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In this article, we review and compare different methods for the localization of an illuminator of opportunity for passive radar systems employing cooperative targets. For assessment and comparison, we use numerical simulations and real-world radar measurements. To achieve reliable operation under real-world conditions, a correct and robust association of the bistatic radar target observations with ground truth information is necessary. We introduce a novel solution to this issue with an efficient preprocessing algorithm based on a planar approximation.

While a range of models have been proposed for the multiperiod blend scheduling problem (MBSP), solving even medium-size MBSP instances remains challenging due to the presence of bilinear terms and binary variables. To address this challenge, we develop solution methods for MBSP focusing on the cost minimization objective. We develop a novel preprocessing algorithm to calculate lower bounds on stream flows. We define product dedicated flow variables to address product specific features involved in MBSP. Bounds on stream flows and new product dedicated flow variables are then used to generate tightening constraints which significantly improve the solution time of the mixed integer nonlinear programming models as well as models based on linear approximations.

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology created for this project involving the comparison of all possible pairs of temporal data points for each patient. This data was then used to train various machine learning models. Models were evaluated using 7-fold cross-validation on the training dataset and confirmed using data from a separate testing dataset (110 patients). A neural network model was effective (mAUC = 0.866) at predicting the progression of AD on a month-by-month basis, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics., Comment: 10 pages; updated acknowledgements

This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.

In this work we focus on an extension of the minimum cost flow problem in layered networks. Feasible arc flows must satisfy a specific compatibility restriction in addition to flow balance and capacity restrictions. Namely, at most one of the crossing arcs is allowed to have positive flow on it. This variant of the minimum cost flow problem, which we call the minimum cost noncrossing flow problem, can frequently be encountered in real life. The determination of optimal temporal quay crane allocations to berthed vessels in container terminals, and optimal train schedules through the stations on the same railroad line are two examples. We first analyze the complexity of the problem and show that the noncrossing flow problem is in fact N P -complete in a layered network. Then, we introduce mixed-integer linear programming formulations and discuss a polynomially solvable special case. Next we show a sufficient condition for the existence of a crossing in an optimal solution, which can be used for preprocessing the arcs in order to reduce the problem size. Our computational experiments on a large test set show that our preprocessing algorithm can significantly reduce the number of arcs.

Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other’s work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.

Security incidents such as natural disasters and power outages can cause inter-domain routing system regional failures, significantly impact the Internet’s safety. Reducing the impact of security incidents is essential for maintaining the stability of the Internet. One of the major impacts of security incidents is that many UPDATE messages will generate, which may easily cause network oscillations. This paper presents the UPDATE messages analysis during the six security incidents and finds that many duplicates and invalid messages are the leading cause of network instability. To effectively process these UPDATE messages, this paper proposes an UPDATE message preprocessing algorithm by analyzing the UPDATE operating mechanism to remove duplicate and invalid messages. Aiming at the problem of slow route search in existing route update methods using software-defined networking (SDN), this paper designs a RIB hierarchical structure for multi-level retrieval and proposes SMSEI-SDN combination with current route update strategies. Experimental results show that when a security incident occurs, by removing duplicate and invalid messages, SMSEI-SDN can reduce the total number of messages by an average of 19% and a maximum of 34.9% within the 60 s of caching time. Besides, SMSEI-SDN can reduce the routing update time by more than 99.98% compared to existing methods. This work provides insights for network operators and researchers interested in security incident impact suppression in the inter-domain routing system.

Because of its high research value, action recognition has become a very popular research direction in recent years. However, the research on the combination of motion recognition technology and dance movements is still in its infancy. At the same time, due to the high complexity of dance movements and the problems of human body self-occlusion when performing dances, research on dance video action recognition has been caused. Progress is relatively slow. This article mainly introduces the research of sports dance action recognition system oriented to human motion monitoring and sensing, fully considers the abovementioned problems, and makes in-depth research and analysis on the current excellent action recognition research content in this field. This paper proposes a research method of sports dance movement recognition for human movement monitoring and sensing, including sports dance movement classification algorithm and sports dance movement preprocessing algorithm, which is used to conduct research experiments on sports dance movement recognition for human movement monitoring and sensing. The experimental results of this article show that the average recognition accuracy of the sports dance action recognition system for human motion monitoring and sensing is 92%, which can be used in daily sports dance training and competition.

One of the significant challenges in machine learning is the classification of imbalanceddata. In many situations, standard classifiers cannot learn how to distinguish minority classexamples from the others. Since many real problems are unbalanced, this problem has becomevery relevant and deeply studied today. This paper presents a new preprocessing method based onDelaunay tessellation and the preprocessing algorithm SMOTE (Synthetic Minority Over-samplingTechnique), which we call DTO-SMOTE (Delaunay Tessellation Oversampling SMOTE). DTO-SMOTEconstructs a mesh of simplices (in this paper, we use tetrahedrons) for creating synthetic examples.We compare results with five preprocessing algorithms (GEOMETRIC-SMOTE, SVM-SMOTE,SMOTE-BORDERLINE-1, SMOTE-BORDERLINE-2, and SMOTE), eight classification algorithms,and 61 binary-class data sets. For some classifiers, DTO-SMOTE has higher performance than othersin terms of Area Under the ROC curve (AUC), Geometric Mean (GEO), and Generalized Index ofBalanced Accuracy (IBA).

Stroke is a high-risk disease causing death, permanent disability in patients, and is the leading cause of death worldwide. Stroke can be quickly examined for disease through CT, an imaging diagnostic tool. However, the diagnosis of Ischemic Stroke using a CT image has the advantage of being able to take a picture in a short time with less restrictions in place, but there is a problem that diagnosis through an image is very difficult. In this paper, we propose a deep learning system capable of learning and classifying ischemic stroke diseases that are small datasets and difficult to learn about image data. We propose a preprocessing algorithm optimized for ischemic stroke based on Non-Contrast CT data in Middle Cerebral Artery (MCA) area.

Background A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. Methods In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) Results Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI Conclusions An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.

We study edit distance computation with preprocessing: the preprocessing algorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings. This model is inspired by scenarios where we would like to compute edit distance between many pairs in the same pool of strings. Our results include: Permutation-LCS: If the LCS between two permutations has length $n-k$, we can compute it \textit{ exactly} with $O(n \log(n))$ preprocessing and $O(k \log(n))$ query time. Small edit distance: For general strings, if their edit distance is at most $k$, we can compute it \textit{ exactly} with $O(n\log(n))$ preprocessing and $O(k^2 \log(n))$ query time. Approximate edit distance: For the most general input, we can approximate the edit distance to within factor $(7+o(1))$ with preprocessing time $\tilde{O}(n^2)$ and query time $\tilde{O}(n^{1.5+o(1)})$. All of these results significantly improve over the state of the art in edit distance computation without preprocessing. Interestingly, by combining ideas from our algorithms with preprocessing, we provide new improved results for approximating edit distance without preprocessing in subquadratic time.

In this work, we have proposed a simple overlapping community detection algorithm based on a distributed neighbourhood threshold method (DNTM). DNTM uses pre-partitioned disjoint communities and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. Threshold value for each boundary node is used as minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the DNTM algorithm has been demonstrated by testing on fifteen real-world datasets and compared with seven overlapping community detection algorithms. DNTM outperforms comparable algorithms with 10 out of 15 datasets and gives comparable results for the remaining 5 datasets in terms of the extended modularity \(Q_{ov}\) measure. Experiments with various disjoint algorithms on 15 datasets reveal that DNTM with tolerance community detection (TCD) as a preprocessing algorithm gives the best result.

Ціла низка екзогенних та ендогенних факторів чинить комплексний синергічний негативний вплив на стан серцево-судинної системи як основного адаптогена. Для його ранньої діагностики, зокрема у системах віддаленого моніторування, запропоновано використати фонокардіосигнал (синхронно зареєстрований з електрокардіосигналом), який у термінах системно-сигнальної концепції є переносником відомостей про морфофункціональний стан серця. Як модель фонокардіосигналу використано періодично корельований випадковий процес (оскільки ця модель є адекватною до природи породження сигналу) та синфазний метод опрацювання, що дало змогу врахувати вплив нервової регуляції (синусового вузла як водія ритму). Синфазний метод вимагає забезпечення однорідності статистичного матеріалу та визначення періоду корельованості вхідної послідовності, який у випадку фонокардіосигналу може бути представленим як середня тривалість серцевого циклу. Визначення його за тривалістю R-R інтервалу не дає змоги забезпечити вибірку однієї окремої реалізації, тобто інтервалу між моментами прояву дії синусового вузла, що на електрокардіограмі виявляється P-зубцем. Тому запропоновано визначати одну реалізацію серцевого циклу як інтервал P-P. Наведено процедуру попереднього опрацювання зареєстрованого сигналу за алгоритмом: детрендінг сигналу (позбавлення від тренду, постійної складової сигналу), згладжування (позбавлення від високочастотних шумів з мінімальним спотворенням спектру та зміщенням локалізацій P-зубців), знаходження періоду повторюваності за P-зубцями. Така модель фонокардіосигналу дає можливість удосконалення існуючих систем віддаленого кардіомоніторингу для виявлення змін у роботі серця на ранніх стадіях розвитку патологічного процесу шляхом упровадження нового класу інформативно-інваріантних ознак – спектральних компонент, отриманих у результаті опрацювання сигналу синфазним методом.

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.

Time Reversal (TR) imaging methods have recently generated considerable interest due to their robustness and ability to locate multiple targets. However, problems arise when the targets are behind an obstructing material. We develop a preprocessing algorithm for TR imaging methods to obtain images of targets hidden behind a wall, applying temporal and spatial windows on the full-Multistatic Data Matrix (MDM). By using temporal-windows, we divide the full-MDM into sub-MDMs. We further segment the sub-MDMs spatially by selecting its elements corresponding to a predetermined number of antennas in space.We take the singular value decomposition of the sub-MDMs to obtain singular value distributions as well as singular vectors which we analyse individually and use for the MUltiple SIgnal Classification (MUSIC) TR imaging method. The obtained images from the these localised sub-MDMs collectively yield to clearerimages of scenarios than we can obtain using the full-MDM.

In order to better assess the health of the human body and protect people from disease, this study designs a wearable glasses system based on pulse signal preprocessing algorithm, pulse rate calculation algorithm and motion recognition algorithm. The pulse data before and after exercise are used to analyze the health status of the human body. Finally, the performance in various aspects of the system was verified by relevant experiments. The results show that the system is feasible. It can be seen that the system can be used for the assessment of human health conditions, and it is expected to serve people well in the future and make people live a healthier life.

With the development of information society, biometrics technology has been paid more and more attention. Iris recognition is considered as the most promising biometric authentication technology in the 21st century because of its uniqueness, stability and non-creativity. However, due to the high cost of iris recognition equipment and some defects of the algorithm, iris recognition cannot be applied in real life on a large scale. In this paper, a fast localization iris recognition algorithm is proposed, which combines the iris segmentation algorithm with deep learning to quickly extract the iris region for recognition. Firstly, the pupil edge was extracted by dynamic threshold analysis and contour extraction, and then iris was located by edge detection and gray calculation. Finally, features of normalized images were learned by deep learning network. Experiments show that the method can guarantee the accuracy and efficiency of iris segmentation and has a high degree of recognition and matching.

Personal laser scanning (PLS) has significant potential for estimating the in-situ diameter of breast height (DBH) with high efficiency and precision, which improves the understanding of forest structure and aids in building carbon cycle models in the big data era. PLS collects more complete stem point cloud data compared with the present laser scanning technology. However, there is still no significant advantage of DBH estimation accuracy. Because the error caused by merging different point cloud fragments has not yet been eliminated, overlapping and inaccurate co-registered point cloud fragments are often inevitable, which are usually the leading error sources of PLS-based DBH estimation. In this study, a novel pre-processing algorithm named annular neighboring points distribution analysis (ANPDA) was developed to improve PLS-based DBH estimation accuracy. To reduce the impact of inaccurately co-registered point cloud fragments, ANPDA identified outliers through iterative removal of outermost points and analyzing the distribution of annular neighboring points. Six plots containing 247 trees under different forest conditions were selected to evaluate the ANPDA. Results showed that in the six plots, error reductions of 53.80&ndash, 87.13% for bias, 38.82&ndash, 57.30% for mean absolute error (MAE), and 27.17&ndash, 56.02% for root mean squared error (RMSE) were achieved after applying ANPDA. These results confirmed that ANPDA was generally effective for improving PLS-based DBH estimation accuracy. It appeared that ANPDA could be conveniently fused with an automatic PLS-based DBH estimation process as a preprocessing algorithm. Furthermore, it has the potential to predict and warn operators of potential large errors during hierarchical semi-automatic DBH estimation.

Persistent homology allows for tracking topological features, like loops, holes and their higher-dimensional analogues, along a single-parameter family of nested shapes. Computing descriptors for complex data characterized by multiple parameters is becoming a major challenging task in several applications, including physics, chemistry, medicine, and geography. Multiparameter persistent homology generalizes persistent homology to allow for the exploration and analysis of shapes endowed with multiple filtering functions. Still, computational constraints prevent multiparameter persistent homology to be a feasible tool for analyzing large size data sets. We consider discrete Morse theory as a strategy to reduce the computation of multiparameter persistent homology by working on a reduced dataset. We propose a new preprocessing algorithm, well suited for parallel and distributed implementations, and we provide the first evaluation of the impact of multiparameter persistent homology on computations.

With the wide spreading of geo-aware mobile applications, huge amounts of user-contributed GPS trajectories become available with different levels of accuracy. Constructing road maps from such datasets is one important benefit from this data, and it is required for many applications. There are some challenges related to the inaccuracies incurred on real datasets, such as missing GPS signals, low sampling rate and bad driving behaviour. In this paper, we present a new preprocessing algorithm to address the problems of GPS data. Additionally, we present a clustering-based technique to extract the road map from GPS tracks. Firstly, the tracks are simplified in order to extract road turns and remove the noise data. Then, we adjust the points of the simplified tracks to solve the problems caused by the low sampling rate by moving them closer to the positions of the real turns. Afterwards, a progressive clustering is applied to extract turns and intersections. Finally, we connect them to build the road segments. To ensure the accuracy of our results, we compare the proposed technique with two of the best state-of-the-art methods using a small-scale dataset with inconsistent sampling rate. Another experiment is conducted by extracting a part of the road segments of Egypt using a large-scale dataset with more than 12 million GPS points that are captured with high sampling rate. Experimental results show that our proposed technique exceeds the other methods with regard to F-measure., SCOPUS: ar.j, info:eu-repo/semantics/published

Plants are such important keys of biological part of our environment, supply the human life and creatures. Understanding how the plant’s functions react with our surroundings, helps us better to make plant growth and development of food products. It means the plant phenotyping gives us bio information which needs some tools to reach the plant knowledge. Imaging tools is one of the phenotyping solutions which consists of imaging hardware such as the camera and image analysis software analyses the plant images changings such as plant growth rates. In this paper, we proposed a preprocessing algorithm to eliminate the noise and separate foreground from the background which results the plant image to help the plant image segmentation. The preprocessing is one of important levels has effect on better image segmentation and finally better plant’s image labeling and analysis. Our proposed algorithm is focused on removing noise such as converting the color space, applying the filters and local adaptive binarization step such as Niblack. Finally, we evaluate our algorithm with other algorithms by testing a variety of binarization methods.

The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body, and this can be sensed and used to predict emotions using only biomedical measurements. Based on theoretical and empirical psychophysiological research, the foundation of autonomic specificity facilitates the establishment of a strong background for recognising human emotions using machine learning on physiological patterning. However, a systematic way of choosing the physiological data covering the elicited emotional responses for recognising the target emotions is not obvious. The current study demonstrates through experimental measurements the coverage of emotion recognition using common off-the-shelf wearable biosensors based on the synchronisation between audiovisual stimuli and the corresponding physiological responses. The work forms the basis of validating the hypothesis for emotional state recognition in the literature and presents coverage of the use of common wearable biosensors coupled with a novel preprocessing algorithm to demonstrate the practical prediction of the emotional states of wearers.

Indoor positioning methods based on wireless local area network (WLAN) signal measurements have gained popularity because of high localisation accuracy. These methods use radio-maps obtained from wireless signal measurement surveys on location grids. Measurement sets from various WLAN access points are called fingerprints and they can be used to identify locations where the measurements are collected. WLAN positioning methods face unexpected changes in signal patterns because of attenuation changes or transient faults in WLAN cards or access points that often make signal strength readings unavailable. This study studies the effect of faulty measurements on the performance of popular state-of-the-art WLAN indoor positioning methods. Additionally, an integrity monitoring preprocessing algorithm is provided that demonstrates a possibility of faulty measurements mitigation for conventional methods such as K-nearest-neighbour. This is achieved by detecting and excluding faulty measurements prior to classification. Performance figures are provided for both simulated and empirical environments.

Due to increasingly dense agricultural land use, many ground breeding birds are threatened with extinction or near threatened. We present a new method, to detect lapwing and other ground breeding bird nests. A small UAV with a thermal camera is suitable to detect a lapwing nest from a flight altitude of 40 m. With the aid of a special thermal camera image preprocessing algorithm the contrast could be enhanced by 10 to 50% which improves the detectability of nests. We successfully demonstrated, that we detected 93% of the known nests. This paper describes and analyzes the necessary conditions for optimal lapwing nest detection with a UAV borne thermal camera.

missing values are presented in this paper. The traditional algorithms (3)(7) which are improved in the present work are described. This paper presents the general methodology used to handle missing values. The working of the two proposed missing values algorithms are presented. 2. PROPOSED ALGORITHM The general process of the proposed missing value handling algorithms is given here

In the field of image processing, complex illumination conditions have a serious impact on the accurate recognition of targets. Pre-processing complex illumination before face recognition can improve the accuracy of face recognition. In this paper, the Light is preprocessed by image enhancement algorithm and illumination invariant feature extraction algorithm based on Lambertian illumination model. The image enhancement algorithm achieves the purpose of preprocessing by adjusting the gray scale, and the illumination invariant feature algorithm achieves the purpose of preprocessing by extracting the features of the face. Finally, the advantages and disadvantages of the two types of algorithms are compared and analyzed through experimental results.

The $$k$$ k -NN classifier is a widely used classification algorithm. However, exhaustively searching the whole dataset for the nearest neighbors is prohibitive for large datasets because of the high computational cost involved. The paper proposes an efficient model for fast and accurate nearest neighbor classification. The model consists of a non-parametric cluster-based preprocessing algorithm that constructs a two-level speed-up data structure and algorithms that access this structure to perform the classification. Furthermore, the paper demonstrates how the proposed model can improve the performance on reduced sets built by various data reduction techniques. The proposed classification model was evaluated using eight real-life datasets and compared to known speed-up methods. The experimental results show that it is a fast and accurate classifier, and, in addition, it involves low pre-processing computational cost.

Head-related transfer functions (HRTFs) describe the directional filtering of the incoming sound caused by the morphology of a listener's head and pinnae. When an accurate model of a listener's morphology exists, HRTFs can be calculated numerically with the boundary element method (BEM). However, the general recommendation to model the head and pinnae with at least six elements per wavelength renders the BEM as a time-consuming procedure when calculating HRTFs for the full audible frequency range. In this study, a mesh preprocessing algorithm is proposed, viz., a priori mesh grading, which reduces the computational costs in the HRTF calculation process significantly. The mesh grading algorithm deliberately violates the recommendation of at least six elements per wavelength in certain regions of the head and pinnae and varies the size of elements gradually according to an a priori defined grading function. The evaluation of the algorithm involved HRTFs calculated for various geometric objects including meshes of three human listeners and various grading functions. The numerical accuracy and the predicted sound-localization performance of calculated HRTFs were analyzed. A-priori mesh grading appeared to be suitable for the numerical calculation of HRTFs in the full audible frequency range and outperformed uniform meshes in terms of numerical errors, perception based predictions of sound-localization performance, and computational costs.

Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the di-culty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately re∞ect the efiect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.

In this paper we present a new algorithm for computing the homology of regular CW-complexes. This algorithm is based on the coreduction algorithm due to Mrozek and Batko and consists essentially of a geometric preprocessing algorithm for the standard chain complex generated by a CW-complex. By employing the concept of S-complexes the original chain complex can—in all known practical cases—be reduced to a significantly smaller S-complex with isomorphic homology, which can then be computed using standard methods. Furthermore, we demonstrate that in the context of non-uniform cubical grids this method significantly improves currently available algorithms based on uniform cubical grids.

International audience; A branch-and-bound algorithm is devised to determine the optimal attack strategy to disconnect a network where the objective is to minimize the expected attacking cost. The attacker cannot launch an attack if its cost is beyond his available budget or its probability of success falls below a threshold level. The proposed branch-andbound algorithm includes, among other features, a dynamic programming-based lower bound as well as a preprocessing algorithm which aims at identifying unattackable links and removing irrelevant ones. Extensive use of the min-cut algorithm is made to derive valid upper bounds and to perform feasibility tests. Preliminary numerical implementation shows potential to provide exact solutions for medium-sized networks within reasonable time.

With the development of high-speed sampling and real-time signal processing technologies, the power consumption for implementing large-point FFT operations is drastically increasing in application systems. This paper presents an FFT preprocessing algorithm based on a multi-channel analysis filter bank, which is implemented by an efficient multi-phase parallel structure. The operating frequency is reduced to fs/M for each constituent channel, while the spectrum width and the resolution are maintained. Meanwhile, the valid frequency point can be correctly identified and the resulting error is controlled below -30dB. Compared with the two-dimensional FFT algorithm, the power consumption accelerating factor can reach 2.4~4 by utilizing 4-channel analysis filter bank.

We present a preprocessing algorithm for hyperspectral remote sensing datasets. The algorithm is based on a geostatistical method and should be helpful when a spatial relationship is detected in a dataset. One significant advantage of hyperspectral remote sensing using spectral profiles is the ability to compare an unknown pixel's profile with endmembers that have already been identified by a variety of methods (e.g., laboratory experiments with high-precision spectrometers), with the final goal of determining the unknown pixel. The conditions under which the airborne or spaceborne hyperspectral data are collected, however, differ from those that prevail in the laboratory or field. Therefore, a dataset should be preprocessed so as to eliminate or considerably reduce these differences; the algorithm presented here could be used for that purpose. The result will not only improve the smoothness of spectral profiles, but it may also offer advantages for geological investigations to study mineral anomalies usi...

Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an efficient illumination preprocessing method for face recognition. illumination preprocessing algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing effects of illumination. Due to the result of these improvements, face images preprocessed by the proposed illumination preprocessing method becomes to have more distinctive feature vectors(Gabor feature vectors). Through experiments of face recognition using Gabor jet similarity, the effectiveness of the proposed illumination preprocessing method is verified.