Window Features Research Articles

Indoor–Outdoor Point Cloud Alignment Using Semantic–Geometric Descriptor

Aligning indoor and outdoor point clouds is a challenging problem since the overlapping area is usually limited, thus resulting in a lack of correspondence features. The windows and doors can be observed from both sides and are usually utilized as shared features to make connections between indoor and outdoor models. However, the registration performance using the geometric features of windows and doors is limited due to the considerable number of extracted features and the mismatch of similar features. This paper proposed an indoor/outdoor alignment framework with a semantic feature matching method to solve the problem. After identifying the 3D window and door instances from the point clouds, a novel semantic–geometric descriptor (SGD) is proposed to describe the semantic information and the spatial distribution pattern of the instances. The best object match is identified with an improved Hungarian algorithm using indoor and outdoor SGDs. The matching method is effective even when the numbers of objects are not equal in the indoor and outdoor models, which is robust to measurement occlusions and feature outliers. The experimental results conducted in the collected dataset and the public dataset demonstrated that the proposed method could identify accurate object matches under complicated conditions, and the alignment accuracy reached the centimeter level.

The effects of window blind positions and control on patients’ hospital and care quality perception: A mediation and moderation analysis

Previous research has demonstrated the benefits of access to daylight and views on patient experience and outcomes. However, there is a lack of research investigating the relationship between window features, such as blinds, and patient experience as well as the mediating impacts of daylight quality versus access to views on the relationship between these variables. Using a nationwide online survey, 652 participants rated daylight satisfaction, view access, experience of room, hospital, and care quality based on their most recent inpatient hospital stay. Participants were also asked to rate their satisfaction based on images of patient rooms with and without blinds. Findings revealed that participants who stayed in rooms with open blinds reported significantly higher ratings for room, hospital, and care quality experience. Also, the relationship between these ratings and blind positions was mediated by ratings of daylight and view access. This study can help healthcare designers and stakeholders improve patient experience through optimizing daylight and view access in patient rooms.

Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning.

In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combining eye movement features and scene features. This method uses an adaptive window to partition samples and enhances sample features through fine-grained feature extraction. Using an adaptive window to partition samples can make the eye movement information in the sample more abundant, and fine-grained feature extraction from an adaptive window can increase discrimination between samples. After adopting the method proposed in this paper, the four-category emotion recognition accuracy of the single modality of eye movement reached 65.1% in MOOC learning scenarios. Both the adaptive window partition method and the fine-grained feature extraction method based on eye movement signals proposed in this paper can be applied to other modalities.

A Segmentation Scheme for Knowledge Discovery in Human Activity Spotting.

Spotting the target activity in a sequence of activities and transitions without applying a predetermined window size is a challenging task. The sliding window method, which is the typical approach in segmenting the continuous data stream, operates with optimal segment sizes chosen considering the type and duration of the activities. Nevertheless, activity type and duration are usually unknown to the detection unit in practice. In this study, we proposed the nonpredetermined size windowing (NSW) scheme to spot the target activity performed in a sequence of unseen activities. NSW is built on classifying progress-based features in multilayer training and prediction stages where the time-domain progress is expressed in terms of polynomials. Thus, it operates without incorporating the information regarding duration and type of the activities. We verified our method with a proof-of-concept use case where data are acquired by a single wrist-worn 3-D accelerometer. We compared our method against fixed size windowing performed with varying window sizes and feature extraction schemes; windowed energy, peak frequency, Shannon entropy, and wavelet entropy. Our method outperforms the compared schemes, reaching a median accuracy of 89% and a median true positive rate of 1 in both intrasubject and intersubject cases based on an independent test set where each test instance contains a sequence of nine different activities.

Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction

Surface grinding, being one of the later stages of a manufacturing process, is crucial to achieve the desired workpiece quality by maintaining proper wheel condition. Machine learning and acoustic emission (AE) detection have demonstrated potentials for developing an effective on-line wheel wear monitoring system. However there are inconsistencies among existing studies regarding to signal sampling and feature extraction methods. In the present paper, surface grinding experiments using medium carbon steel workpieces were conducted to determine the effects of using different signal analysis window lengths, feature types, and AE sensor bandwidths on the wheel wear detection performances. In addition, the use of AE transient spikes occurred during wheel entry and exit were analyzed. Feature were extracted from time domain (TD), frequency domain (FD), and time–frequency domain (TFD) representations of the measured AE signals, which were then refined using sequential floating forward selection (SFFS) algorithm to reduce feature redundancy. Support vector machine (SVM) algorithm with linear kernel was used for pattern classification. A number of insights for future monitoring system development were obtained. Firstly, while the lower frequency AE sensor (below 100 kHz) provided adequate classification performances, the higher frequency AE sensor (above 100 kHz) produced more robust results. Secondly, 100% classification rate was achievable using features derived from Fast Fourier Transform (FFT) in combination with descriptive statistics. Thirdly, it was determined that a window length as short as 1 ms (1000 data points) could capture enough signal information using FFT with the higher frequency AE sensor. Lastly, the choice sampling instance did not significantly impact classification outcomes.

Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network

Anomaly detection of time series data is one of the most important problems in industrial applications. Most of methods are difficult to obtain rich features of samples due to the lack of dimension. In this paper, we develop an anomaly detection method for time series data based on multi-granularity neighbor residual network (MGNRN). First, we construct a neighbor input vector with a sliding time window for each data sample, and define neighbor-based input matrix by considering multi-granularity neighborhood features. Second, we compute the linear and non-linear neighbor features of multi-granularity time windows for the sample. Finally, by combining the linear neighborhood residual with nonlinear residual, we predict the abnormal probability of the sample. Experiments verify the multi-granularity neighbor residual network improves the accuracy of abnormal detection, and show good performance on precision and F1 metrics.

Flexible Normally Transparent Smart Window Modulating Near‐Infrared Penetration under Ambient Conditions

AbstractSmart windows that both reversibly occlude visible light and modulate the penetration of near‐infrared (NIR) light save a great deal of energy. Here, a simple, super‐fast, electrically switchable smart window that initially reflects NIR light and then scatters it in response to thermal stimuli is designed and fabricated. Transparent polymer‐stabilized cholesteric liquid crystals (PSCLCs) are prepared by photo‐polymerizing RM 257 monomers in which the planar CLCs fully reflected NIR light (1050–1550 nm). Next, an ultra‐thin, thermotropic, W0.003V0.997O2‐doped polyvinylpyrrolidone hybrid thin layer that modulates NIR light penetration over a very broad temperature range is overlaid on the PSCLCs, and the final WVO‐PSCLCs smart window features four NIR modulation modes. Specifically, when a model wood house with three 1.5‐ × 2‐cm WVO‐PSCLCs smart windows is thermally irradiated by a 250‐W infrared light for 10 min, the internal temperature is 4.8 °C less than that of a house lacking smart windows. Flexible smart windows fabricated from polyethylene terephthalate (PET) could be bent through a radius of 15 mm without any noticeable damage, and are very resistant to external vertical pressure. The flexible WVO‐PSCLCs smart windows are versatile, and afford outstanding transparency management and modulation of NIR light penetration.

A trajectory map matching algorithm via exploring the average directional features of continuous windows

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

Online interaction method of mobile robot based on single-channel EEG signal and end-to-end CNN with residual block model

The development of alternative pathways to communicate with outside world independent on language or limb motions is important. However, one of the challenges is the multi-dimensional and accurate control of robot using head signals. This paper proposes an end-to-end CNN with residual block model, which uses the filtered raw signal as input and detects left eye blink, right eye blink, continuous eye blink, and grit teeth tasks, and develops an online brain–computer interface (BCI) system to control the left turn, right turn, forward, stop, and speed of the mobile robot platform, TurtleBot. The portable EEG measurement device TGAM module is used to collect single channel dry electrode EEG signals. Through time domain (TD) and time–frequency analysis, the time period and frequency range of each task signal are analyzed, which lays the foundation for analysis window parameter determination and feature extraction. The proposed model uses one-dimensional convolution to extract local features and two-dimensional convolution to extract global features, and the average detection accuracy is 97.399%, which is significantly higher than that of the state-of-the-art machine learning classifiers with the TD and frequency domain (FD) fusion features as input (p<0.01). The detection performance of FD features outperforms the TD features (p<0.01). Online BCI system based on the proposed method is developed to interact with the TurtleBot.

An Improved Unsegmented Phonocardiogram Classification Using Nonlinear Time Scattering Features

Abstract Phonocardiogram (PCG) signals highlight the relevant characteristics for the prediction of heart diseases or heart-related disorders. However, it is challenging to classify heart abnormality relying on an unbalanced PCG dataset due to low classification performance. Recently, several studies have attempted to predict heart abnormality based on segmented and unsegmented features extracted using PCG signals. This study aims to develop an automated PCG classification model eliminating any segmentation of the heart sound signal for predicting heart abnormality. So, we have proposed a new approach based on wavelet scattering transform to predict two classes of PCG signals, namely, normal and abnormal. Based on the wavelet scattering transform, five scattering time window features were extracted from each PCG signal. The PhysioNet 2016 PCG database has been used here to evaluate and compare the classification performance based on the k Nearest Neighbors (KNN) classifier. The proposed architecture used a KNN classifier with different distance functions (Euclidean, Cityblock, Chebyshev, Minkowsky, Correlation, Spearman and Cosine) and has been compared with other traditional classifiers (classification tree, linear discriminant analysis, support vector machine and ensemble). The proposed framework using nonlinear wavelet scattering features with a KNN classifier based Cityblock distance function achieved classification performance over the total datasets with accuracy, sensitivity and specificity values of 97.82%, 95.04% and 98.72%, respectively.

Network Anomaly Sequence Prediction Method Based on LSTM and Two-layer Window Features

To solve the over-fitting problem in the prediction algorithm caused by the small number of features that arise during the network anomaly prediction process, an LSTM algorithm for network anomaly predictions based on two-layer time window features was proposed. Firstly, the network alarm data sequence was divided according to the observation time window and prediction time window. Secondly, considering that the time series of the anomaly alarm data can be somewhat periodic, a time window sequence dataset was created with the periodic features and statistical features in the two-layer windows. Finally, one-shot and feedback models of the LSTM algorithm were employed to predict network anomalies. The experiment showed that the best prediction accuracy for this method is over 80% with both one-shot and feedback models, when the prediction time window is 12h.

Large-scale Robotic Fabrication of Polychromatic Relief Glass

This research investigates a new digital fabrication method for large-scale polychromatic glass elements. Glass elements with locally differentiated properties usually require manual labor or are limited to film applications of secondary materials that are incapable of producing material texture and relief in glass. To create mono-material glass elements for buildings with customized color, opacity, and relief present in the same glass element, this research investigates a novel robotic multi-channel printing process for industrial float glass. Mono-material polychromatic glasses do not require any additional material and can be fully recycled. This paper presents a design-to-production workflow for the construction scale within feasible cost. Investigations include kilning and material considerations, multi-channel tool and fabrication setup, tool path generation, process parameter calibration, and large-scale prototyping. The co-occurrence of locally varying opacities, colors, material textures, and relief within one glass element enabled by the presented robotic fabrication method could allow for novel optical and decorative features in facades and windows.

Lighting conditions in physiotherapy centres: A comparative field study

Visual comfort plays a central role in building occupants’ comfort, well-being and productivity. It is therefore fundamental to meet the occupants’ visual and lighting needs, carefully accounting for the room layout, usage and activities. In this framework, physiotherapy centres constitute a peculiar case since they are occupied by therapists and patients from different age groups, engaged in various activities performed in different positions in the rooms, and affected by different health issues. Lighting quality and satisfaction were monitored in four physiotherapy centres in Northern Italy and Denmark. Subjective assessments were compared with objective illuminance measurements at the task areas, and the sites were also analysed in terms of window and architectural features. Moreover, the interaction between visual and other environmental perceptions was studied. The results revealed: (1) positive influence of daylight access on the satisfaction of occupants; (2) occupants’ complaints about low lighting levels associated with artificial lighting being unable to provide 300 lx and natural light not balancing this lack of illumination; and (3) lower satisfaction among therapists, who also showed the tendency to assess their environmental perception more globally, since correlations between light and daylight satisfaction and other stimuli were observed when comfort issues were present.

A Ratio-Difference Local Feature Contrast Method for Infrared Small Target Detection

Local contrast has been proven to be effective in infrared (IR) small target detection, but existing algorithms still have some drawbacks. In this letter, a ratio-difference (RD) local feature contrast (LFC) method is proposed. First, a new nested window is designed, and the local gray contrast is selected as the features of inner and outer windows to investigate the characteristics of a local area. Then, the RD LFC is calculated. At last, a simple but useful weighting function is proposed. Experiments show that the proposed algorithm can achieve better performance in terms of detection rate and false alarm rate and has good robustness against noise.

DATA AUGMENTATION FOR FMRI-BASED FUNCTIONAL CONNECTIVITY AND ITS APPLICATION TO CROSS-SITE ADHD CLASSIFICATION

Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is widely used to study brain function and disorders due to its advantages of non-invasiveness, no radiation damage, and high spatial resolution. Existing studies have focused on fMRI-based recognition models to help diagnose brain disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. In addition, cross-site classification is always a challenge in fMRI study, because the heterogeneity of data collection at different sites increases the complexity and diversity of the data distribution, making the cross-site classification less robust than site-specific classification. In this paper, we propose three data augmentation methods based on functional connectivity networks (FCNs) of fMRI data, aided by a deep feature fusion method, for automatic disease identification. Firstly, Gaussian noise method, Mixup method, and sliding window method are proposed to effectively augment FCN data, respectively, this can balance the variability of sample distribution. Secondly, convolution neural network and graph attention network are separately employed to extract local and global features from FCN. Finally, the two kinds of features are integrated to classify subjects. The experimental results on the ADHD-200 dataset indicate that: (1) the data augmentation methods can effectively improve identification performance, in particular, the sliding window method performs best; (2) the cross-site attention deficit and hyperactivity disorder (ADHD) classification is improved by combining the data augmentation method of sliding window and deep feature fusion method; (3) the rationality of data augmentation for FCNs is explained by visualizing the hidden fused features with t-stochastic neighborhood embedding algorithm.

Using Discrete Choice Methodology to Explore the Impact of Patient Room Window Design on Hospital Choice.

Evidence-based design has been fundamental to designing healthcare environments for patient outcomes and experience, yet few studies have studied how design factors drive patient choice. 652 patients who recently received care at hospitals across the United States were administered an online discrete choice survey to investigate the factors playing into their choice between hypothetical hospitals. Discrete choice models are widely used to model patient preferences among treatment alternatives, but few studies have utilized this approach to investigate healthcare design alternatives. In the current study, respondents were asked to choose between hypothetical hospitals that differed in patient room design, window features of the room, appointment availability, distance from home, insurance coverage, and HCAHPS ratings. The results demonstrate that patient room design that allowed unobscured access to daylight and views through windows, in-network insurance coverage, closer distance from home, and one-star higher patient experience rating increased the likelihood of a patient's hospital choice. The study broadly explores discrete choice model's applicability to healthcare design and its ability to quantify patient perceptions with a metric meaningful for hospital administrators.

AFGL-Net: Attentive Fusion of Global and Local Deep Features for Building Façades Parsing

In this paper, we propose a deep learning framework, namely AFGL-Net to achieve building façade parsing, i.e., obtaining the semantics of small components of building façade, such as windows and doors. To this end, we present an autoencoder embedding position and direction encoding for local feature encoding. The autoencoder enhances the local feature aggregation and augments the representation of skeleton features of windows and doors. We also integrate the Transformer into AFGL-Net to infer the geometric shapes and structural arrangements of façade components and capture the global contextual features. These global features can help recognize inapparent windows/doors from the façade points corrupted with noise, outliers, occlusions, and irregularities. The attention-based feature fusion mechanism is finally employed to obtain more informative features by simultaneously considering local geometric details and the global contexts. The proposed AFGL-Net is comprehensively evaluated on Dublin and RueMonge2014 benchmarks, achieving 67.02% and 59.80% mIoU, respectively. We also demonstrate the superiority of the proposed AFGL-Net by comparing with the state-of-the-art methods and various ablation studies.

Fiber Fabry-Perot astrophotonic correlation spectroscopy for remote gas identification and radial velocity measurements.

We present a novel, to the best of our knowledge, remote gas detection and identification technique based on correlation spectroscopy with a piezoelectric tunable fiber-optic Fabry-Perot filter. We show that the spectral correlation amplitude between the filter transmission window and gas absorption features is related to the gas absorption optical depth, and that different gases can be distinguished from one another using their correlation signal phase. Using a previously captured telluric-corrected high-resolution near-infrared spectrum of Venus, we show that the radial velocity of Venus can be extracted from the phase of higher order harmonic lock-in signals. This correlation spectroscopy technique has applications in the detection and radial velocity determination of weak spectral features in astronomy and remote sensing. We experimentally demonstrate a remote CO2 detection system using a lock-in amplifier, fiber-optic Fabry-Perot filter, and single channel avalanche photodiode.

An overlapping sliding window and combined features based emotion recognition system for EEG signals

Purpose The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data. Design/methodology/approach Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features. Findings The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches. Originality/value The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features

Diabetic retinopathy (DR) is one of the diseases that cause blindness globally. Untreated accumulation of fat and cholesterol may trigger atherosclerosis in the diabetic patient, which may obstruct blood vessels. Retinal fundus images are used as diagnostic tools to screen abnormalities linked to diseases that affect the eye. Blurriness and low contrast are major problems when segmenting retinal fundus images. This article proposes an algorithm to segment and detect hemorrhages in retinal fundus images. The proposed method first performs preprocessing on retinal fundus images. Then a novel smart windowing-based adaptive threshold is utilized to segment hemorrhages. Finally, conventional and hand-crafted features are extracted from each candidate and classified by a support vector machine. Two datasets are used to evaluate the algorithms. Precision rate (P), recall rate (R), and F1 score are used for quantitative evaluation of segmentation methods. Mean square error, peak signal to noise ratio, information entropy, and contrast are also used to evaluate preprocessing method. The proposed method achieves a high F1 score with 83.85% for the DIARETDB1 image dataset and 72.25% for the DIARETDB0 image dataset. The proposed algorithm adequately adapts when compared with conventional algorithms, hence will act as a tool for segmentation.