Query: Vital Sign Detection Method Based on Multiple Higher Order Cumulant for Ultrawideband Radar

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ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR <=SEP=> Ultra-wide Band (UWB) technology is a new, useful and safe technology in the field of wireless body networks. This paper focuses on the feasibility of estimating vital signs — specifically breathing rate and heartbeat frequency — from the spectrum of recorded waveforms, using an impulse-radio (IR) UWB radar. To this end, an analytical model is developed to perform and interpret the spectral analysis. Both the harmonics and the intermodulation between respiration and heart signals are addressed. Simulations have been performed to demonstrate how they affect the detection of vital signs and also to analyze the influence of the pulse waveform. A filter to cancel out breathing harmonics is also proposed to improve heart rate detection. The results of the experiments are presented under different scenarios which demonstrate the accuracy of the proposed technique for determining respiration and heartbeat rates. It has been shown that an IR-UWB radar can meet the requirements of typical biomedical applications such as non-invasive heart and respiration rate monitoring.

Synthetic aperture beamformer for imaging through a dielectric wall <=SEP=> A coarray-based aperture synthesis scheme using subarrays and postdata acquisition beamforming is presented for through-the-wall wideband microwave imaging applications. The wall causes wave refraction and a change in the propagation speed, both effects alter the travel time between the transmitter, the target, and the receiver. Coherent combining of the pulse waveforms emitted by the different transmitters and incident at the receivers through reflections from targets and clutter requires incorporation of wall effects into the beamformer design. Simulation results verifying the proposed synthetic aperture technique for a through-the-wall imaging (TWI) system are presented. The impact of the wall ambiguities or incorrect estimates of the wall parameters, such as thickness and dielectric constant, on performance is considered.

Detection of Human Breathing and Heartbeat by Remote Radar <=SEP=> The radar capable to detect and perform diagnostics of a state of the human being behind obstacles and in conditions of bad visibility could be used for the manifold applications. The potential consumers of the radar are: rescue services, antiterrorist detachments and law-enforcement bodies. It is possible also to use this device in medicine. In this paper, the experiments with using of continuouswave subsurface radar are described. The recorded oscillograms and their frequency spectrums for heartbeat, respiration and articulation of a man, which is taking place behind an obstacle (wall), are presented.

MIMO Radar with Widely Separated Antennas <=SEP=> MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed transmitters and receivers. While, in a general sense, MIMO radar can be viewed as a type of multistatic radar, the separate nomenclature suggests unique features that set MIMO radar apart from the multistatic radar literature and that have a close relation to MIMO communications. This article reviews some recent work on MIMO radar with widely separated antennas. Widely separated transmit/receive antennas capture the spatial diversity of the target's radar cross section (RCS). Unique features of MIMO radar are explained and illustrated by examples. It is shown that with noncoherent processing, a target's RCS spatial variations can be exploited to obtain a diversity gain for target detection and for estimation of various parameters, such as angle of arrival and Doppler. For target location, it is shown that coherent processing can provide a resolution far exceeding that supported by the radar's waveform.

Recent system applications of short-pulse ultra-wideband (UWB) technology <=SEP=> Developed in the early 1960s, time-domain electromagnetics, the study of electromagnetic-wave propagation from a time-domain perspective, has given birth to a fascinating new technology, which today is commonly referred to as ultra-wideband (UWB). It has now been slightly more than 25 years since the 1978 seminal paper of Bennett and Ross, which summarized UWB's early applications. It thus seems appropriate, given the tremendous increase in interest in the technology since the Federal Communications Commission modified its Part 15 rules to accommodate UWB transmissions, to take a look at more recent system applications of this unique technology. This paper provides a brief historical perspective of UWB, discusses recent techniques for the generation and reception of short-pulse electromagnetic waveforms, and examines a number of recently developed UWB systems in the communications, radar, and precision-positioning fields. Finally, a brief assessment of future trends for the technology is provided.

Detecting Display Energy Hotspots in Android Apps <=SEP=> Energy consumption of mobile apps has become an important consideration as the underlying devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. In this paper, we present a technique for detecting display energy hotspots - user interfaces of a mobile app whose energy consumption is greater than optimal. Our technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. In an evaluation on a set of popular Android apps, our technique was very accurate in both predicting energy consumption and ranking the display energy hotspots. Our approach was also able to detect display energy hotspots in 398 Android market apps, showing its effectiveness and the pervasiveness of the problem. These results indicate that our approach represents a potentially useful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

Secure Coding Practices in Java: Challenges and Vulnerabilities <=SEP=> The Java platform and its third-party libraries provide useful features to facilitate secure coding. However, misusing them can cost developers time and effort, as well as introduce security vulnerabilities in software. We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices.
 We observed a wide adoption of the authentication and authorization features provided by Spring Security---a third-party framework designed to secure enterprise applications. We found that programming challenges are usually related to APIs or libraries, including the complicated cross-language data handling of cryptography APIs, and the complex Java-based or XML-based approaches to configure Spring Security. In addition, we reported multiple security vulnerabilities in the suggested code of accepted answers on the StackOverfow forum. The vulnerabilities included disabling the default protection against Cross-Site Request Forgery (CSRF) attacks, breaking SSL/TLS security through bypassing certificate validation, and using insecure cryptographic hash functions. Our findings reveal the insufficiency of secure coding assistance and documentation, as well as the huge gap between security theory and coding practices.

Measuring Word Significance using Distributed Representations of Words <=SEP=> Distributed representations of words as real-valued vectors in a relatively lowdimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.

Tamperproof transmission of fingerprints using visual cryptography schemes <=SEP=> Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k-out-of-n secret sharing schemes. Whenever the number of participants from the group (n) is larger than or equal to the predetermined threshold value (k), the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k (k ≤ n) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image.

Deep Learning-Based Multimedia Analytics: A Review <=SEP=> The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.

Child Education Through Animation: An Experimental Study <=SEP=> Teachers have tried to teach their students by introducing text books along with verbal instructions in traditional education system. However, teaching and learning methods could be changed for developing Information and Communication Technology (ICT). It's time to adapt students with interactive learning system so that they can improve their learning, catching, and memorizing capabilities. It is indispensable to create high quality and realistic leaning environment for students. Visual learning can be easier to understand and deal with their learning. We developed visual learning materials (an overview of solar system) in the form of video for students of primary level using different multimedia application tools. The objective of this paper is to examine the impact of student’s abilities to acquire new knowledge or skills through visual learning materials and blended leaning that is integration of visual learning materials with teacher’s instructions. We visited a primary school in Dhaka city for this study and conducted teaching with three different groups of students (i) teacher taught students by traditional system on same materials and marked level of student’s ability to adapt by a set of questions (ii) another group was taught with only visual learning material and assessment was done with 15 questionnaires, (iii) the third group was taught with the video of solar system combined with teacher’s instructions and assessed with the same questionnaires. This integration of visual materials (solar system) with verbal instructions is a blended approach of learning. The interactive blended approach greatly promoted students ability of acquisition of knowledge and skills. Students response and perception were very positive towards the blended technique than the other two methods. This interactive blending leaning system may be an appropriate method especially for school children.

Efficient occupancy grid computation on the GPU with lidar and radar for road boundary detection <=SEP=> Accurate maps of the static environment are essential for many advanced driver-assistance systems. A new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laser-scanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.

Segmentation of White Blood Cells Image Using Adaptive Location and Iteration <=SEP=> Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the GrabCut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.

A Coplanar Reconfigurable Folded Slot Antenna Without Bias Network for WLAN Applications <=SEP=> The design of a reconfigurable single folded slot antenna is shown. Metal strips are used in the slot to manipulate the ground size around the slot, which yields to changing the slot's perimeter and thus changing the resonant frequency of the antenna. The design of a single folded slot antenna is first illustrated. Later, the design is modified to be reconfigurable. The resonant frequencies for the reconfigurable design are chosen to be applicable with the WLAN applications. The antenna design, simulation, and measurements are described. The simulated results both for the return loss and maximum gain agree with the measurements. The antenna has similar radiation patterns in both bands. The advantage of this design is that the length of the bias lines does not affect the antenna performance, thus making its design, feeding, and matching an extremely simple and low-cost procedure for antenna designers.

Opportunities and advances in ultra-wideband electronically scanned arrays <=SEP=> This paper discusses bandwidth and polarization challenges in modern dual-polarized ultra-wideband (UWB) electronically scanned array (ESA) aperture design and two recent solution advances aiming to remedy such issues: a 6:1 Planar Ultrawideband Modular Antenna (PUMA) array and a 10:1 Sliced Notch Antenna (SNA) array. The proposed arrays enable wide bandwidth, low cross-polarization, and high efficiency from both low-profile and high-profile array research directions to appeal to a wider variety of applications and design embodiments. The proposed UWB-ESAs are analyzed alongside a conventional 10:1 all-metal Vivaldi array to shed light upon modern design trends and implications.

Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques <=SEP=> Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor detection. In this paper an intelligent system is designed to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization tools, such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The detection of tumor is performed in two phases: Preprocessing and Enhancement in the first phase and segmentation and classification in the second phase

The Impact of Information Technology Investment Announcements on the Market Value of the Firm <=SEP=> Determining whether investments in information technology (IT) have an impact on firm performance has been and continues to be a major problem for information systems researchers and practitioners. Financial theory suggests that managers should make investment'decisions that maximize the value of the firm. Using event-study methodology, we provide empirical evidence on the effect of announcements of IT investments on the market value of the firm for a sample of 97 IT investments from the finance and manufacturing industries from 1981 to 1988. Over the announcement period, we find no excess returns for either the full sample or for any one of the industry subsamples. However, cross-sectional analysis reveals that the market reacts differently to announcements of innovative IT investments than to foUowup, or noninnovative investments in IT. Innovative IT investments increase firm value, while noninnovative investments do not. Furthermore, the market's reaction to announcements of innovative and noninnovative IT investments is independent of industry classification. These results indicate that, on average, IT investments are zero net present value (NPV) investments; they are worth as much as they cost. Innovative IT investments, however, increase the value of the firm.

Social Media and the Decision to Participate in Political Protest : Observations From Tahrir Square <=SEP=> Based on a survey of participants in Egypt’s Tahrir Square protests, we demonstrate that social media in general, and Facebook in particular, provided new sources of information the regime could not easily control and were crucial in shaping how citizens made individual decisions about participating in protests, the logistics of protest, and the likelihood of success. We demonstrate that people learned about the protests primarily through interpersonal communication using Facebook, phone contact, or face-to-face conversation. Controlling for other factors, social media use greatly increased the odds that a respondent attended protests on the first day. Half of those surveyed produced and disseminated visuals from the demonstrations, mainly through Facebook.

Relevant Acoustic Group Features for Automatic Sleepiness Recognition <=SEP=> This paper compares the discriminating powers of various acoustic group features for the task of automatic sleepiness recognition using three different classifiers: Voted Perceptron, Simple Logistic, and Random Forest. Interspeech 2011 Sleepiness Sub-Challenge’s “Sleepy Language Corpus” (SLC) is used to generate the 4368 acoustic features of the official baseline feature set. The feature space is divided into Low-Level Descriptor (LLD) partitions. We consider the resulting feature space in groups rather than individually. A group feature corresponds to a set of one or more LLD partitions. The relevance of various group features to sleepiness state is then evaluated using the mentioned classifiers. Employing larger feature sets has been shown to increase the classification accuracy in sleepiness classification. Our results, however, demonstrate that a much smaller subset of the baseline feature set outperforms the official Sub-Challenge baseline on the SLC test data.

Optimizing 2D Gabor Filters for Iris Recognition <=SEP=> The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.

Noninvasive blood glucose detection using near infrared sensor <=SEP=> The development of noninvasive blood glucose detector is desired to replace invasive method which is costly and uncomfortable. Near Infrared (NIR) spectroscopy is used to detect blood glucose noninvasively since glucose has specific absorbance spectrum in NIR range, 850nm-2500nm. LED is utilized as incoherent infrared source to irradiate body surface in wavelength 1550 nm. Penetrated light is then detected by InGaAs photodiode, sensitive for wavelength 850-1700 nm. As the measurement sites, earlobe is chosen since it is thin. Photodiode's current is converted into voltage using transimpedance amplifier circuit. In order to minimize high frequency noise, low pass filter is applied consecutively. Early testing is conducted by measuring penetrated light in various concentration of glucose solution with wavelength 1300nm, 1450nm, and 1550nm. Measurement indicates correlation between voltage and glucose concentration. In earlobe testing, light can penetrate tissue with detected voltage, 300mV-4V depend on the wavelength. Linear approximation and two point equation is applied to the data. This two approach extracts formula that can estimate blood glucose concentration with maximum 30% error. By using two point equation, a specific formula for each person can be obtained.

Image contrast enhancement by automatic multi-histogram equalization for satellite images <=SEP=> In this paper, a new automatic histogram equalization algorithm which is based on Bi-Histogram Equalization (BHE) is proposed. The proposed method preserves the brightness and also improves the contrast. Mean value of the intensity is used for selecting the thresholds to avoid over enhancement also improving contrast of the image. The calculated mean limits the valley points to divide the histogram into small parts which guarantee equal input and output mean brightness. In addition, normalization of image brightness is applied to assure less Absolute Mean Brightness Error (AMBE). The performances of proposed algorithm, Global Histogram equalization algorithm and Bi-Histogram equalization algorithm are measured by calculating another validity parameter called Peak Signal to Noise Ratio (PSNR). The proposed method confirms the improvement in qualitative parameters as compared with the other two algorithms considered.

Multiple Emitter Location and Signal Parameter-- Estimation <=SEP=> 

Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models <=SEP=> Yahoo’s native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is Offset a feature enhanced collaborative-filtering (CF) based event prediction algorithm. Offset is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, Offset includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal. In this work we present an online hyper-parameters tuning algorithm, which takes advantage of the system parallel map-reduce based architecture, and strives to adapt the hyper-parameters set to provide the best performance at a specific time interval. Online evaluation via bucket testing of the tuning algorithm showed a significant 4.3% revenue lift overall traffic, and a staggering 8.3% lift over Yahoo HomePage section traffic. Since then, the tuning algorithm was pushed into production, tuning both clickand conversionprediction models, and is generating a hefty estimated revenue lift of 5% yearly for Yahoo Gemini native. The proposed tuning mechanism can be easily generalized to fit any learning algorithm that continuously learns on incoming streaming data, in order to adapt its hyper-parameters to temporal changes.

Sockeye: A Toolkit for Neural Machine Translation <=SEP=> We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks. SOCKEYE also supports a wide range of optimizers, normalization and regularization techniques, and inference improvements from current NMT literature. Users can easily run standard training recipes, explore different model settings, and incorporate new ideas. In this paper, we highlight SOCKEYE’s features and benchmark it against other NMT toolkits on two language arcs from the 2017 Conference on Machine Translation (WMT): English–German and Latvian–English. We report competitive BLEU scores across all three architectures, including an overall best score for SOCKEYE’s transformer implementation. To facilitate further comparison, we release all system outputs and training scripts used in our experiments. The SOCKEYE toolkit is free software released under the Apache 2.0 license.

Document Image Quality Assessment: A Brief Survey <=SEP=> To maintain, control and enhance the quality of document images and minimize the negative impact of degradations on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.

Theoretical analysis of trend vanishing moments for directional orthogonal transforms <=SEP=> This work contributes to investigate theoretical properties of the trend vanishing moment (TVM) which the authors have defined in a previous work and applied to the directional design of 2-D nonsep-arable GenLOTs. The TVM condition can be regarded as a natural extention of 1-D VM to 2-D one and alternative of the conventional directional vanishing moment (DVM). Firstly, the definition of TVM is given to clarify what it is, and then the mathematical meaning is discussed. While the conventional DVM condition requires for the moments to vanish along lines in the frequency domain and restricts the direction to a rational factor, the TVM condition imposes the moments only point-wisely and the direction can be steered flexibly. Some significant properties of TVMs are also shown and a simulation result of zonal coding for an artifical picture is given to verify the trend surface annihilation property.

Virtual Fog: A Virtualization Enabled Fog Computing Framework for Internet of Things <=SEP=> The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

Toward machines that behave ethically better than humans do <=SEP=> With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert ethicists in the health domain. This may be useful in many applications, especially where machines interact with humans in a medical context. Additionally, when connected to a cognitive model of emotional intelligence and affective decision making, it can be explored how moral decision making impacts affective behavior.

Learning How to Combine Internal and External Denoising Methods <=SEP=> Different methods for image denoising have complementary strengths and can be combined to improve image denoising performance, as has been noted by several authors [11, 7]. Mosseri et al. [11] distinguish between internal and external methods depending whether they exploit internal or external statistics [13]. They also propose a rule-based scheme (PatchSNR) to combine these two classes of algorithms. In this paper, we test the underlying assumptions and show that many images might not be easily split into regions where internal methods or external methods are preferable. Instead we propose a learning based approach using a neural network, that automatically combines denoising results from an internal and from an external method. This approach outperforms both other combination methods and state-of-the-art stand-alone image denoising methods, hereby further closing the gap to the theoretically achievable performance limits of denoising [9]. Our denoising results can be replicated with a publicly available toolbox.