text:
# Vol. 18 (2009):   Abstracts of Papers

No. 1,
No. 2: Special Issue on Special Issue on Application of Novel Classification Methods in Computer Vision,
No. 3,
No. 4.

Reprints of the papers may be obtained from their authors; contact Editorial Office in case you need the address of the respective author.
Editorial Office, MGV
Institute of Computer Science
ul. Ordona 21
01-237 Warszawa, Poland

<mgv@ipipan.waw.pl>

## Machine GRAPHICS & VISION, Vol. 18 (2009), No. 1:

Jourdan G.-V., Rakotomalala L., Zaguia N.:
LR-Upward Drawing of Ordered Sets.
MGV vol. 18, no. 1, 2009, pp. 3-19.
In this paper we introduce a new concept to draw an ordered set: the \textit{LR-Upward} drawing is an upward drawing based on a chain decomposition of the order such that elements drawn on the same vertical line are always comparable and all other comparabilities flow from left to right. We describe a particular technique for automatic generation of an enhanced \textit{LR-Upward} drawing for \textit{N-Free} orders that are \textit{X-Cycle-Free}. This technique first enhances the drawing locally, around a particular chain, and then expands the enhancement to the remaining part of the order. A Java-based implementation is also presented.
Key words: Ordered sets, Ordered sets visualization, Upward drawings, N-Free, X-Cycle-Free.

Beszédes M., Culverhouse P., Oravec M.:
Facial Emotion Classification Using Active Appearance Model and Support Vector Machine Classifier.
MGV vol. 18, no. 1, 2009, pp. 21-46.
Automatic analysis of human face expression is an interesting and non-trivial problem. In the last decade, many approaches have been described for emotion recognition based on analysis of facial expression. However, little has been done in the sub-area of the recognition of facial emotion intensity levels. This paper proposes the analysis of the use of Active Appearance Models (AAMs) and Support Vector Machine (SVM) classifiers in the recognition of human facial emotion and emotion intensity levels. AAMs are known as a tool for statistical modeling of object shape/appearance or for precise object feature detection. In our case, we examine their properties as a technique for feature extraction. We analyze the influence of various facial feature data types (shape / texture / combined AAM parameter vectors) and the size of facial images on the final classification accuracy. Then, approaches to proper C-SVM classifiers (RBF kernel) training parameter adjustment are described. Moreover, an alternative way of classification accuracy evaluation using the human visual system as a reference point is discussed. Unlike the usual to the approach evaluation of recognition algorithms (based on comparison of final classification accuracies), the proposed evaluation schema is independent of the testing set parameters, such as number, age and gender of subjects or the intensity of their emotions. Finally, we show that our automatic system gives emotion categories for images more consistent labels than human subjects, while humans are more consistent in identifying emotion intensity level compared to our system.
Key words: automatic facial emotion recognition, active appearance model, support vector machine, psychological experiment, cross-validation.

Shahbe M.D., Hati S.:
Kernel Based Subspace Methods: Infrared vs Visible Face Recognition.
MGV vol. 18, no. 1, 2009, pp. 47-66.
This paper investigates the use of kernel theory in two well-known, linear-based subspace representations: Principle Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (FLD). The kernel-based method provides subspaces of high-dimensional feature spaces induced by some nonlinear mappings. The focus of this work is to evaluate the performances of Kernel Principle Component Analysis (KPCA) and Kernel Fisher's Linear Discriminant Analysis (KFLD) for infrared (IR) and visible face recognition. The performance of the kernel-based subspace methods is compared with that of the conventional linear algorithms: PCA and FLD. The main contribution of this paper is the evaluation of the sensitivities of both IR and visible face images to illumination conditions, facial expressions and facial occlusions caused by eyeglasses using the kernel-based subspace methods.
Key words: Face Recognition; Principle Component Analysis; Linear Discriminant Analysis, Kernel Methods.

Holinka S., Sára R., Smutek D.:
Relation Between Structural Changes in B-mode Ultrasound Images of Thyroid Parenchyma and the Presence of Thyroid Antibodies in Blood Sample.
MGV vol. 18, no. 1, 2009, pp. 67-82.
Ultrasonography is a cheap and quick non-invasive medical imaging technique, used as a diagnostic method for autoimmune thyroiditis. Another important diagnostic method for this chronic inflammation is measuring the increased level of thyroid antibodies (anti-thyroid peroxidase (TPOAb) and/or anti- thyroglobulin (TgAb)) in blood samples. This paper shows that B-mode ultrasound images contain weak information related to the presence or absence of these antibodies. Ultrasound image analysis is based on textural recognition using probabilistic spatial features. Two studies are performed. The results of the first study show that the spatial texture features we used contain weak information about the presence or absence of TPOAb and TgAb antibodies as measured by conditional entropy. In the second study, a classifier derived from Bayesian decision theory is tested on a set of 2820 sonograms of 94 subjects. The training set contains 67 subjects and the test set consists of 27 independent subjects. The results of classification to three classes (healthy, thyroiditis with positive antibody test, thyroiditis with negative antibody test) achieved sensitivity 29% and specificity 100% on the test set.
Key words: B-mode sonography, texture analysis, classification, thyroid gland, autoimmune lymphocytic thyroiditis, thyroid antibodies.

Aouat S., Larabi S.:
Comparison of Detailed Descriptors for Noisy Silhouettes.
MGV vol. 18, no. 1, 2009, pp. 83-104.
In this paper we propose a new method for comparing silhouettes. Silhouettes of 3D objects, extracted from 2D images, are described using the LWDOS language \cite {1}. Sometimes, even though the silhouettes appear identical, their LWDOS detailed descriptors may be very different; therefore, we might wrongly conclude that the two silhouettes are different. In order to eliminate this problem which is due to several phenomena, we try to deduce the same detailed descriptor from two slightly different descriptors of two silhouettes.
Key words: Detailed Descriptor, Silhouette, LWDOS, Comparison.

Sharma A., Sharma R.K., Kumar R.:
Online Preprocessing of Handwritten Gurmukhi Strokes.
MGV vol. 18, no. 1, 2009, pp. 105-120.
In this paper, the authors have implemented preprocessing algorithms for online handwritten Gurmukhi strokes in order to find the improvements in recognition of four high-level features (loop, headline, straight line and dot) of Gurmukhi strokes. Preprocessing algorithms include size normalization and centering, interpolating missing points, smoothing, slant correction and resampling of points. Recognition algorithms for the above mentioned four high-level features are also introduced in this paper. Experiments have been conducted across 60 writers and 5%, 3.33%, 6.66% and 8.34% improvements have been observed for recognition of loop, headline, straight line and dot features, respectively, after using preprocessing algorithms.
Key words: Recognition, Preprocessing, High-Level features.

## Machine GRAPHICS & VISION, Vol. 18 (2009), No. 2:

Special Issue on Application of Novel Classification Methods in Computer Vision
Special Issue Editor: Katarzyna Stapor.

Koczkodaj W.W., Robidoux N., Tadeusiewicz R.:
Classifying visual objects with the consistency-driven pairwise comparisons method.
MGV vol. 18, no. 2, 2009, pp. 143-153.
The classification of the various image features or visual objects can be carried out by the consistency-driven pairwise comparisons method based on their relative importance. A key issue in the proposed approach is a weight-based synthesis for combining various image features. When compared with the traditional experience-based linear assignment method, the proposed approach is more effective and easy to communicate.
Key words: machine vision, visual object, pairwise comparisons, inconsistency analysis, content-based image retrieval.

Iwanowski M.:
Morphological Classification of Binary Image's Pixels.
MGV vol. 18, no. 2, 2009, pp. 155-174.
The paper presents a novel approach to the classification of binary image pixels based on mathematical morphology. The proposed method makes use of new {\em class extractor} operators to extract pixels belonging to pre-defined classes from the original image. The classes consist of pixels characterized by particular morphological properties. In the paper, a general scheme of a morphological classifier is introduced. Depending on the class to be detected, various extractors can be defined based on the mathematical morphology. The extracted classes can be organized in a class hierarchy tree structure. Apart from the general framework of a morphological classifier, various class extractors are described a well. Another notion introduced in the paper are class distribution functions, which can be used as shape descriptors for pattern recognition. Finally, two practical examples of classification of binary image pixels are presented, representing an application to shape description and to analysis of the shape of water bodies.
Key words: mathematical morphology, image classification, binary images.

Gridin V.N., Titov V.S., Truphanov M.I., Korostelev S.I.:
Vision System for Image Recognition Based on Three-dimensional Vector Patterns.
MGV vol. 18, no. 2, 2009, pp. 175-.
We introduce a vision system for object image recognition based on a three-dimensional vector pattern, which offers a possibility to solve a broad variety of problems connected with image recognition. We point out that the use of three-dimensional vector models as patterns enables substantial reduction in the memory costs and simplification of the learning process. In the paper we provide a description of the image recognition method implemented by the vision system, and propose the ways for its development.
Key words: Vision System, Image Recognition, Three-dimensional Vector Patterns, 3D pattern.

Amine A., Ghouzali S., Rziza M., Aboutajdine D.:
An improved Method for Face Recognition Based on SVM in Frequency Domain.
MGV vol. 18, no. 2, 2009, pp. 187-.
We examine the problem of discriminating between objects of more than two classes using 'minimum information'. Discrete Cosine Transforms (DCT) represents a computationally simple and efficient method that preserves the structure of the data without introducing significant distortion. In this paper, an efficient face recognition method combined DCT and Support Vector Machine (SVM) is proposed. The underlying algorithm is derived by applying DCT to several regions of a face image. Only a small subset of the DCT coefficients is retained by truncating high frequency DCT components in each block. Selected DCT features are then subjected to SVM for class separability enhancement before being used for face recognition. This leads to a new, low-dimensional representation of images which allows for a fast and simple classification. In this context, we have performed a large number of experiments using two popular face databases: ORL and Yale, and comparisons using PCA, LDA, ICA, MLP, etc. Experimental results show that the proposed method performs better than traditional approaches in terms of both efficiency and accuracy.
Key words: Face recognition, Feature Extraction, Support Vector Machine, Discrete Cosine Transform.

Ghouzali S., Hemami S., Rziza M., Aboutajdine D.:
Generalized Gaussian Density for Skin Detection in DCT Domain.
MGV vol. 18, no. 2, 2009, pp. 201-.
In this paper, we propose a highly efficient algorithm to model the human skin color. The algorithm involves generating a discrete Cosine transform (DCT) at each pixel location, using the surrounding points. The DCT coefficients incorporate the pixel color and texture information to distinguish between skin and non-skin. A generalized Gaussian distribution (GGD) is used in this framework to model the DCT coefficients at low frequencies. Next, the model parameters are estimated using the maximum-likelihood (ML) criterion applied to a set of training skin samples. Finally, each pixel is classified as skin if its likelihood ratio exceeds some threshold. The experimental results show that our model avoids excessive false detection while still retaining a high degree of correct detection.
Key words: Skin detection, Generalized Gaussian distribution, Discrete Cosine Transform, Image segmentation.

Mahersia H., Hamrouni K.:
Rotation- and Scale-Invariant Texture Classification Using Log-Polar and Ridgelet Transforms.
MGV vol. 18, no. 2, 2009, pp. 215-.
Classification of distorted texture images is a challenging and important problem in real world image analysis and understanding. This paper proposes a new texture characterization method which is robust to geometric distortions, including rotation and scale changes. The rotation- and scale-invariant feature extraction for a given image involves applying the log-polar transform to eliminate the rotation and scale effects, followed by the ridgelet transform. In the experiments, the K-nearest neighborhood classifier is employed, using Euclidian and Manhattan distances to classify two sets of 30 and 40 distinct natural textures selected from the Brodatz and the VisTex albums. The experimental results, based on different test data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar ridgelet signatures outperforms texture classification based on log-polar and wavelet transforms. Its overall accuracy rate reaches 100% for orientation or scale changes, and is about 73.708\% for joint rotation and scale changes. These results demonstrate the effectiveness of our characterization method in texture image classification experiments.
Key words: Classification, Log-polar transform, Segmentation, Ridgelet transform, Texture.

Tominaga S.:
Material Classification Method for Printed Circuit Boards Using a Spectral Imaging System.
MGV vol. 18, no. 2, 2009, pp. 233-.
This paper proposes a method for classifying object materials on a raw circuit board into element materials by means of surface-spectral reflectance. First, we develop a spectral imaging system for observing the minute details of the board and capturing their spectral data. Second, the surface-spectral reflectance functions of the board are estimated by a direct method using narrow band sensor outputs. We investigate the reflection properties of various objects on the board under different illumination directions. Third, we find key features of the body spectral reflectances for different materials, and present a rule for classifying the objects into six element materials. Finally, experiments are executed using a real circuit board. The observed spectral reflectance image is segmented into the element material areas. The performance and robustness of the proposed method are examined in detail in comparison with other methods.
Key words: spectral imaging, material classification, raw circuit boards, surface-spectral reflectance.

## Machine GRAPHICS & VISION, Vol. 18 (2009), No. 3:

Zelawski M.:
Detecting Pathologies with Homology Algorithms in Magnetic Resonance Images of Brain.
MGV vol. 18, no. 3, 2009, pp. 253-266.
This paper describes a method for detecting the presence of pathological changes in two-dimensional brain images from magnetic resonance examination. The proposed idea is based on homology theory, which makes it easily extendable to three-dimensional brain images in particular it may be applied to the computer tomography data.
Key words: homology algorithm, brain tumor, MR imaging, medical imaging.

Abdel-Qader I., Zyout I., Jacobs C.:
Detection of Synthetic and Real Microcalcifications based on Statistical Analysis of Original and Highpass-Filtered Mammograms.
MGV vol. 18, no. 3, 2009, pp. 267-288.
Detection of clustered microcalcifications in digitized mammograms can be very useful for early detection of breast cancer. Clustered microcalcifications have a distinguished signature in both spatial and frequency domains. In the spatial domain, they appear as white spots which represent local maxima, while in the frequency domain microcalcifications represent local anomalies that can be captured within the high frequency subbands. In this work, we propose an algorithm for detection of clustered microcalcifications by utilizing these signatures, integrating the statistical parameters of both spatial and frequency domains. The results prove the effectiveness of the proposed method, and indicate that the exploitation of both domain signatures of the clustered microcalcifications yields significantly better detection results.
Key words: wavelet transforms, high order statistics, tail ratio, synthetic microcalcifications.

Rachubi\'nski M.:
Iris Features Extraction Using Beamlets and Wedgelets.
MGV vol. 18, no. 3, 2009, pp. 289-304.
A new approach to iris feature extraction using geometrical wavelets is presented. Iris code is generated by using representation of the wavelet coefficients based on a wedgelet dictionary. The accuracy of identification in the case of head inclination by a certain angle for different ranges of possibilities of shifting the iris code is shown. Experimental results on the CASIA iris database show that the proposed method is effective and exhibits encouraging performance.
Key words: biometrics, iris recognition, personal identification, beamlets, wedgelets.

Yang B., Li S.:
Texture Classification Using Combined Image Decomposition Methods.
MGV vol. 18, no. 3, 2009, pp. 305 - 319.
The developments of multiresolution analysis, such as the wavelet, curvelet and contourlet transforms, have yielded adequate tools to characterize different scales of textures effectively. These methods exhibit different performances in processing texture images due to their different characteristics. In order to use those complementary characteristics simultaneously, a texture classification method by combining different image decomposition methods is proposed. The proposed method is compared with the methods where only one kind of multiresolution transform is used. The experimental results demonstrate that the combined features can effectively capture the complementary information from different image decomposition methods and obviously improve the texture classification accuracy.
Key words: Texture classification, multiresolution transformation, features extraction, combined features, support vector machine.

Hussein A. S.:
TA System for Reconstruction of Solid Models from Large Point Clouds.
MGV vol. 18, no. 3, 2009, pp. 321 - 344.
This paper presents an integrated system for reconstructing solid models capable of handling large-scale point clouds. The present system is based on new approaches to implicit surface fitting and polygonization. The surface fitting approach uses the Partition of Unity (POU) method associated with the Radial Basis Functions (RBFs) on a distributed computing environment to facilitate and speed up the surface fitting process from large-scale point clouds without any data reduction to preserve all of the surface details. Moreover, the implicit surface polygonization approach uses an innovative Adaptive Mesh Refinement (AMR) based method to adapt the polygonization process to geometric details of the surface. This method steers the volume sampling via a series of predefined optimization criteria. Then, the reconstructed surface is extracted from the adaptively sampled volume. The experimental results have demonstrated accurate reconstruction with scalable performance. In addition, the proposed system reaches more than 80% savings in the total reconstruction time for large datasets of O(107) points.
Key words:solid modeling, computational geometry, surface reconstruction, parallel implementation, Radial Basis Functions, CAD, point clouds.

Huber-Mörk R., Löhndorf M., Heiss-Czedik D., Mayer K., Penz H., Soukup D.:
Fast and Efficient Colour Inspection using Sets of Ellipsoidal Regions.
MGV vol. 18, no. 3, 2009, pp. 345-361.
This paper addresses inspection of d-dimensional data using ellipsoidal decision regions for problems in automated visual inspection. For the special case of d = 3, the proposed method is researched in detail with respect to efficient real-time operation and compact storage of ellipsoid parameters. In order to reduce storage requirements, a method based on clustering the parameters describing the shapes of ellipsoids is proposed. Results are presented for colour images used in the quality analysis step in banknote printing. Additionally, estimations of computational effort and storage requirements are provided.
Key words: industrial inspection, ellipsoidal regions, real-time image processing.

Ahmed K., El-Henawy I., Atwan A.:
Novel DWT Video Watermarking Schema.
MGV vol. 18, no. 3, 2009, pp. 363-380.
In this paper a new video watermarking scheme is proposed which depends on 2-level Discrete Wavelet Transform decomposition of each component of an RGB video frame. The scheme embeds independent watermarks into different shots. A genetic algorithm is employed to match shots to watermarks. The scheme chooses between the HL$_1$ of red or green or blue components of each frame based on a key and embeds error correcting code into one of them. The scheme is blind. Experimental results show that the scheme is robust against attacks such as frame dropping, frame averaging, frame swapping, statistical analysis, and MPEG-2 and MPEG-4 compression. The proposed scheme uses a composite three-element key to increase the security.
Key words: Video Watermarking, Discrete Wavelet Transform, Genetic, Shot, Frame.

## Machine GRAPHICS & VISION, Vol. 18 (2009), No. 4:

Hiremath P.S., Prabhakar C.J.:
Symbolic Kernel Fisher Discriminant Method With a New RBF Kernel Function for Face Recognition.
MGV vol. 18, no. 4, 2009, pp. 383-403 .
In this paper, we present a new radial basis kernel function (RBF) in symbolic kernel Fisher discriminant analysis (symbolic KFD) to extract nonlinear interval type features for face recognition. The kernel-based methods form a powerful paradigm, they are not favorable to deal with the challenge of large datasets of faces. We propose to scale up training task based on the interval data concept. Our investigation aims at extending KFD to interval data using new RBF kernel function. We adapt symbolic KFD to extract interval type nonlinear discriminating features, which are robust enough to varying facial expression, viewpoint and illumination. In the classification phase, we employ the minimum distance classifier with the squared Euclidean distance measure. The new algorithm has been successfully tested using four databases, namely, the ORL face database, the Yale face database, the Yale face database B and the FERET face database. The experimental results show that the symbolic KFD with the new RBF kernel function yields improved performance.
Key words: Face Recognition, Symbolic Data Analysis, Kernel Fisher Discriminant Analysis, Interval Type Features, RBF kernel function.

Hazem M. El-Bakry:
New Fast Principal Component Analysis For Real-Time Face Detection.
MGV vol. 18, no. 4, 2009, pp. 405-425.
Principal component analysis (PCA) has various important applications, especially in pattern detection, such as face detection and recognition. In real-time applications, the response time must be as short as possible. In this paper, a new implementation of PCA for fast face detection is presented. Such implementation relies on performing cross-correlation in the frequency domain between the input image and eigenvectors (weights). Furthermore, this approach is developed to reduce the number of computation steps required by fast PCA. The "divide and conquer" principle is applied through image decomposition. Each image is divided into smaller-size sub-images, and then each of them is tested separately using a single fast PCA processor. In contrast to using only fast PCA, the speed-up ratio increases with the size of the input image when using fast PCA and image decomposition. Simulation results demonstrate that the proposed algorithm is faster than conventional PCA. Moreover, experimental results for different images show its good performance. The proposed fast PCA increases the speed of face detection, and at the same time does not affect the performance or detection rate.
Key words: Image Decomposition, Fast PCA, Cross correlation, Frequency Domain, Face Detection.

Degtyarev S.V., Miroshnichenko S.Yu., Titov V.S.:
Detection of Object Edges in Aerospatial Cartographic Images.
MGV vol. 18, no. 4, 2009, pp. 427-437.
Modern geographic information systems (GIS) have a wide range of applications, including town-planning, town traffic control, ecology, logistics and others. These GIS require frequent update's using information supplied by aerospatial cartographic images. The standard approach to processing aerospatial cartographic images is interactive vectorization using software tools. This paper presents a method for object edge detection which is part of automated vectorization of aerospatial cartographic images. The method uses edge detection with local scale estimation and is well suited for locating anthropogenic objects (such as buildings and roads). The effectiveness of the proposed method has been experimentally verified on real aerospatial cartographic images.
Key words: image processing, aerospatial images vectorization, edge detection, local scale estimation.

Okarma K., Lech P.:
Application of Monte Carlo Preliminary Image Analysis and Classification Method for Automatic Reservation of Parking Space.
MGV vol. 18, no. 4, 2009, pp. 439-452.
The paper presents a Monte Carlo-based method for preliminary image analysis and classification applied to parking space reservation. The proposed approach can be used especially for large parking areas with high density of parking places, where automatic recognition of some geometrical parameters of the entering vehicles can be helpful for optimised usage of the available parking area. After performing real-time classification of the entering vehicles, the shortest path towards the reserved parking place can be presented for the driver e.g. on some display screens. This requires appling a~fast classification method which can be easily implemented in embedded systems instead of some typical classification algorithms characterised by relatively high computational complexity. The Monte Carlo approach presented in the paper can be used as an efficient solution of this problem.
Key words: Monte Carlo method, statistical image analysis, automatic classification.

Martyn T.:
Exploring the infinite-time behavior of the chaos game: Approximation and interactive visualization of 3D IFSP and RIFS invariant measures using PC graphics accelerators.
MGV vol. 18, no. 4, 2009, pp. 453-476.
In this paper we tackle the problem of approximation and visualization of invariant measures arising from Iterated Function Systems with Probabilities (IFSP) and Recurrent Iterated Function Systems (RIFS) on $\Real^3$. The measures are generated during the evolution of a stochastic dynamical system, which is a random process commonly known as the chaos game. From the dynamical system viewpoint, an invariant measure gives a temporal information on the long-term behavior of the chaos game related to a given IFSP or RIFS. The non-negative number that the measure takes on for a given subset of space says how often the dynamical system visits that subset during the temporal evolution of the system as time tends to infinity. In order to approximate the measures, we propose a method of measure instancing that can be considered an analogue of object instancing for IFS attractors. Although the IFSP and RIFS invariant measures are generated by the long-term behavior of stochastic dynamical systems, measure instancing makes it possible to compute the value that the measure takes on for a given subset of space in a deterministic way at any accuracy required. To visualize the data obtained with the algorithm, we use direct volume rendering. To incorporate the global structure of invariant measures along with their local properties in an image, a modification of a shading model based on varying density emitters is used. We adapt the model to match the fractal measure context. Then we show how to implement the model on commodity graphics hardware using an approach that combines GPU-based direct volume raycasting and 3D texture slicing used in the object-aligned manner. By means of the presented techniques, visual exploration of 3D IFSP and RIFS measures can be carried out efficiently at interactive frame rates.
Key words: Fractals, Recurrent iterated function system, Invariant measure, Volume rendering, GPU programming.

Yuan T., Cheng W., Tao G., Lijun L.:
An Improved Interactive Color Image Segmentation Using Region-Based Graph Cuts.
MGV vol. 18, no. 4, 2009, pp. 477-488.
The problem of efficient interactive extraction of a foreground object in a complex environment is the primary one in image processing and computer vision. The segmentation method based on graph cuts has been studied over the recent years. There are two main drawbacks of these studies: decrease in performance when the foreground and the background have similar colors, and long computing time when the image is large. In this paper, we present a new foreground objects extraction method using a region-based graph cuts algorithm. The image is pre-segmented into a large number of small partitions using the mean shift (MS) method. We use the partitions to represent the nodes in the graph instead of pixels. This approach can reduce the optimization time, which is closely related to the number of nodes and edges in the graph. Compared with the pixel-based method, our method can yield an excellent performance and exhibit a faster speed.
Key words: Image Segmentation, Graph Cuts, Mean Shift (MS), Foreground, Interactive Extraction.

Reviewers' index
Authors' index
First Negative Selection Ratio, Final Acceptance Ratio
Contents of volume 18, 2009

 17 (2008) main 19 (2010)


text:
# bayesplot 1.8.0 2021-01-10

### Bug fixes

• mcmc_areas() tries to use less vertical blank space. (#218, #230)

• Fix bug in color_scheme_view() minimal theme (#213).

• Fix error in mcmc_acf() for certain input types. (#244, #245, @hhau)

### New features

• New plotting functions ppc_dens_overlay_grouped() and ppc_ecdf_overlay_grouped() for plotting density and cumulative distributions of the posterior predictive distribution (versus observed data) by group. (#212)

• New plotting function ppc_km_overlay() for outcome variables that are
right-censored. Empirical CCDF estimates of yrep are compared with the Kaplan-Meier estimate of y. (#233, #234, @fweber144)

• ppc_loo_pit_overlay() now uses a boundary correction for an improved kernel density estimation. The new argument boundary_correction defaults to TRUE but can be set to FALSE to recover the old version of the plot. (#171, #235,

• CmdStanMCMC objects (from CmdStanR) can now be used with extractor functions nuts_params(), log_posterior(), rhat(), and neff_ratio(). (#227)

• On the y axis, ppc_loo_pit_qq(..., compare = "normal") now plots standard normal quantiles calculated from the PIT values (instead of the standardized PIT values). (#240, #243, @fweber144)

• mcmc_rank_overlay() gains argument facet_args. (#221, @hhau)

• For mcmc_intervals() the sizeof the points and interval lines can be set withmcmc_intervals(…, outer_size, inner_size, point_size). (#215, #228, #229)

# bayesplot 1.7.2 2020-05-28

Compatibility with dplyr 1.0.0 (#219)

# bayesplot 1.7.1 2019-12-01

Release requested by CRAN to fix errors at https://cran.r-project.org/web/checks/check_results_bayesplot.html due to matrices also inheriting from “array” in R 4.0.

# bayesplot 1.7.0 2019-05-23

(GitHub issue/PR numbers in parentheses)

• The pars argument of all MCMC plotting functions now supports tidy variable selection. See ?"tidy-params" for details and examples. (#161, #183, #188)

• Two new plots have been added for inspecting the distribution of ranks. Rank histograms were introduced by the Stan team’s new paper on MCMC diagnostics. (#178, #179)

mcmc_rank_hist(): A traditional traceplot (mcmc_trace()) visualizes how sampled values the MCMC chains mix over the course of sampling. A rank histogram (mcmc_rank_hist()) visualizes how the ranks of values from the chains mix together. An ideal plot would show the ranks mixing or overlapping in a uniform distribution.

mcmc_rank_overlay(): Instead of drawing each chain’s histogram in a separate panel, this plot draws the top edge of the chains’ histograms in a single panel.

• Added mcmc_trace_data(), which returns the data used for plotting the trace plots and rank histograms. (Advances #97)

• ColorBrewer palettes are now available as color schemes via color_scheme_set(). For example, color_scheme_set("brewer-Spectral") will use the Spectral palette. (#177, #190)

• MCMC plots now also accept objects with an as.array method as input (e.g., stanfit objects). (#175, #184)

• mcmc_trace() gains an argument iter1 which can be used to label the traceplot starting from the first iteration after warmup. (#14, #155, @mcol)

• mcmc_areas() gains an argument area_method which controls how to draw the density curves. The default "equal area" constrains the heights so that the curves have the same area. As a result, a narrow interval will appear as a spike of density, while a wide, uncertain interval is spread thin over the x axis. Alternatively "equal height" will set the maximum height on each curve to the same value. This works well when the intervals are about the same width. Otherwise, that wide, uncertain interval will dominate the visual space compared to a narrow, less uncertain interval. A compromise between the two is "scaled height" which scales the curves from "equal height" using height * sqrt(height). (#163, #169)

• mcmc_areas() correctly plots density curves where the point estimate does not include the highest point of the density curve. (#168, #169, @jtimonen)

• mcmc_areas_ridges() draws the vertical line at x = 0 over the curves so that it is always visible.

• mcmc_intervals() and mcmc_areas() raise a warning if prob_outer is ever less than prob. It sorts these two values into the correct order. (#138)

• MCMC parameter names are now always converted to factors prior to plotting. We use factors so that the order of parameters in a plot matches the order of the parameters in the original MCMC data. This change fixes a case where factor-conversion failed. (#162, #165, @wwiecek)

• The examples in ?ppc_loo_pit_overlay() now work as expected. (#166, #167)

• Added "viridisD" as an alternative name for "viridis" to the supported colors.

• Added "viridisE" (the cividis version of viridis) to the supported colors.

• ppc_bars() and ppc_bars_grouped() now allow negative integers as input. (#172, @jeffpollock9)

# bayesplot 1.6.0 2018-08-02

(GitHub issue/PR numbers in parentheses)

• Loading bayesplot no longer overrides the ggplot theme! Rather, it sets a theme specific for bayesplot. Some packages using bayesplot may still override the default ggplot theme (e.g., rstanarm does but only until next release), but simply loading bayesplot itself will not. There are new functions for controlling the ggplot theme for bayesplot that work like their ggplot2 counterparts but only affect plots made using bayesplot. Thanks to Malcolm Barrett. (#117, #149).

• bayesplot_theme_set()
• bayesplot_theme_get()
• bayesplot_theme_update()
• bayesplot_theme_replace()
• The Visual MCMC Diagnostics vignette has been reorganized and has a lot of useful new content thanks to Martin Modrák. (#144, #153)

• The LOO predictive checks now require loo version >= 2.0.0. (#139)

• Histogram plots gain a breaks argument that can be used as an alternative to binwidth. (#148)

• mcmc_pairs() now has an argument grid_args to provide a way of passing optional arguments to gridExtra::arrangeGrob(). This can be used to add a title to the plot, for example. (#143)

• ppc_ecdf_overlay() gains an argument discrete, which is FALSE by default, but can be used to make the Geom more appropriate for discrete data. (#145)

• PPC intervals plots and LOO predictive checks now draw both an outer and an inner probability interval, which can be controlled through the new argument prob_outer and the already existing prob. This is consistent with what is produced by mcmc_intervals(). (#152, #154, @mcol)

# bayesplot 1.5.0 2018-03-30

(GitHub issue/PR numbers in parentheses)

• New package documentation website: https://mc-stan.org/bayesplot/

• Two new plots that visualize posterior density using ridgelines. These work well when parameters have similar values and similar densities, as in hierarchical models. (#104)

• mcmc_dens_chains() draws the kernel density of each sampling chain.
• mcmc_areas_ridges() draws the kernel density combined across chains.
• Both functions have a _data() function to return the data plotted by each function.
• mcmc_intervals() and mcmc_areas() have been rewritten. (#103)

• They now use a discrete y-axis. Previously, they used a continuous scale with numeric breaks relabelled with parameter names; this design
caused some unexpected behavior when customizing these plots.
• mcmc_areas() now uses geoms from the ggridges package to draw density curves.
• Added mcmc_intervals_data() and mcmc_areas_data() that return data plotted by mcmc_intervals() and mcmc_areas(). (Advances #97)

• New ppc_data() function returns the data plotted by many of the PPC plotting functions. (Advances #97)

• Added ppc_loo_pit_overlay() function for a better LOO PIT predictive check. (#123)

• Started using vdiffr to add visual unit tests to the existing PPC unit tests. (#137)

# bayesplot 1.4.0 2017-09-12

(GitHub issue/PR numbers in parentheses)

• New plotting function mcmc_parcoord() for parallel coordinates plots of MCMC draws (optionally including HMC/NUTS diagnostic information). (#108)

• mcmc_scatter gains an np argument for specifying NUTS parameters, which allows highlighting divergences in the plot. (#112)

• New functions with names ending with suffix _data don’t make the plots, they just return the data prepared for plotting (more of these to come in future releases):

• ppc_intervals_data() (#101)
• ppc_ribbon_data() (#101)
• mcmc_parcoord_data() (#108)
• mcmc_rhat_data() (#110)
• mcmc_neff_data() (#110)
• ppc_stat_grouped(), ppc_stat_freqpoly_grouped() gain a facet_args argument for controlling ggplot2 faceting (many of the mcmc_ functions already have this).

• The divergences argument to mcmc_trace() has been deprecated in favor of np (NUTS parameters) to match the other functions that have an np argument.

• Fixed an issue where duplicated rhat values would break mcmc_rhat() (#105).

# bayesplot 1.3.0 2017-08-07

(GitHub issue/PR numbers in parentheses)

• bayesplot::theme_default() is now set as the default ggplot2 plotting theme when bayesplot is loaded, which makes changing the default theme using ggplot2::theme_set() possible. Thanks to @gavinsimpson. (#87)

• mcmc_hist() and mcmc_hist_by_chain() now take a freq argument that defaults to TRUE (behavior is like freq argument to R’s hist function).

• Using a ts object for y in PPC plots no longer results in an error. Thanks to @helske. (#94)

• mcmc_intervals() doesn’t use round lineends anymore as they slightly exaggerate the width of the intervals. Thanks to @tjmahr. (#96)

# bayesplot 1.2.0 2017-04-12

A lot of new stuff in this release. (GitHub issue/PR numbers in parentheses)

## Fixes

• Avoid error in some cases when divergences is specified in call to mcmc_trace() but there are not actually any divergent transitions.

• The merge_chains argument to mcmc_nuts_energy() now defaults to FALSE.

## New features in existing functions

• For mcmc_*() functions, transformations are recycled if transformations argument is specified as a single function rather than a named list. Thanks to @tklebel. (#64)

• For ppc_violin_grouped() there is now the option of showing y as a violin, points, or both. Thanks to @silberzwiebel. (#74)

• color_scheme_get() now has an optional argument i for selecting only a subset of the colors.

• New color schemes: darkgray, orange, viridis, viridisA, viridisB, viridisC. The viridis schemes are better than the other schemes for trace plots (the colors are very distinct from each other).

## New functions

• mcmc_pairs(), which is essentially a ggplot2+grid implementation of rstan’s pairs.stanfit() method. (#67)

• mcmc_hex(), which is similar to mcmc_scatter() but using geom_hex() instead of geom_point(). This can be used to avoid overplotting. (#67)

• overlay_function() convenience function. Example usage: add a Gaussian (or any distribution) density curve to a plot made with mcmc_hist().

• mcmc_recover_scatter() and mcmc_recover_hist(), which are similar to mcmc_recover_intervals() and compare estimates to “true” values used to simulate data. (#81, #83)

• New PPC category Discrete with functions:

• ppc_rootogram() for use with models for count data. Thanks to @paul-buerkner. (#28)
• ppc_bars(), ppc_bars_grouped() for use with models for ordinal, categorical and multinomial data. Thanks to @silberzwiebel. (#73)
• New PPC category LOO (thanks to suggestions from @avehtari) with functions:

• ppc_loo_pit() for assessing the calibration of marginal predictions. (#72)
• ppc_loo_intervals(), ppc_loo_ribbon() for plotting intervals of the LOO predictive distribution. (#72)

# bayesplot 1.1.0 2016-12-20

(GitHub issue/PR numbers in parentheses)

## Fixes

• Images in vignettes should now render properly using png device. Thanks to TJ Mahr. (#51)

• xaxis_title(FALSE) and yaxis_title(FALSE) now set axis titles to NULL rather than changing theme elements to element_blank(). This makes it easier to add axis titles to plots that don’t have them by default. Thanks to Bill Harris. (#53)

## New features in existing functions

• Add argument divergences to mcmc_trace() function. For models fit using HMC/NUTS this can be used to display divergences as a rug at the bottom of the trace plot. (#42)

• The stat argument for all ppc_stat_*() functions now accepts a function instead of only the name of a function. (#31)

## New functions

• ppc_error_hist_grouped() for plotting predictive errors by level of a grouping variable. (#40)

• mcmc_recover_intervals)( for comparing MCMC estimates to “true” parameter values used to simulate the data. (#56)

• bayesplot_grid()` for juxtaposing plots and enforcing shared axis limits. (#59)

# bayesplot 1.0.0 2016-11-18

Initial CRAN release

format: plain


text:
Journal topic
Atmos. Meas. Tech., 13, 2413–2423, 2020
https://doi.org/10.5194/amt-13-2413-2020
Atmos. Meas. Tech., 13, 2413–2423, 2020
https://doi.org/10.5194/amt-13-2413-2020

Research article 15 May 2020

Research article | 15 May 2020

# Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors
Joel Kuula1, Timo Mäkelä2, Minna Aurela1, Kimmo Teinilä1, Samu Varjonen3, Óscar González4, and Hilkka Timonen1 Joel Kuula et al.
• 1Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
• 2Climate System Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
• 3Department of Computer Science, University of Helsinki, Helsinki, 00560, Finland
• 4Fab Lab Barcelona, Institute of Advanced Architecture of Catalonia, Barcelona, 08005, Spain

Correspondence: Joel Kuula (joel.kuula@fmi.fi)

Abstract

Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.

1 Introduction

The recent emergence of low-cost sensors has enabled new possibilities in traditional air quality monitoring (Kumar et al., 2015; Morawska et al., 2018; Snyder et al., 2013). As a result of low unit costs and compact size, sensors can be deployed to the field in much higher quantities than before, thus enabling higher-resolution spatiotemporal data. Few studies have demonstrated applications of sensor networks (Caubel et al., 2019; Feinberg et al., 2019; Gao et al., 2015; Jiao et al., 2016; Popoola et al., 2018; Yuval et al., 2019). Distributed sensing of air quality can be seen as an important progression towards a more comprehensive understanding of city-scale air quality dynamics as air pollution, and particulate matter (PM) in particular, may have highly localized concentration “hot spots” in urban areas. Practical limitations, such as expensiveness and bulkiness, constrain the use of conventional instrumentation in monitoring networks; therefore, low-cost sensors could have an essential role in the spatial extension of measurement coverage.

Numerous field studies have been conducted previously; the majority have underlined the potential usefulness of optical particulate matter sensors (Karagulian et al., 2019; Rai et al., 2017). However, the literature has also emphasized that the risk of sensor misuse is still high and that some external factors, such as relative humidity, may produce significant measurement artifacts in the data (Jayaratne et al., 2018; Kuula et al., 2018; Liu et al., 2019). In comparison to gas sensing, PM measurements are notably more challenging when ambient particle sizes and their respective distributions vary significantly from source to source and from location to location. Along with size, particle physical properties such as shape and refractive index also affect the sensor output. Several studies have pointed out that along with dynamic adjustment for meteorological parameters, on-site calibrations are required in order to achieve higher levels of accuracy and precision (Zheng et al., 2018). However, when considering advanced calibration techniques, Schneider et al. (2019) have raised a valid point noting that it may be unclear whether the sensor data resulting from complex correction and conversion processes (e.g., machine learning) are still a legitimate and independent product of the sensor measurement and not a combination of secondary data and statistical model prediction. This is an important remark when evaluating the usability of sensors as it highlights the need to identify the reasons behind inaccuracies in low-cost sensor measurements.

While field evaluations are a natural step towards understanding and developing sensors, they provide limited information about the detailed sensor response characteristics. In particular, less attention has been paid to the investigation of particle-size discrimination of sensors. Although a few studies have noted that the detectable particle-size ranges of sensors may be significantly different from the ones declared in their respective technical specification sheets (Budde et al., 2018; Levy Zamora et al., 2019), this factor is not commonly considered when assessing sensor accuracy. Thus, more research is needed. The objective of this study was to investigate and characterize the size selectiveness of some of the optical low-cost sensors commonly appearing in the literature. The evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. Along with these low-cost sensors, a mid-cost optical aerosol size spectrometer (Grimm model 1.108, Grimm Aerosol Technik GmbH, Germany) was evaluated cursorily to highlight the differences between the responses of low-cost and mid-cost devices. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes. Sensor responses were compared to a reference instrument (APS, aerodynamic particle sizer 3321, TSI Inc., USA), and detectable particle-size ranges of the sensors were obtained.

2 Methods

## 2.1 Evaluated sensors

The sensors evaluated in this study, and their main detection properties, are listed in Table 1. The optical detection configurations of these sensors were arranged in either a 90 or 120 scattering angle, and either a red laser or an infrared (IR) light-emitting diode (LED) was used as a light source. Sensors utilizing an LED were equipped with additional light focusing lenses. The optical chamber itself was composed of an injection-molded plastic body which was placed onto an electronic circuit board. The PMS5003, SDS011, and SPS30 use fans to generate sample flow, whereas the PPD42 and B5W utilized natural convection resulting from a heating resistor. The sampling of the GP2Y1010AU0F was based on diffusion. The optical configurations and plastic body layouts are shown in Fig. S1 in the Supplement. Three units for each sensor model were evaluated in order to assess their inter-unit variation.

Table 1Basic features of the evaluated sensors declared by the manufacturers.

a Standard particle (CF =1) output was used. b Manually adjusted threshold voltage was set to 0.5 V as recommended by the manufacturer.

The mid-cost Grimm 1.108 spectrometer, which was used here for demonstration purposes, is an optical aerosol size spectrometer with 15 size bins (from 0.23 to 20 µm). Previous evaluations of the Grimm 1.108 spectrometer have shown its response to be similar to that of the APS (Peters et al., 2006); furthermore, its accuracy (mass of C-factor-adjusted total suspended particles) is comparable to that of mass measurement methods such as the filter weighing method (Burkart et al., 2010).

## 2.2 Reference aerosol

### 2.2.1 Vibrating orifice aerosol generator and gradient elution pump

The aerosol sampled by the low-cost sensors was generated using a vibrating orifice aerosol generator 3450 (VOAG, TSI Inc., USA). The operating principle of the VOAG is based on the instability and breakup of a cylindrical liquid jet. Mechanical disturbances of a resonance frequency vibration disintegrate the cylindrical jet into uniform droplets, which are dispersed into an aerosol flow system with appropriate dilution air. Dispersed droplets evaporate before significant coagulation occurs and form particles from the non-volatile solute dissolved in the volatile liquid. If the droplet liquid is non-volatile, the particle diameter and droplet diameter are equal. Otherwise, the produced particle size is calculable from the volumetric fraction of the non-volatile solute, as shown in Eqs. (1)–(2):

$\begin{array}{}\text{(1)}& {D}_{\mathrm{d}}=\left(\frac{\mathrm{6}Q}{\mathit{\pi }f}{\right)}^{\mathrm{1}/\mathrm{3}},\end{array}$

where Dd is the generated droplet diameter, Q is the solution feed rate, and f is the disturbance frequency.

$\begin{array}{}\text{(2)}& {D}_{\mathrm{p}}=\left(C+I{\right)}^{\mathrm{1}/\mathrm{3}}\cdot {D}_{\mathrm{d}},\end{array}$

where Dp is the diameter of the formed particle, C is the volumetric concentration of the non-volatile solute in the volatile liquid (typically 2-propanol or purified water), and I is the volumetric fraction of impurity in the volatile liquid.

According to Berglund and Liu (1973), the output aerosol number concentration of the VOAG has a relative standard deviation of less than 3 %, and the formed particle-size distribution is monodisperse having a geometric standard deviation (GSD) less than 1.014. These, and particularly the capability to produce highly monodisperse size distribution, are important features regarding sensor size selectivity evaluation; while polydisperse aerosol can be used, for instance, to estimate response stability and linearity to varying concentration levels (Hapidin et al., 2019; Papapostolou et al., 2017; Sayahi et al., 2019a), the presence of multiple different-sized particles prevents the distinction between sensor response and specific particle size. The most significant deficiency of the VOAG (and the main limitation of this study) is that its smallest producible particle size is in practice limited by the impurity within the carrier liquid to approximately 0.55 µm.

The novelty of the aerosol generation method used in this research is based on the observation that the particle size of the monodisperse and constant number concentration reference aerosol can be controlled by feeding solutions with different non-volatile concentrations to the VOAG, one after the other. Such an aerosol generation technique was first utilized by Kuula et al. (2017), who accomplished the solution blending with a supplementary syringe pump and a manually operated three-way valve. In this study, however, the solution feeding was done with a gradient elution pump typically used in ion chromatography (GP50, Dionex Inc., USA). The GP50 gradient pump has four different eluent channels and is capable of dispensing liquids with high pressure (max. 5000 psi) and accurate volume flow rate (0.04–10.0 mL min−1 in increments of 0.01 mL min−1). The four eluent channels can be mixed with a resolution of 0.1 % (combined output of the four channels is always 100 %); furthermore, the GP50 has a user interface that enables the operator to generate parameterized eluent-dispensing programs. In essence, the utilization of the GP50 allows the user to freely choose and produce monodisperse aerosols of desired particle sizes without the tuning of VOAG running parameters or manual alternation of the liquid concentrations. Additionally, the preconfigured dispensing programs are fully automated, making the comparison of consecutive test runs more reliable.

### 2.2.2 Sampling configuration

A schematic figure of the used test setup is shown in Fig. 1. Reference aerosol was generated using the VOAG–GP50 system as described in the previous section. Dioctyl sebacate (DOS, density of 0.914 g cm−3) was used as a non-volatile solute in a 2-propanol solvent (>99.999 %, Sigma-Aldrich), and the formed particles were transparent oil droplets. Although the reference instrument APS is known for having decreased counting efficiency for liquid droplets over ∼5µm in size (Volckens and Peters, 2005), no additional corrections were used. Running parameters of the VOAG and GP50 are shown in Table S1. The three different DOS concentrations (A–C) refer to the four different eluent channels of the GP50 (the use of three channels was sufficient for this study).

Figure 1Schematic of the sensor evaluation setup. The Grimm 1.108 spectrometer drew its sample from where the sensor enclosure is now shown.

The GP50 used an automated program for dispensing the liquids. A program involves a number of consecutive time steps in which the blending ratios of eluent channels, step durations, and volumetric flow rate of the liquid can be defined separately. Executing the program means that the GP50 dispenses the liquids according to the settings determined in each step. The program used in this evaluation consisted of 10 steps in which the produced particle sizes were logarithmically spaced from 0.45 to 9.78 µm. The calculated blending ratios and the respective particle sizes are shown in Supplemental Table S2. Step duration of 5 min was used; a single test run thus lasted approximately 60 min. Dead volumes in the GP50 and VOAG slightly extend the theoretical run time duration. A complete test run can be performed as quickly as in 15 min, which results in fewer measurement points and weaker statistical power though. An example of the produced reference aerosol number size distribution measured with the APS is shown in Fig. 2. It is worth underlining that the number of steps used in the GP50 dispensing program does not dictate the number of different particle sizes produced. The number of steps and the parameters assigned to them simply define the minimum (blending ratio of the first step) and maximum (blending ratio of the last step) particle size and the rate (step duration) at which the particle-size gradient evolves from the minimum size to maximum size. The word “gradient” is used to note that a step from 2 to 3 µm, for instance, does not lead to a discontinuous and sudden step from one particle size to another.

Figure 2An example of the produced reference aerosol. Decreasing number concentrations below 1 and above 5 µm result from approaching the lower detection limit (0.5 µm) of the APS and increasing inertial deposition losses in the sampling lines, respectively (concentration range 30–90 cm−3 of particle number concentration). This had, however, no effect on the evaluation results as the sensor response was normalized against the concentration measured by the APS. Along with the lower detection limit of the APS, another limiting factor of the study was the smallest producible particle size, which was approximately 0.55 µm. The GSD of the size distribution remained below 1.2.

Formed particles were neutralized in the dispersion outlet of the VOAG and further fed into a flow splitting section where the reference aerosol was symmetrically directed to both the reference instrument (aerodynamic particle sizer 3321, TSI Inc., USA) and sensor. The sensors were encapsulated in 3D-printed airtight enclosures with an external pump connected to them in order to ensure appropriate sample flow through the sensor. The sample flow rate was set to be 1 L min−1 – the aerosol flow rate of the APS (sheath flow of the APS taken from the laboratory air). Although there is no clear theoretical basis as to why a different flow rate would affect the way the sensor discriminates different particle sizes (apart from different particle-size-specific sampling losses), additional tests were conducted with flow rates of 0.5 and 2 L min−1 to ensure that this was indeed the case (see Fig. S2). For the PMS5003 and SPS30 sensors, an exhaust deflector was used to prevent unwanted sample mixing resulting from the fan outlet, which for these sensors, was situated right next to the sensor inlet. An illustration of the PMS5003 sampling arrangement is shown in Fig. 3. A schematic figure of all the sampling arrangements is shown in Fig. S3.

Figure 3A cross-section view of the sampling arrangement of the PMS5003 sensor. The flow deflector is shown in light grey, the inlet pipe in red, and the exhaust connector of the housing in yellow.

All sensor units were in the original condition except for the PPD42 and B5W sensors which had their air heating resistors removed. The evaluation platform used in this study did not require independent means of sample flow. Furthermore, holes were drilled into the plastic body of the PPD42 to ensure that the sample aerosol could reach to the optical detection volume. The inlet of the PPD42 was originally designed to be on top of the plastic body (facing towards the electronic circuit board); therefore, when the electronic circuit board of the sensor was oriented in parallel with the sample stream, the majority of the particles would have bypassed the sensor. In general, along with the PPD42, the plastic body layouts of the PMS5003 and SPS30 are susceptible to inertial deposition losses due to their 90 elbows in particle stream pathways. However, the more stable sample flow system (i.e., fan instead of convection) might help compensate for the sub-optimal layouts of these sensors.

## 2.3 Data processing

The output signal of the evaluated sensor and APS was measured synchronously using a 10 s time resolution and moving average. Any raw measurement point which had GSD (calculated from the APS data) exceeding 1.2 was disregarded (∼2.1 % of the data), but typically the GSD values ranged between 1.04 and 1.08. The sensor bias was set to zero by sampling clean air for 10 min (60 data points) and then subtracting the clean air response from the test aerosol response. The bias correction was only relevant for the GP2Y1010AU0F and B5W sensors. In order to prevent arbitrary unit comparisons, the sensor response was normalized using Eq. (3):

$\begin{array}{}\text{(3)}& \text{normalized\hspace{0.17em} detection\hspace{0.17em} efficiency}=\frac{\frac{{\text{sensor}}_{i}}{{\text{APS}}_{i}}}{\mathrm{max}\left(\frac{\text{sensor}}{\text{APS}}\right)},\end{array}$

where i is the ith measurement point, “sensor” is the sensor signal, and APS is the APS total mass concentration. The maximum sensor  APS ratio refers to the maximum ratio measured during a single test run.

The normalized 10 s resolution data were divided into 30 logarithmically spaced size bins (from 0.45 to 9.73 µm) according to the count median diameters (CMDs, aerodynamic) measured by the APS. An average sensor response as a function of average CMD was then calculated for each size bin. The decision to divide the data into 30 bins was based on the clarity of the produced figure and statistically sufficient number of measurement points belonging to each bin. This process was completed for three different sensor units, and a combined (average and standard deviation) sensor response was calculated. Valid detection ranges, which were defined as the upper half of the detection efficiency curve, of the sensors were linearly interpolated from the average response functions. A detailed example of how the data were processed and how the valid detection ranges were calculated is shown in the Supplement. The cursory evaluation of the Grimm instrument was conducted using the same data processing method. The size bins of PMS5003, SPS30, SDS011, and B5W were discretized so that no overlapping signals were obtained. For example, the outputs of the SDS011 were used as PM2.5 and PM10−2.5 (PM10−2.5 calculated as PM10–PM2.5) instead of PM2.5 and PM10.

The PMS5003, SDS011, and SPS30 sensors have digital outputs whereas the others are analog-based. Along with the PM mass fractions listed in Table 1, the PMS5003 and SPS30 sensors also output particle number concentrations, but these signals were not used because the response comparison to the reference instrument was carried out using only mass concentration values. This decision was based on the observation that low-cost sensors have been predominantly used to measure mass concentration and not number concentration.

3 Results and discussion

## 3.1 Grimm model 1.108

The normalized detection efficiencies of the 15-bin Grimm 1.108 spectrometer are shown in Fig. 3. The normalized detection efficiency of 70 %–90 % results from the average efficiency from multiple data points and, in this case, does not imply that the Grimm spectrometer would systematically underestimate particle mass concentrations. The same applies to the respective sensor response figures (next section).

The response characteristics of the Grimm spectrometer are in line with its technical specifications showing that each size bin only corresponds to its specific detection range. A flat response curve would indicate that the strength of the output signal remains unchanged regardless of the particle size, which would show that the size bin is unable to make a distinction between different particle sizes. Some mismatch between the particle sizing of the APS and the Grimm spectrometer can be observed as a result of different particle sizing techniques (time of flight and optical), but this is trivial, considering the objective of this study. The purpose of this figure is to highlight how an aerosol measurement device with several particle sizing bins should respond to the evaluation method used in this study.

## 3.2 Low-cost sensors

Response functions of the evaluated sensors are shown in Fig. 4a–f.

Figure 4Normalized detection efficiency of the 15 particle-size bins as a function of the count median diameter of the reference aerosol. Consecutively increasing and decreasing response curves indicate that the particle sizing of the instrument is functioning correctly. For the sake of clarity, degrees of measurement variation have been excluded from the figure. Bins 14 and 15, which correspond to 10–15 and 15–20 µm, respectively, are not shown as they did not produce any response (as expected).

Figure 5Normalized detection efficiency of discretized PM mass fractions reported by the low-cost sensors as a function of the count median diameter of the reference aerosol. The colored circles represent the calculated average responses of the three sensor units, and the shaded background areas represent the respective standard deviations. Standard deviations of the average CMDs were negligible due to the reliable and reproducible test method. Figure legends correspond to the bin size ranges stated by the corresponding manufacturer.

## Plantower PMS5003

According to Fig. 4a, it is apparent that the PMS5003 does not accurately distinguish between PM1, PM2.5, and PM10 size fractions. The first and the second bin (supposedly corresponding to 0.3–1.0 and 1.0–2.5 µm) are similar, with valid detection ranges of approximately <0.7 and <0.8µm, respectively (valid detection ranges were defined as the upper half of the detection range; see the section “Data processing”). The lower cut points of these bins may reach close to 0.3 µm, as stated by the manufacturer; however, this could not be confirmed using the VOAG–GP50 system. As the larger standard deviations indicate, the third bin is noisier and significantly off of its stated detection range (2.5–10 µm).

Based on the test, the PMS5003 cannot be used to measure coarse-mode particles (2.5–10 µm); furthermore, its ability to measure PM2.5 depends on the stability of the ambient air size distribution: for example, if the proportions of mass in <0.8 and >0.8µm fractions change significantly, the PMS5003 is susceptible to inaccuracies because its valid detection range cannot account for changes occurring in parts of the size distribution that it can hardly observe. However, if the ambient size distribution is stable, the PMS5003 can be adjusted to measure PM2.5 with reasonable accuracy (Bulot et al., 2019; Feenstra et al., 2019; Magi et al., 2019; Malings et al., 2019). Similarly, the validity of PM10 measurements can only be ensured when the proportion of mass in >0.7 or >0.8µm size fractions is either constant or negligible with respect to the total PM10 mass. In reality, this is rarely the case, which poses a high risk of sensor misuse. This observation is in line with the findings from previous studies (Laquai, 2017b; Li et al., 2019; Sayahi et al., 2019b) which show, for instance, that the PMS5003 could not detect a substantial dust storm episode while deployed in the field. The most accurate and reliable results are most likely achieved for the PM1 size fraction by using either bin 1 or bin 2 signals.

## Nova SDS011

The response function of the SDS011 is shown in Fig. 4b. Contrary to the PMS5003, the SDS011 exhibits two clearly different detection ranges: the first bin (0.3–2.5 µm) corresponds approximately to <0.8µm, and the second bin (2.5–10 µm) corresponds approximately to 0.7–1.7 µm. Similarly to the PMS5003, the SDS011 is not suitable for the measurement of coarse-mode particles, and the measurements of PM10 can be grossly inaccurate, as also noted by Budde et al. (2018) and Laquai (2017a). However, due to the clearer difference between bin 1 and bin 2 detection ranges, the SDS011 has the potential to measure PM2.5 more accurately than the PMS5003. For example, by calculating the ratio of bins 1 and 2, it is possible to approximate the distribution of mass in the 0.3–2.5 µm size range, thus using an additional correction factor to obtain more accurate results. Previous studies have shown that the SDS011 can be reasonably accurate in the measurements of PM2.5 (Badura et al., 2018; Liu et al., 2019).

## Sensirion SPS30

The response function of the SPS30 is shown in Fig. 4c. The valid detection range of the first bin (0.3–1.0 µm) is approximately <0.9µm. The second, third, and fourth bins (supposedly corresponding to 1.0–2.5, 2.5–4.0, and 4.0–10 µm) are nearly identical, with valid detection ranges of approximately 0.7–1.3 µm. The identical detection ranges indicate that these bins may have been factory calibrated using the same test aerosol. The SPS30 is a relatively new sensor (introduced to the markets in late 2018), and neither Web of Science nor Scopus showed any existing studies as of September 2019. However, the South Coast Air Quality Management District (SCAQMD, USA) has conducted a preliminary field test where three SPS30 units were compared to three different federal equivalent method (FEM) monitors (SCAQMD, 2019). The results of this test showed that the SPS30 sensors had very low cross-unit variability (∼1 %, 1.3 %, and 2.4 % for PM1, PM2.5, and PM10, respectively), and, more importantly, the coefficient of determinations for the measurement of PM1, PM2.5, and PM10 decreased from R2∼0.91 to 0.83 and further down to 0.12, respectively. These observations strongly align with the results of this study; furthermore, they illustrate how a sensor with limited operational range may exhibit a near-regulatory-grade performance if the measured size fraction is in alignment with the valid detection range of the sensor (<0.9µm and PM1). On the other hand, the severity of data misinterpretation is apparent when the sensor measurement is extended to cover particle sizes that it cannot observe.

## Sharp GP2Y1010AU0F

The response function of the GP2Y1010AU0F is shown in Fig. 4d, and its valid detection range appears to be approximately <0.8µm. Like the previously discussed sensors, the GP2Y1010AU0F can be used to measure small particles (e.g., PM1) but not coarse-mode particles. Several laboratory evaluations have been previously conducted for the GP2Y1010AU0F, but none of these have assessed its detection range using monodisperse test aerosols (Li and Biswas, 2017; Manikonda et al., 2016; Sousan et al., 2016). Wang et al. (2015) used atomized polystyrene latex (PSL) particles to evaluate the effect of particle size on the GP2Y1010AU0F response, but no concluding remarks can be obtained from these results. The study method utilized only three different sized PSLs; moreover, it was not designed to investigate the complete detection range of the GP2Y1010AU0F. However, according to the authors, the results implied that the sensor was more sensitive to 300 nm particles than to 600 and 900 nm particles, which is in slight disagreement with the results of this study whereby the normalized detection efficiency curve shows the highest sensitivity peak for 0.6 µm sized particles as well as a decreasing trend for particles smaller than this. There is no obvious explanation for this discrepancy, but it is worth re-emphasizing the differences in the used evaluation approaches.

## Shinyei PPD42

Response functions of the three PPD42 sensor units are shown in Fig. 4e. Contrary to the other sensors, a combined response function was not calculated as the three units exhibited significantly different response characteristics. The circles and shaded background areas represent average responses and respective standard deviations of the individual sensor units (calculated from the ∼300 raw data points). The valid detection range of the first unit is 1.0–2.1 µm, and it is likely to be best suited for PM2.5 measurements. However, the low detection efficiency of <1.0µm sized particles may considerably hinder its accuracy. Valid detection ranges of the second and third units are >5.9 and 1.5–4.9 µm, indicating preferable applicability to coarse-mode particle measurements. Previous laboratory evaluations have noted that the PPD42 output is a function of particle size but could not provide a more detailed analysis of the complete detection range (Austin et al., 2015; Wang et al., 2015). A study of Kuula et al. (2017) reported a valid detection range of approximately 2.5–4.0 µm, which is in the same range as the third unit of this study.

Due to the apparent inter-unit inconsistency in valid detection ranges, it is evident that the response characteristics of the PPD42 have to be quantified case by case before reliable measurements can be achieved. Accordingly, the inconsistent response characteristics may also contribute to the fact that previous field evaluation studies have achieved varying results regarding the performance of PPD42; Bai et al. (2019) and Holstius et al. (2014) reported R2 values of 0.75 and 0.55–0.60, respectively, for the measurement of PM2.5, whereas N. E. Johnson et al. (2018) and K. K. Johnson et al. (2018) reported more modest values of 0.36–0.51 and 0–0.28, respectively (Bai et al., 2019; Holstius et al., 2014; N. E. Johnson et al., 2018, K. K. Johnson et al., 2018). On the other hand, Kuula et al. (2017, 2018) showed that higher levels of accuracy can be achieved if the measured size fraction is targeted to correspond to the characteristic response function of the PPD42 (R2=0.96 and R2=0.87, respectively).

## Omron B5W

The response function of the B5W is shown in Fig. 4f. The two size bins exhibit two different detection ranges (0.6–1.0 and >3.2µm) that are reasonably close to the ones declared by the manufacturer (0.5–2.5 and >2.5µm). In fact, out of all sensors, the B5W appears to be the most promising sensor for the ambient monitoring of PM2.5 and PM10–2.5 size fractions. In comparison to SDS011 and SPS30, for instance, the usability of the B5W may be hindered by its temperature-gradient-based sampling method, which is not as reliable as the respective fan-based method. Nonetheless, it is the only sensor capable of measuring both fine- and coarse-fraction particles. Neither Web of Science nor Scopus showed existing studies for the Omron B5W.

4 Conclusions

According to the results obtained in this study, low-cost optical sensors exhibit widely varying response characteristics regarding their size selectivity (from <0.7 to >5.9µm, Table 2). However, none of the sensors have precisely the same response characteristics stated by their manufacturers, which provides evidence of the fact that particle-size selectivity may play an essential role in the analysis of the sources of errors in sensors and underlines that scientists, as well as manufacturers, need to acknowledge the limitations related to this: attempts to artificially extend the operational range of sensors beyond their practical capabilities using complex statistical models may be unreasonable and lead to misleading conclusions. Empirical corrections for known artifacts, such as humidity, can be justifiable; however, sensor data and advanced modeling techniques should be merged cautiously in order to retain both the validity and representativeness of the data.

Table 2Valid detection ranges of the evaluated sensors. Symbols of “greater than” or “smaller than” refer to cases where the other end of the size cut point was outside of the particle-size range producible by the VOAG–GP50 system (0.45–9.73 µm). Units are in micrometers.

* Valid detection ranges of the individual sensors, not bins.

A cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108) shows that low-cost sensor development is still considerably behind its more expensive alternative: while the Grimm 1.108 spectrometer could sufficiently characterize particle sizes with up to 15 different size bins, the low-cost sensors could only achieve independent responses for one or two bins, which is a significant weakness, considering that the ability to measure particle size correctly is at the foundation of accurate mass measurement (mass α dp3). The development of low-cost sensors should focus on increasing the number of size bins, and more importantly, making sure that each size bin is calibrated correctly. Improperly configured bin sizing poses a significant risk of data misinterpretation and will inevitably lead to inaccurate measurements. A low number of size bins limits the valid operational range of sensors; however, it is unclear how the number of advanced measurement features and low unit cost should be reconciled.

The VOAG–GP50 aerosol generation system described in this study introduced a novel approach to the quick and efficient evaluation of aerosol measurement devices. The use of a GP50 gradient pump eliminates much of the manual labor that previously was an inseparable part of the VOAG operation, thus making the generation of reference aerosols more consistent and reliable. Its automated dispensing programs allow for highly repeatable testing; furthermore, the four different eluent channels enable the operator to pick and choose the desired particle size to be produced freely. Along with saving manual labor and time, this is also a cost-saving feature as traditionally used polystyrene latex (PSL) particles are not needed. Considering these matters, the VOAG–GP50 system can potentially be scaled to an industrial-level operation, which is an intriguing feature when considering the mass deployment of sensors and their respective quality assurance and control.

Data availability
Data availability.

The data can be openly accessed upon request.

Supplement
Supplement.

Author contributions
Author contributions.

JK and TM designed the experimental setup, and JK carried out the tests. KT had an important role in refurbishing the gradient elution pump. SM and OG provided some of the sensors. JK was responsible for the data analysis, although all co-authors provided valuable feedback, particularly TM. JK wrote the manuscript with the help of all co-authors.

Competing interests
Competing interests.

The authors declare that they have no conflict of interest.

Financial support
Financial support.

This research has been supported by the European Regional Development Fund (Urban innovative actions initiative project HOPE; UIA03-240 grant) and Horizon 2020 (iSCAPE grant no. (689954)).

Review statement
Review statement.

This paper was edited by Murray Hamilton and reviewed by two anonymous referees.

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text:
# How to calculate the electric field outside an infinitely long conducting cylinder with surface charge density σ

I'm currently studying electromagnetism, specifically Gauss's Law, and have been presented with the following question:

Consider an infinitely long cylinder of radius R made out of a conducting material. The charge density of the surface of the cylinder is 𝜎. Use Gauss law to calculate the electric field outside the cylinder. (Note that the element of surface in cylindrical coordinates is given by 𝑑𝑎 = 𝑠𝑑𝜙𝑑𝑧).

I am still quite stuck despite having searched the internet for a walkthrough of this problem. The answers I can find do not seem to contain the surface charge σ term which leads me to believe my answer is wrong. My current working out is as followed:

Gauss's Law is: $$\oint E da = \frac{Q_{enc}}{\epsilon_0} \label{eq1}$$

Using a cylindrical Gaussian surface with radius r coaxial with the infinitely long conducting cylinder of radius R and length l, I calculated the LHS of the above equation to be: $$\oint E da = |E|2\pi r l \label{eq2}$$ Then working out the RHS as I understand it gives me: $$\frac{Q_{enc}}{\epsilon_0} = \frac{σa}{\epsilon_0} = \frac{σ2\pi R l}{\epsilon_0}$$ Equating the LHS to the RHS and rearranging for $$E$$: $$|E|2\pi r l = \frac{σ2\pi R l}{\epsilon_0}$$ $$E = \frac{R\sigma}{r\epsilon_0}$$ Which just does not sit right with me. Where have I gone wrong? I feel like I am being a bit thick at the moment. The website Hypherphysics (http://hyperphysics.phy-astr.gsu.edu/hbase/electric/elecyl.html) states that the electric field outside the conducting infinite cylinder should be: $$E = \frac{\lambda}{2\pi r\epsilon_0}$$ However it doesn't explain how they arrived at that answer, whilst also not containing $$\sigma$$. Any help would be greatly appreciated! Hopefully I have made this clear enough a question.

• As $\lambda = 2\pi R \sigma$ the two answers are identical. – my2cts Apr 9 at 12:48

Your solution is actually correct. The difference between your solution and the one you quote is that $$\lambda/(2\pi \epsilon r)$$ is the field of a wire, not that of a cylinder of finite radius $$R$$. For a wire one usually gives a linear charge density $$\lambda$$ but for a conducting cylinder this charge is on its surface, so the charge is given through a surface charge density $$\sigma$$. The total charge enclosed by your Gaussian cylinder (of radius $$r$$ and length $$\ell$$) is the charge on the surface of your conducting cylinder (of length $$\ell$$ and radius $$R): $$Q=\sigma \times 2\pi R\times \ell\, .$$ Note that, for the wire version of this problem, $$Q=\lambda\times \ell$$ and you would recover the expression given in your link.

the electric field outside any conductor surface (in your question charges are at surface) is $$\cfrac{\sigma}{\epsilon o}$$

in non uniform charge distribution $$\sigma$$ may be different at different points but question states uniform surface charge distribution

link describes cylinder of volume charge density not surface one like in your question

this is true for every case in electrostatics, the electric field outside surface of conductor is as above

as electric field inside conductor is $$0$$ , your question states a cylinder made of conducting material

in a conductor charges always accumulate at surface to make flux $$0$$ inside since $$E=$$ $$0$$ (inside conductor) so flux should be $$0$$ as well.

so the link you have mentioned doesn't mention conducting word for cylinder of surface charge density as in your question

question states surface charge density $$\sigma$$ not volume charge density like in link