Dual Window Research Articles

ZGSO:Cr3+,Ni2+ Persistent Phosphors with Dual Emission in NIR-I and SWIR Ranges for Bio-Imaging Applications.

Persistent luminescence (PersL) is widely used for near infrared (NIR-I, 650-950nm) imaging as they allow getting images without background. Bio-imaging in the second shortwave-infrared region SWIR-II (NIR-II, 1000-1400nm) is less widespread but is growing as it offers the advantages of low photon scattering, increased in vivo penetration depth, and improved imaging clarity. In this work, the preparation and the complete optical properties of a new material is reported, Zn1.33Ga1.33Ni0.005Cr0.005Sn0.33O3.995 (ZGSO:Cr3+, Ni2+) able of emitting in both deep-red/NIR-I and SWIR (NIR-II) and shows its potential in bioimaging. ZGSO:Cr3+, Ni2+ can be excited using different sources such as X-rays, UV, and visible light to emit persistent signals in dual biological windows (dual-BW). By integrating an energy transfer process from Cr3+ to Ni2+ within this newly synthesized material, the influence of co-dopants on signal intensity and emission wavelengths is sought to explore. PersL at ≈700nm (NIR-I) and ≈1300nm (NIR-II) have been tested in preliminary bioimaging experiments using different protocols, allowing signal detection with good spatial resolution and depth sensitivity. The dual-BW PersL imaging strategy expands the toolbox for highly accurate analysis and has, for the first time, allowed access to accurately high-resolution sensing, and tracing.

A simulation study on the sub-threshold joint gravitational wave-electromagnetic wave observation on binary neutron star mergers

Abstract The coalescence of binary neutron stars (BNS) is a prolific source of gravitational waves (GWs) and electromagnetic (EM) radiation, offering a dual observational window into the Universe. Lowering the signal-to-noise ratio (S/N) threshold is a simple and cost-effective way to enhance the detection probability of GWs from BNS mergers. In this study, we introduce a metric of the purity of joint GW and EM detections Pjoint, which is in analogue to Pastro in GW only observations. By simulating BNS merger GWs jointly detected by the HLV network and EM counterparts (kilonovae and short Gamma-ray bursts, sGRBs) with an assumed merger rate density of BNS, we generate catalogs of GW events and EM counterparts. Through this simulation, we analyze joint detection pairs, both correct and misidentified. We find the following: 1. For kilonovae, requiring Pjoint > 95% instead of $P_{\rm astro}>95~{{\%}}$ reduces the S/N from 9.2 to 8.5-8.8, allowing 5-13 additional joint detections per year and increasing the GW detection volume by 9-17%; 2. For sGRBs, requiring Pjoint > 95% instead of Pastro reduces the S/N from 9.2 to 8.1-8.5; 3. Increasing kilonova or sGRB detection capability does not improve Pjoint due to a higher rate of misidentifications. We also show that sub-threshold GW and kilonova detections can reduce the uncertainty in measuring the Hubble constant to 89-92% of its original value, and sub-threshold GW and sGRB observations can enhance the precision of constraining the speed of GWs to 88% of previously established values.

Imparting insoluble-soluble property to Cyt c by immobilizing Cyt c in UCST-pH dual responsive polymer for highly sensitive detection of phenol

Immobilization of enzymes in porous organic framework (POF) materials is popular strategy to stabilize enzymes. For such solid enzyme catalysis system, improving the catalytic efficiency is challenging due to the diffusion resistance from solid-liquid interface and inner pores. Here, UCST-pH dual responsive polymeric carrier (PEG-b-PAAm-b-P(GMA-co-AAc)) was synthesized to immobilize cytochrome c (Cyt c), which impart the reversibly insoluble-soluble property to the immobilized Cyt c. The PEG-b-PAAm-b-P(GMA-co-AAc) could serve as an insoluble-soluble matrix to fast and efficiently immobilize Cyt c via covalent attachment, achieving a remarkable 92% loading efficiency within just 120min. The obtained insoluble PEG-b-PAAm-b-P(GMA-co-AAc)-Cyt c micelles exhibited an improvement in thermal, pH stability and reusability. The completely soluble PEG-b-PAAm-b-P(GMA-co-AAc)-Cyt c conjugates accelerated substrate diffusion and enhanced the catalytic efficiency. These excellent advantages led to low detection limit (1.99μM), lower than the presently reported biosensors based on enzyme mimics in the colorimetric detection of phenol. This UCST-pH dual responsive window presents a new platform to efficiently control the immobilization and release of enzymes, which will achieve excellent stability and catalytic efficiency.

A Broadband RCS Reduction Metasurface With Dual Transmission Windows

A Broadband RCS Reduction Metasurface With Dual Transmission Windows

Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework’s capabilities.

A Thermosensitive Ionic Hydrogel for Thermotropic Smart Windows With Integrated Thermoelectric Energy Harvesting Capability

AbstractHarvesting low‐grade waste heat into electrical energy is recognized as a promising solution for sustainable energy supply. In parallel, thermotropic smart windows have emerged as an efficient way to reduce building energy consumption. It is posit that thermotropic smart windows with inherent thermoelectric property may offer unique advantages for practical applications. Herein, the preparation and characterization of a series of ionic hydrogels that exhibit temperature‐sensitive phase transition behavior is reported, which are suitable for both thermotropic smart windows and thermoelectric generators. Notably, the lower critical solution temperature (LCST) of this ionic hydrogels can be feasibly modulated, and the thermoteopic phase transition also induces a sharp increase in their Seebeck coefficient, reaching up to 39.03 mV K−1 with a power factor (PFi) value up to 0.838 mW m−1 K−2. A prototype dual functional thermotropic smart window that simultaneously works as an ionic thermoelectric generator is further demonstrated, achieving both efficient phase transition driven by solar heat at ≈26 °C and an energy density up to 250 mJ m−2. This study offers new opportunity for the development of smart materials that can bridge the gap between thermal comfort and energy sustainability for energy harvesting and smart building applicat.

Supermarket-chain grocery delivery optimization through crowdshipping

Acknowledging the rising significance of online sales, the grocery business has embraced the challenge of fulfilling the consequently growing consumer expectations for the last-mile delivery efficiency. This paper investigates the grocery delivery optimisation for the supermarket chain based on the crowdshipping mechanism, which can be one of the viable strategies for establishing prompt and affordable delivery service for customers. Considering the deterministic optimisation setting, this study presents a characteristic routing model with crowdsourced couriers named supermarket-chain grocery delivery crowdshipping problem (SCGDCP), which is a variant of the pickup-and-delivery problem, and develops a corresponding mixed integer linear programming (MILP) model. The SCGDCP involves distinctive problem features including individual depots for couriers, multi-trip open routing, and dual time windows of courier operating and order arrival, which pose the computational challenge in problem solving. A bespoke solution procedure based on adaptive variable neighbourhood search (AVNS) strategy is thus designed for tackling the practical-size SCGDCP. The conducted numerical experiments demonstrate the computational efficiency of the proposed MILP model for the small-size instances with no more than 30 grocery orders and the superiority of the developed AVNS procedure for the Grubhub sampling test instances with up to 200 orders.

Treatment of the Distal Forearm Fracture by Volar Dual Window Approach.

Distal forearm fractures were defined as distal radius fractures with concomitant distal ulna fractures, except ulna styloid fractures. Distal forearm fractures are common among geriatric populations, particularly those with osteoporosis. Conventionally, distal forearm fractures are reduced by a double incision approach; however, malreduction and instability of the distal radioulnar joint were not uncommon. We introduced a modified volar dual window approach to treat the distal forearm fracture and evaluate the functional outcomes and complications. From January 2020 to June 2023, 13 patients with distal forearm fractures underwent open reduction by the modified dual window approach with locking plate fixation. After surgery, splints were applied for two weeks, and the patients underwent postoperative hand therapy for three months. The mean Quick Disabilities of the Arm, Shoulder, and Hand scores, range of motions, grip strength, postoperative radiographic parameters, and complications data were collected. The mean follow-up period was 12.1 months, and the mean age was 52.3 years. Average wrist flexion was 67°, extension 69°, pronation 81°, and supination 79°. Grip strength was 28.3 ± 11.5 kg, which was 88% of the uninjured opposite side. The Visual Analog Scale score during activities was recorded as 0.5 ± 0.9. The mean Quick Disabilities of the Arm, Shoulder, and Hand score was 14 ± 11.5. The postoperative radiographic parameters were as follows: radial height: 10.8 ± 1.7 mm, radial inclination: 22.6 ± 3.7°, volar tilting: 4.0 ± 3.9°, and ulnar variance: -0.4 ± 1.4 mm. All the patients achieved bone union at the final follow-up. Two patients underwent ulnar implant removal due to irritation symptoms. Neither infection, nor neurovascular injury, nor malreduction developed in these patients. The modified volar dual window approach can achieve good wrist function and distal forearm fracture reduction without increasing neurovascular or wound healing complications. This method is an alternative approach for distal forearm fracture, especially in comminuted distal ulna fracture or distal radioulnar joint incongruity.

Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting

Application of Dynamic Weight Mixture Model Based on Dual Sliding Windows in Carbon Price Forecasting

Symmetry-Engineered Dual Plasmon-Induced Transparency via Triple Bright Modes in Graphene Metasurfaces

Dynamical manipulation of plasmon-induced transparency (PIT) in graphene metasurfaces is promising for optoelectronic devices such as optical switching and modulating; however, previous design approaches are limited within one or two bright/dark modes, and the realization of dual PIT windows through triple bright modes in graphene metasurfaces is seldom mentioned. Here, we demonstrate that dual PIT can be realized through a symmetry-engineered graphene metasurface, which consists of the graphene central cross (GCC) and graphene rectangular ring (GRR) arrays. The GCC supports a bright mode from electric dipole (ED), the GRR supports two nondegenerate bright modes from ED and electric quadrupole (EQ) due to the C2v symmetry breaking, and the resonant coupling of these three bright modes induces the dual PIT windows. A triple coupled-oscillator model (TCM) is proposed to evaluate the transmission performances of the dual PIT phenomenon, and the results are in good agreement with the finite-difference time-domain (FDTD) method. In addition, the dual PIT windows are robust to the variation of the structural parameters of the graphene metasurface except for the y-directioned length of the GRR. By changing the carrier mobility of graphene, the amplitudes of the two PIT windows can be effectively tuned. The alteration of the Fermi level of graphene enables the dynamic modulation of the dual PIT with good performances for both modulation degree (MD) and insertion loss (IL).

Hyperspectral anomaly detection based on adaptive background dictionary construction and collaborative representation

ABSTRACT Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size.

AUD-Net: A Unified Deep Detector for Multiple Hyperspectral Image Anomaly Detection via Relation and Few-Shot Learning.

This article addresses the problem of the building an out-of-the-box deep detector, motivated by the need to perform anomaly detection across multiple hyperspectral images (HSIs) without repeated training. To solve this challenging task, we propose a unified detector [anomaly detection network (AUD-Net)] inspired by few-shot learning. The crucial issues solved by AUD-Net include: how to improve the generalization of the model on various HSIs that contain different categories of land cover; and how to unify the different spectral sizes between HSIs. To achieve this, we first build a series of subtasks to classify the relations between the center and its surroundings in the dual window. Through relation learning, AUD-Net can be more easily generalized to unseen HSIs, as the relations of the pixel pairs are shared among different HSIs. Secondly, to handle different HSIs with various spectral sizes, we propose a pooling layer based on the vector of local aggregated descriptors, which maps the variable-sized features to the same space and acquires the fixed-sized relation embeddings. To determine whether the center of the dual window is an anomaly, we build a memory model by the transformer, which integrates the contextual relation embeddings in the dual window and estimates the relation embeddings of the center. By computing the feature difference between the estimated relation embeddings of the centers and the corresponding real ones, the centers with large differences will be detected as anomalies, as they are more difficult to be estimated by the corresponding surroundings. Extensive experiments on both the simulation dataset and 13 real HSIs demonstrate that this proposed AUD-Net has strong generalization for various HSIs and achieves significant advantages over the specific-trained detectors for each HSI.

Calibration of medical gamma cameras for estimation of internal contamination from 137Cs

Calibration of 22 gamma camera units was performed at 15 hospitals in southern and western Sweden to estimate 137Cs contamination in humans in a supine static geometry, with a new developed calibration protocol and phantom. The minimum detectable activities (MDAs) and the estimated committed effective doses (CEDs) were calculated for each calibration. Generic calibration factors were calculated for five predetermined groups based on the detector type and manufacturer. Group 1 and 2 included NaI-based gamma cameras from General Electrics (GEs) with a crystal thickness of 5/8′′ and 3/8′′ respectively. Group 3 and 4 included NaI-based gamma cameras from Siemens Healthineers with a crystal thickness of 3/8′′, with a similar energy window as the GE NaI-based cameras and a dual window respectively. Group 5 included semiconductor-based gamma cameras from GE with a CdZnTe (CZT) detector. The generic calibration factors were 60.0 cps kBq−1, 52.3 cps kBq−1, 50.3 cps kBq−1, 53.2 cps kBq−1 and 48.4 cps kBq−1 for group 1, 2, 3, 4, and 5 respectively. The MDAs ranged between 169 and 1130 Bq for all groups, with measurement times of 1–10 min, corresponding to a CED of 4.77–77.6 μSv. A dead time analysis was performed for group 1 and suggested a dead time of 3.17 μs for 137Cs measurements. The dead time analysis showed that a maximum count rate of 232 kcps could be measured in the calibration geometry, corresponding to a CED of 108–263 mSv. It has been shown that semiconductor-based gamma cameras with CZT detectors are feasible for estimating 137Cs contamination. The generic calibration factors derived in this study can be used for gamma cameras of the same models in other hospitals, for measurements in the same measurement geometry. This will increase the measurement capability for estimating internal 137Cs contamination in the recovery phase following radiological or nuclear events.

The Impact of Dual and Triple Energy Window Scatter Correction on I-123 Postsurgical Thyroid SPECT/CT Imaging Using a Phantom with Small Sizes of Thyroid Remnants.

I-123 is preferential over I-131 for diagnostic SPECT imaging after a thyroidectomy to determine the presence and size of residual thyroid tissue for radioiodine ablation. Scattering degrades the quality of I-123 SPECT images, primarily due to the penetration of high-energy photons into the main photopeak. The objective of this study was to quantitatively and qualitatively investigate the impact of two widely used window-based scatter correction techniques, the dual energy window (DEW) and triple energy window (TEW) techniques, in I-123 postsurgical SPECT/CT thyroid imaging using an anthropomorphic phantom with small sizes of remnants and anatomically correct surrounding structures. For this purpose, non-scatter-corrected, DEW and TEW scatter-corrected SPECT/CT acquisitions were performed for 0.5-10 mL remnants within a phantom, with 0.5-12.6 MBq administered activities within the remnants, and without and with background-to-remnant activity ratios of 5% and 10%. The decrease in photons, the noise and non-uniformity in the background region due to scatter correction were measured, as well as the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) from small remnants. The images were also visually evaluated by two experienced nuclear medicine physicians. Scatter correction decreased photons to a higher extent in larger regions than smaller regions. Larger remnants yielded higher SNR and CNR values, particularly at lower background activities. It was found from the quantitative analysis and the qualitative evaluation that TEW scatter correction performed better than DEW scatter correction, particularly at higher background activities, while no significant differences were reported at lower background activities. Scatter correction should be applied in I-123 postsurgical SPECT/CT imaging to improve the image contrast and detectability of small remnants within the background.

Dual response multi-function smart window: An integrated system of thermochromic hydrogel and thermoelectric power generation module for simultaneous temperature regulation and power generation

Thermochromic smart windows have the capability to intelligently adjust color and light transmission based on ambient temperature and light levels, attracting considerable attention due to their potential to enhance building energy efficiency and residential comfort. However, excessive sunlight exposure can lead to increased heat generation, potentially affecting the service life of the window. In this work, we present the development of an efficient intelligent solar thermal utilization window, incorporating heat collection, temperature regulation, and power generation functionalities. The smart window achieves light modulation by adjusting the glycerol content in the thermochromic hydrogel. In addition, the thermoelectric material is arranged in series and parallel to the smart window functioning as the power generation module. The developed smart window shows excellent sunlight modulation capability (91.2 % visible light transmittance and 99.2 % sunlight shielding rate). Meanwhile, the response temperature of the window can be adjusted from 34.5 °C to 29 °C. The thermoelectric hydrogel can generate continuous and stable current (∼75 µA) with a temperature difference of 40 ℃. The practical application proves that the generated power by the temperature difference can fully meet the switching needs of small electrical appliances. This transformation framework between photothermal-electricity conversion deserves attention in the design of future smart windows.

Improved cement sheath thickness measurement and its application in cement sheath variation monitoring

Monitoring cement sheath thickness by the logging tool that utilizes Am–Be neutron generator as radiation source is crucial for the secure exploitation of oil and gas fields. Generally, a positive source distance probe is adopted to evaluate the cement sheath thickness by recording single energy window count, but the measurement result is greatly impacted by the working state of instrument. To get accurate changes about the thickness of cement sheath, this paper proposes a dual energy window combination method by photoelectric window and Compton backscatter window based on the scattering and attenuation theory of gamma ray in the medium. Subsequently, the relationship between the counting ratio of low window to high energy window and the thickness of cement sheath is established. By adjusting MCNP model settings, it can be concluded that the borehole fluid, the density of cement sheath, and the thickness of casing effects on the counting ratio of energy window A to energy window C for measuring cement sheath thickness can be disregarded. Besides, high-density formation and corrected borehole diameter are beneficial for evaluating the cement sheath thickness. According to these results, the simulation example shows that the thickness calculation error of the method at the overall cement sheath variations is less than 9%. The research result can provide reference for underground cement sheath monitoring and evaluation method.

Hyperspectral Anomaly Detection Using Spatial–Spectral-Based Union Dictionary and Improved Saliency Weight

Hyperspectral anomaly detection (HAD), which is widely used in military and civilian fields, aims to detect the pixels with large spectral deviation from the background. Recently, collaborative representation using union dictionary (CRUD) was proved to be effective for achieving HAD. However, the existing CRUD detectors generally only use the spatial or spectral information to construct the union dictionary (UD), which possibly causes a suboptimal performance and may be hard to use in actual scenarios. Additionally, the anomalies are treated as salient relative to the background in a hyperspectral image (HSI). In this article, a HAD method using spatial–spectral-based UD and improved saliency weight (SSUD-ISW) is proposed. To construct robust UD for each testing pixel, a spatial-based detector, a spectral-based detector and superpixel segmentation are jointly considered to yield the background set and anomaly set, which provides pure and representative pixels to form a robust UD. Differently from the conventional operation that uses the dual windows to construct the background dictionary in the local region and employs the RX detector to construct the anomaly dictionary in a global scope, we developed a robust UD construction strategy in a nonglobal range by sifting the pixels closest to the testing pixel from the background set and anomaly set to form the UD. With a preconstructed UD, a CRUD is performed, and the product of the anomaly dictionary and corresponding representation coefficient is explored to yield the response map. Moreover, an improved saliency weight is proposed to fully mine the saliency characteristic of the anomalies. To further improve the performance, the response map and saliency weight are combined with a nonlinear fusion strategy. Extensive experiments performed on five datasets (i.e., Salinas, Texas Coast, Gainesville, San Diego and SpecTIR datasets) demonstrate that the proposed SSUD-ISW detector achieves the satisfactory AUCdf values (i.e., 0.9988, 0.9986, 0.9939, 0.9945 and 0.9997), as compared to the comparative detectors whose best AUCdf values are 0.9938, 0.9956, 0.9833, 0.9919 and 0.9991.

CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate. However, the traditional CR presents a low utilization rate for deep latent features in hyperspectral images, making the dictionary construction and the optimization of weight matrix sub-optimal. Due to the excellent capacity of neural networks for generation, we formulate the deep learning-based method into CR optimization in both global and local streams, and propose a novel hyperspectral anomaly detection method based on collaborative representation neural networks (CRNN) in this paper. In order to gain a complete background dictionary and avoid the pollution of anomalies, the global dictionary is collected in the global stream by optimizing the dictionary atom loss, while the local background dictionary is obtained by using a sliding dual window. Based on the two dictionaries, our two-stream networks are trained to learn the global and local representation of hyperspectral data by optimizing the objective function of CR. The detection result is calculated by the fusion of residual maps of original and represented data in the two streams. In addition, an autoencoder is introduced to obtain the hidden feature considered as the dense expression of the original hyperspectral image, and a feature extraction network is concerned to further learn the comprehensive features. Compared with the shallow learning CR, the proposed CRNN learns the dictionary and the representation weight matrix in neural networks to increase the detection performance, and the fixed network parameters instead of the complex matrix operations in traditional CR bring a high inference efficiency. The experiments on six public hyperspectral datasets prove that our proposed CRNN presents the state-of-the-art performance.

A Curcumin‐Based MOF Embedded Ag NPs and Modified with Polydopamine for Dual‐Drug Delivery and Dual Biological Window Responsive Synergetic Cancer Therapy

AbstractA dual‐drug delivery, pH‐responsive composite nanoplatform (MAPD NPs) that can respond to two biological windows is developed to improve the efficacy of synergetic chemotherapic/photothermal/chemodynamic therapy (CDT) against tumors. This nanoplatform is surface‐modified polydopamine (PDA) with excellent biocompatibility as the shell and Ag NPs as the catalyst for CDT. The curcumin (Cur) acts as an organic ligand to be encapsulated in metal−biomolecule frameworks (Bio‐MOFs) by self‐assembly, and Bio‐MOF acts as a delivery carrier to deliver of DOX•HCl and then releases the Cur when it degrades in vivo. Moreover, Bio‐MOF can be taken up by cells faster and accelerate cell death compared to free Cur. PDA modification enables MAP (PDA@MOF‐Ag) to have photothermal properties under 808 and 1064 nm light irradiation, which not only improves the biocompatibility of MAP but also makes it produce high heat and abundant ·OH. The photothermal performance of MAP is stable after irradiation at 808 or 1064 nm, and the photothermal conversion efficiency reaches 63.57% and 26.25%. The survival rate of HeLa cells co‐incubation with MAPD NPs after irradiation at 808 and 1064 nm decreases to 19.52 ± 0.69% and 30.48 ± 0.49%, respectively, providing a feasible scheme for the realization of deep tumor killing.

Adaptive window based collaborative representation for hyperspectral anomaly detection with fusion of local and global information

Hyperspectral anomaly detection using collaborative representation (CR) has attracted high interest in recent years. Ignoring global information and the use of fixed dual window, which is inappropriate for targets with different sizes, are some disadvantages of the existing methods. In this paper, the adaptive window based CR, called as AWCR, is proposed, which utilizes the results of two segmentation maps with different numbers of superpixels to find appropriate size of inner and outer windows for each test pixel. In addition to local information contained in adaptive dual windows, two individual dictionaries are obtained for background and anomaly subspaces from the whole image to provide the global information. Both local and global residual terms are fused to result in the final residual term in AWCR. The experiments show high detection performance with a reasonable computation time for AWCR compared to several serious competitors.