Adaptive Window Research Articles

Influence of atmospheric clearness on PDLC switchable glazing transmission

Electrically activated switchable polymer dispersed liquid crystal (PDLC) is suitable for adaptive windows. A particular type requires 20 V to become 71% transparent while in the absence of power it is 27% transparent. Glazing transmission changes with light incident angle. As the clearness of a sky changes the fraction changes alter of direct insolation (that has an azimuthally changing incident) and diffuse insolation (that has a largely constant incident). Thus, the effective overall incident angle determining the glazing transmittance also changes. In this work for the first time, the variation of PDLC glazing transmission with clearness index has been investigated. For diffuse sky condition, single glazing transmittance value can be used below a particular clearness index for building energy calculation. This threshold clearness index changes with different azimuthal direction. In Dublin for south facing vertical plane PDLC glazing, yearly usable single transmittance (38% for transparent and 25% for translucent state), transmitted solar energy (TSE) (70 W/m2 for transparent state and 20 W/m2 for translucent state) and solar heat gain coefficient (SHGC) (0.17 for transparent state and 0.005 for translucent state) for transparent and translucent states were investigated.

Adaptive Contention Window Control Scheme in Wireless Ad Hoc Networks

The current contention mechanism of the IEEE 802.11 distributed coordination function (DCF) is known to have some drawbacks, e.g., collisions and low short-term fairness induced by minimizing the backoff stage after every successful transmission. To solve the drawbacks we propose an adaptive contention window control scheme which uses a single backoff stage while maintaining the advantages of the current IEEE 802.11 DCF such as simplicity and distributivity. We first introduce the key concept called the optimal collision probability and explain the detailed procedure of the proposed scheme which is based on the optimal collision probability. The convergence of the proposed scheme is shown. The validation from simulation results shows that the proposed scheme provides higher throughputs and higher short-term fairness than the IEEE 802.11 DCF.

Isophote-Constrained Autoregressive Model With Adaptive Window Extension for Image Interpolation

The autoregressive (AR) model is widely used in image interpolations. Traditional AR models consider utilizing the dependence between pixels to model the image signal. However, they ignore the valuable patch-level information for image modeling. In this paper, we propose to integrate both the pixel-level and patch-level information to depict the relationship between high-resolution and low-resolution pixels and obtain better image interpolation results. In particular, we propose an isophote-constrained AR (ICAR) model to perform AR-flavored interpolation within an identified joint stable region and further develop an AR interpolation with an adaptive window extension. Considering the smoothness along the isophote curve, the ICAR model searches only several successive similar patches along the isophote curve over a large region to construct an adaptive window. These overlapped patches, representing the patch-level structure similarity, are used to construct a joint AR model. To better characterize the piecewise stationarity and determine whether a pixel is suitable for AR estimation, we further propose pixel-level and patch-level similarity metrics and embed them into the ICAR model, introducing a weighted ICAR model. Comprehensive experiments demonstrate that our method can effectively reconstruct the edge structures and suppress jaggy or ringing artifacts. In the objective quality evaluation, our method achieves the best results in terms of both peak signal-to-noise ratio and structural similarity for both simple size doubling (two times) and for arbitrary scale enlargements.

Early antioxidant responses via the concerted activation of NF-κB and Nrf2 characterize the gamma-radiation-induced adaptive response in quiescent human peripheral blood mononuclear cells

The radiation-induced adaptive response (RI-AR) is a non-targeted effect which is outside the scope of the classical Linear-No-Threshold (LNT) dose-response paradigm. However, the mechanisms of the RI-AR are not well understood. We have studied the RI-AR in quiescent human peripheral blood mononuclear cells (PBMCs). PBMCs in G0 phase were ‘primed’ with a low dose (100 mGy gamma radiation) and then, after an ‘adaptive window’ of 4 h, ‘challenged’ with a high dose (2 Gy). A small (5.7%) increase in viability and a decrease in DNA strand breaks were seen in primed cells, compared to non-primed cells. This was consistent with lower levels of reactive oxygen species, higher mitochondrial membrane potential, and increased activity of antioxidant enzymes such as catalase, superoxide dismutase, thioredoxin reductase, and glutathione peroxidase, in the primed cells. Reduced oxidative stress in primed PBMCs correlated with greater nuclear translocation of the redox-sensitive transcription factors Nuclear factor kappa B (NF-κB) and Nuclear factor E2-related factor 2 (Nrf2). Distinct differences in responses were seen in PBMCs irradiated with low dose (100 mGy) and high dose (2 Gy). These findings provide insight into the mechanisms of radioadaptation in human cells.

Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window

ABSTRACTAutomatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This article presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples that follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from experimental synthetic aperture radar, electromagnetics institute synthetic aperture radar, and Radarsat-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.

Evaluation of Velocity Estimation Methods Based on Their Effect on Haptic Device Performance

This paper comparatively evaluates the effect of real-time velocity estimation methods on the passivity and fidelity of virtual walls implemented using haptic interfaces. Impedance width or $Z$ -width is a fundamental measure of performance in haptic devices. Limited accuracy of velocity estimates from position encoder data is an impediment in improving the $Z$ -width in haptic interfaces. We study the efficacy of Levant's differentiator as a velocity estimator to allow passive implementation of higher stiffness virtual walls as compared to some of the commonly used velocity estimators in the field of haptics. We first experimentally demonstrate feasibility of Levant's differentiator as a velocity estimator for haptics applications by comparing $Z$ -width performance achieved with Levant's differentiator and commonly used finite difference method (FDM) cascaded with a low-pass filter. A novel $Z$ -width plotting technique combining the passivity and fidelity of haptic rendering is proposed, and used to compare the haptic device performance obtained with Levant's differentiator, FDM+low-pass filter, first-order adaptive windowing (FOAW), and Kalman-filter-based velocity estimation methods. Simulations and experiments conducted on a custom single degree-of-freedom haptic device demonstrate that the stiffest virtual walls are rendered with velocity estimated using Levant's differentiator, and the highest wall rendering fidelity is achieved by FOAW-based velocity estimation scheme.

Machine learning based adaptive congestion window adjustment for Congestion Aware Routing in Cross Layer Approach Handling of Wireless Mesh Network

Among different researches on Wireless Mesh Networks (WMN), the Cross-layer Handling Link Asymmetry (CHLA) scheme has been enhanced with QoS-based Congestion Avoidance using Congestion Aware Routing (QSCACAR) technique. In this approach, QoS requirements were achieved by estimation of signal strength, network capacity, MAC scheduling, link scheduling and slot assignment. In addition, congestion was controlled based on the bandwidth management mechanism which is according to the congestion window on the dynamic traffic conditions. Based on these approaches, congestion over the network was controlled. However, the congestion window size was not precisely adjusted and selected by using these approaches. Therefore in this article, CHLA-QSCACAR is enhanced by including adaptive congestion window size based on the machine learning algorithm (CHLA-MQSCACAR). In this approach, congestion window size is predicted for adjusting the congestion in the next transmission. The Support Vector Machine (SVM) algorithm utilizes the congestion balance status as label and congestion control parameters as input attributes. Initially, congestion balance status of different transmissions and their corresponding input congestion control parameters are gathered. The collected database is trained with aid of SVM classifier. Then by using the trained SVM model, the congestion status of current transmission with congestion window size is predicted. The predicted congestion window is utilized for achieving congestion balance for the consecutive transmissions. Finally, the experimental results show that the performance effectiveness of the proposed CHLA-MQSCACAR compared to the existing CHLA-QSCACAR method in terms of different metrics such as routing overhead, bit error rate, end-to-end delay, throughput and latency.

Epipolar geometry and stereo matching algorithm for underwater fish-eye images

A fish-eye lens achieves a large field of view at the cost of image distortion, and underwater imaging by a fish-eye lens introduces refraction when light passes through the different medium. To obtain three-dimensional information, under these circumstances, a stereo matching algorithm is proposed that analyzes the geometric characteristics of an underwater fish-eye image, taking into account distortion and refraction. First, the underwater imaging model of a stereo fish-eye camera is established, and the epipolar curve of the underwater fish-eye image is calculated. Then, in the matching step, an adaptive window based on mean-shift segmentation is proposed to further alleviate the impact of distortion. Experiments are performed on synthetic images and natural scene images. The results show that the proposed model and the calculated epipolar curve are effective and that the adaptive window method could improve the precision of stereo matching on underwater fish-eye images.

Near-real-time stereo matching method using both cross-based support regions in stereo views

This paper presents a near-real-time stereo matching method using both cross-based support regions in stereo views. By applying the logical AND operator to the cross-based support region in the reference image and target image, we can obtain an intersection support region, which is used as an adaptive matching window. The proposed method aggregates absolute difference estimates in the intersection support region, which are combined with the census transform results. The census transform with a fixed window size and shape is applied, and only the resultant binary code of the pixel in the intersection support region is used. From Middlebury images and their ground truth disparity maps, we compute the area similarity ratio of support regions in stereo views. Then, a conditional probability of observing a correct disparity estimate with respect to the area similarity ratio is examined. By taking a natural logarithm of the probability, a relative reliability weight about the area similarity of support regions is obtained. The initial matching cost is then combined with the reliability weight to obtain the final cost, and the disparity with the minimum cost is chosen as the final disparity estimate. Experimental results demonstrate that the proposed method can estimate accurate disparity maps.

Topography of activation deficits in schizophrenia during P300 task related to cognition and structural connectivity.

The study of cerebral underpinnings of schizophrenia may benefit from the high temporal resolution of electromagnetic techniques, but its spatial resolution is low. However, source imaging approaches such as low-resolution brain electromagnetic tomography (LORETA) allow for an acceptable compromise between spatial and temporal resolutions. We combined LORETA with 32 channels and 3-Tesla diffusion magnetic resonance (Dmr) to study cerebral dysfunction in 38 schizophrenia patients (17 first episodes, FE), compared to 53 healthy controls. The EEG was acquired with subjects performing an odd-ball task. Analyses included an adaptive window of interest to take into account the interindividual variability of P300 latency. We compared source activation patters to distractor (P3a) and target (P3b) tones within- and between-groups. Patients showed a reduced activation in anterior cingulate and lateral and medial prefrontal cortices, as well as inferior/orbital frontal regions. This was also found in the FE patients alone. The activation was directly related to IQ in the patients and controls and to working memory performance in controls. Symptoms were unrelated to source activation. Fractional anisotropy in the tracts connecting lateral prefrontal and anterior cingulate regions predicted source activation in these regions in the patients. These results replicate the source activation deficit found in a previous study with smaller sample size and a lower number of sensors and suggest an association between structural connectivity deficits and functional alterations.

Design of a High Efficiency DC–DC Buck Converter With Two-Step Digital PWM and Low Power Self-Tracking Zero Current Detector for IoT Applications

In this paper, a high efficiency dc–dc buck converter with two-step digital pulse width modulation (DPWM) and low power self-tracking zero current detector (ST-ZCD) is proposed for Internet of Things (IoT) and ultralow power applications. The hybrid DPWM core with high linearity and low power consumption is proposed to implement the high efficiency DPWM dc–dc converter. It is composed of a two-step delay control using the counter and delay line. An adaptive window analog to digital converter is proposed to reduce the output voltage ripple within 20 mV. A dead time generator is implemented with the proposed ST-ZCD to minimize the reverse current. The ST-ZCD can improve efficiency by reducing the control loss that accounts for a large proportion of the dc–dc converter. Also, all digital type-III compensator is implemented for the low power and small die area. This chip is fabricated with a 55 nm CMOS process, which uses the standard supply voltage of 1.5–3 V to generate the output voltage of 1.2 V. The total active area is 500 μm × 300 μm. The measured peak efficiency of the DPWM dc–dc buck converter is 91.5% with a quiescent current consuming only 130 μA.

Phase unwrapping algorithm using polynomial phase approximation and linear Kalman filter.

A noise-robust phase unwrapping algorithm is proposed based on state space analysis and polynomial phase approximation using wrapped phase measurement. The true phase is approximated as a two-dimensional first order polynomial function within a small sized window around each pixel. The estimates of polynomial coefficients provide the measurement of phase and local fringe frequencies. A state space representation of spatial phase evolution and the wrapped phase measurement is considered with the state vector consisting of polynomial coefficients as its elements. Instead of using the traditional nonlinear Kalman filter for the purpose of state estimation, we propose to use the linear Kalman filter operating directly with the wrapped phase measurement. The adaptive window width is selected at each pixel based on the local fringe density to strike a balance between the computation time and the noise robustness. In order to retrieve the unwrapped phase, either a line-scanning approach or a quality guided strategy of pixel selection is used depending on the underlying continuous or discontinuous phase distribution, respectively. Simulation and experimental results are provided to demonstrate the applicability of the proposed method.

Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal

The goal of this paper is to find an excellent adaptive window function for extracting the weak vibration signal and high frequency vibration signal under strong noise. The relationship between windowing transform and filtering is analyzed first in the paper. The advantage of adjustable time-frequency window of wavelet transform is introduced. Secondly the relationship between harmonic wavelet and multiple analytic band-pass filter is analyzed. The coherence of the multiple analytic band-pass filter and harmonic wavelet base function is discussed, and the characteristic that multiple analytic band-pass filter included in the harmonic wavelet transform is founded. Thirdly, by extending the harmonic wavelet transform, the concept of the adaptive harmonic window and its theoretical equation without decomposition are put forward in this paper. Then comparing with the Hanning window, the good performance of restraining side-lobe leakage possessed by adaptive harmonic window is shown, and the adaptive characteristics of window width changing and analytical center moving of the adaptive harmonic window are presented. Finally, the proposed adaptive harmonic window is applied to weak signal extraction and high frequency orbit extraction of high speed rotor under strong noise, and the satisfactory results are achieved. The application results show that the adaptive harmonic window function can be successfully applied to the actual engineering signal processing.

Wireless Networking Performance in IoT Using Adaptive Contention Window

Internet of Things (IoT) network contains heterogeneous resource‐constrained computing devices which has its unique reputation in IoT environments. In spite of its distinctiveness, the network performance deteriorates by the distributed contention of the nodes for the shared wireless medium in IoT. In IoT network, the Medium Access Control (MAC) layer contention impacts the level of congestion at the transport layer. Further, the increasing node contention at the MAC layer increases link layer frame drops resulting in timeouts at the transport layer segments and the performance of TCP degrades. In addition to that, the expiration of maximum retransmission attempts and the high contentions drive the MAC retransmissions and the associated overheads to reduce the link level throughput and the packet delivery ratio. In order to deal with aforementioned problems, the Adaptive Contention Window (ACW) is proposed, which aims to reduce the MAC overhead and retransmissions by determining active queue size at the contending nodes and the energy level of the nodes to improve TCP performance. Further, the MAC contention window is adjusted according to the node’s active queue size and the residual energy and TCP congestion window is dynamically adjusted based on the MAC contention window. Hence, by adjusting the MAC Adaptive Contention Window, the proposed model effectively distributes the access to medium and assures improved network throughput. Finally, the simulation study implemented through ns‐2 is compared with an existing methodology such as Cross‐Layer Congestion Control and dynamic window adaptation (CC‐BADWA); the proposed model enhances the network throughput with the minimal collisions.

Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database

A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).

Automatic feature extraction of ECG signal based on adaptive window dependent differential histogram approach and validation with CSE database

A very simple and novel idea based on adaptive window dependent differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalised signal has been divided into a few small windows by an adaptive window selection technique. By counting the number of changes between successive samples as frequency, the differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. The proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in sensitivity (99.86%), positive productivity (99.76%) and detection accuracy (99.8%).

An Adaptive Contention Window Scheme for 802.11ah WLANs

In order to cope with the rapid development of the Internet of things, IEEE working group has proposed a new wireless network technology, 802.11ah. IEEE 802.11ah operates at sub 1GHz band, and can provide long transmission range, wide coverage, while supporting more than 6,000 of station to be connected. In order to alleviate the high collision probability, 802.11ah MAC protocol involves in the RAW (restricted access window) mechanism combined with grouping mechanism. However, in 802.11ah WLAN (wireless local area network), most of the stations operate at sleep mode, and it is expected that at the start of a BI, the network suffers much higher collision probability than at the latter period of the BI. In order to improve these drawbacks, we propose an adaptive contention window scheme based on which an optimum contention window size is adapted at the start of the BI, and then gradually halved when the transmission completes successfully. Through conducting simulations, we prove that our proposed protocol can enhance the performance of 802.11ah WLAN.

A Path Planning and Navigation Control System Design for Driverless Electric Bus

Extensive studies have been conducted on unmanned vehicle recently due to its bright prospect. However, few of them have studied the technology of driverless bus. In this paper, a path planning and navigation control system is designed for 12-m length driverless electric bus. The global path planning is adopted by the Dijkstra algorithm based on the analysis tool of ArcGIS. For local planning, a triple-layer finite-state machine is proposed to plan the driving maneuver, and it reduces computational complexity and improves driving effectiveness. Then, optimal trajectory is generated by a curve fitting technique based on path planning and driving maneuver, which takes full account of the safety and kinetics for driverless bus. In addition, we address the speed planning by expert rules to imitate an experienced driver. Finally, the trajectory tracking controller is devised by an adaptive rolling window. Moreover, a heading error compensation model and a self-adaptive strategy of controller parameters are set up to improve the control precision. Finally, simulation by TruckSim and scenes experiments verify the validity and practicability of our proposed path planning and navigation control system for driverless bus.

A Segmented Consistency Check Approach to Disparity Map Refinement

In stereo vision field, image processing techniques are used to increase the robustness of some types of solutions, especially those involved in real-world applications. With the objective to improve raw disparity maps, we investigated strategies of how to reduce the error measurement between a map and its correspondent ground truth. In this paper, we propose a simple but an effective technique, named as segment consistency check (SCC), to adjust a disparity map in a more appropriate configuration. The SCC method is organized into three main steps: segmentation process, statistical analysis, and by using adaptive support window. Experiment results show that the proposed method is comparable with other postprocessing strategies, in some cases with better results. Furthermore, through a qualitative evaluation, it is possible to note that the SCC method reaches significant results, especially in retaining object shapes, in maintaining the regularity of disparity in uniformed areas, and to preserve discontinuities.

Билинейные масштабно-временные распределения энергии аффинного класса в частотно-временном пространстве (продолжение обзора)

The review of theoretical bases of synthesis of scale-time distributions of an affine class is presented. The methodology of wavelet transforms with an adaptive analysis window is analyzed in the interest of processing processes with high parameter non-stationarity. Analytic approaches to synthesis of distributions invariant to affine shifts are generalized. The generality of the scale-time and frequency-time distributions is shown. Analytical expressions and experimental results of the most known forms of affine class distributions are given.