplication (CMVM) operation, is the most general case and occurs in many digital signal processing (DSP) systems. This chapter addresses the problem of minimizing the number of addition and subtraction operations in a CMVM operation and introduces a hybrid algorithm that incorporates efficient techniques. This chapter also describes how the hybrid algorithm can be modified to handle a delay constraint. The experimental results on a comprehensive set of instances show the efficiency of the hybrid algorithms at both high-level and gate-level, in comparison to previously proposed methods.

This paper presents an improved method for detecting objects in stereo images and of calculating the distance, size and speed of these objects in real time. This can be achieved by applying a standard background subtraction method on the left and right image, subsequently a method known as subtraction stereo calculates the disparity of detected objects. This calculation is supported by several additional parameters like the center of object, the color distribution and the object size. The disparity is used to verify the plausibility of detected objects and to calculate the distance and position of this object. Out of position and distance the size of the object can be extracted, additionally the speed of objects can be calculated when tracked over several frames. A dense disparity map produced during the learning phase serves as additional possibility to improve the detection accuracy and reliability.

We previously developed a dynamic 4D anthropomorphic breast phantom, which can be used to optimize contrast-based breast imaging systems, accounting for patient variability and contrast kinetics [1]. In this study we aim to compare the performance of contrast-enhanced mammographic and tomosynthesis imaging protocols followed by temporal subtraction and dual-energy subtraction, qualitatively and quantitatively across a couple of patient models. Signal-difference-to-noise ratio (SDNR) is measured for the six paradigms of contrast enhanced, temporally subtracted, and dual-energy subtracted mammography and tomosynthesis and compared. The results show how the performance is more dependent on the breast model in mammography than in tomosynthesis. Also, it is observed that dual-energy subtraction can be beneficial in mammography, whereas it is not advantageous in tomosynthesis. Lastly, the results suggest that temporal subtraction in general outperforms dual-energy subtraction.

We study the problem of computing an ensemble of multiple sums where the summands in each sum are indexed by subsets of size p of an n-element ground set. More precisely, the task is to compute, for each subset of size q of the ground set, the sum over the values of all subsets of size p that are disjoint from the subset of size q. We present an arithmetic circuit that, without subtraction, solves the problem using O((np+nq)logn) arithmetic gates, all monotone; for constant p, q this is within the factor logn of the optimal. The circuit design is based on viewing the summation as a "set nucleation" task and using a tree-projection approach to implement the nucleation. Applications include improved algorithms for counting heaviest k-paths in a weighted graph, computing permanents of rectangular matrices, and dynamic feature selection in machine learning.

The paper addresses large scale image retrieval with short vector representations. We study dimensionality reduction by Principal Component Analysis (PCA) and propose improvements to its different phases. We show and explicitly exploit relations between i) mean subtraction and the negative evidence, i.e., a visual word that is mutually missing in two descriptions being compared, and ii) the axis de-correlation and the co-occurrences phenomenon. Finally, we propose an effective way to alleviate the quantization artifacts through a joint dimensionality reduction of multiple vocabularies. The proposed techniques are simple, yet significantly and consistently improve over the state of the art on compact image representations. Complementary experiments in image classification show that the methods are generally applicable.

Following a brief presentation of the serial adder, the chapter approaches at large the problems of functional analysis and synthesis of the parallel addition and subtraction devices for binary and decimal numbers. The advantages and disadvantages of the parallel adders and subtracters based on the ripple carry principle are discussed both for binary numbers as well as for decimal numbers. Afterwards solutions for improving the performance with respect to the critical parameter represented by the maximal addition time are analyzed, based on the carry lookahead, carry-skip, carry-select and conditional-sum principles. Distinct sections are allocated to the parallel adders based on the carry save principle allowing for the synthesis of efficient multiplication devices, respectively to the parity checked adders allowing for the enhancement of reliability and dependability.

Traffic is necessary in modern society. Road traffic monitoring is especially fundamental. Unfortunately, road traffic jam is so common nowadays. As a result, road traffic monitoring is getting more and more important. In the research and application of monitoring, a method called background subtraction is often used. This article aims to optimize the approach of refreshing background in road traffic monitoring while using background subtraction method. We study typical methods for refreshing background and make comparisons as well. And after that, you will find different method to update the background that different traffic conditions ask for. The approach chosen to refresh the background decides the accuracy of monitoring. For improper methods will cost unnecessary time and memory. And we can construct a counter affected by subtraction between frames of pictures to decide the best way to refresh the background.

The chapter is dedicated to the functional analysis and synthesis of the floating point arithmetic devices and it begins by highlighting the characteristics of operating in floating point, with investigation of the fundamental operations of addition, subtraction, multiplication and division. Particular consideration is given to the rounding problem with respect to analyzing the rounding modes, establishing the values of the rounding bits following the normalization shifts and of the conditioned implementation of the rounding operation. Synthesis solutions for some floating point units are presented, for which the addition and subtraction operations are to be realized without and with rounding. The steps of the algorithm for floating point addition and subtraction with rounding are dissected and the methods for speeding up the addition/subtraction process are investigated. Besides the speedup techniques based on an arithmetic pipeline, there is an extensive presentation of the solutions based on parallel computation. In this last context we present some innovative solutions based on reconfigurable synthesis. The chapter concludes with a section dedicated to analysis of the floating point multiplication and division operations.

This paper proposes a method for detecting general obstacles on a road by subtracting present and past in-vehicle camera images. The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning-based methods detect only specific obstacles. To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-vehicle camera image sequences, and then registrates road surfaces between the frames. Finally, obstacles are detected by applying image subtraction to the registrated road surface regions with an illumination insensitive feature for robust detection. Experiments were conducted by using several image sequences captured by an actual in-vehicle camera to confirm the effectiveness of the proposed method. The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even in situations with different illuminations.

An assessment is ongoing of the ability of dual energy contrast-enhanced digital breast tomosynthesis (CE-DBT) to depict the morphologic and vascular characteristics of breast cancer in comparison with breast MRI and digital mammography (DM). Eight patients with newly diagnosed breast cancer were imaged with an automated dual-energy CE-DBT system. High energy/low energy image pairs of the index breast were obtained at 1 pre- and 3 post-contrast timepoints. Post-contrast images were obtained after intravenous administration of Visipaque (1 mL/kg). Anatomic images were reconstructed using filtered backprojection, and contrast-enhanced images were generated using simple backprojection followed by temporal or dual-energy subtraction. Dual-energy CE-DBT was able to demonstrate the index malignant lesion in 7 of 8 patients (9 of 10 lesions). Morphologic characteristics including margin detail and associated microcalcifications were qualitatively concordant with DM. Vascular characteristics were identifiable qualitatively on post-processed images in some cases, and judged to be qualitative concordant with breast MRI.

Modular multiplication is widely used in cryptographic algorithms. In order to improve the efficiency, most of the recent implementations adopt precomputation. Precomputation improves the speed and in the meanwhile makes the algorithms more complex. The complex algorithms are not suitable for hardware implementation. We propose a new algorithm without precomputation, which is more efficient even compared with the ones with precomputation. Our algorithm is based on interleaving modular algorithm. The modulus in our algorithm is enlarged, and this modification greatly reduces the number of subtractions. By a small change of the multiplier, our algorithm does not need the last subtraction. We also propose a pipeline scheme which can achieve high frequency. Compared with existing work (including the precomputation ones), our implementation improves the throughput/area by 47%.