The DASDBS project: objectives, experiences, and future prospects

A retrospective of the Darmstadt database system project, also known as DASDBS, is presented. The project is aimed at providing data management support for advanced applications, such as geo-scientific information systems and office automation. Similar to the dichotomy of RSS and RDS in System R, a layered architectural approach was pursued: a storage management kernel serves as the lowest common denominator of the requirements of the various applications classes, and a family of application-oriented front-ends provides semantically richer functions on top of the kernel. The lessons that were learned from building the DASDBS system are discussed. Particular emphasis is placed on the following issues: the role of nested relations, the experiences with using object buffers for coupling the system with the programming-language environment and the learning process in implementing multilevel transactions     

On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia

A spectral window in the thermal infrared permits observations of surface temperature by satellite radiometry. The Heat Capacity Mapping Mission (HCMM) acquired 10–12 μm data at times of day favorable for estimation of surface thermal properties and the surface energy budget. Two variables, surface wetness, which controls evaporation and hence mean surface temperature, and thermal inertia, which relates the diurnal excursion of surface temperature to ground heat flux, are responsible for most observed temperature variability. These variables may be estimated from the mid night (2:30 a.m.) and early afternoon (1:30 p.m.) data from the HCMM or from the afternoon NOAA satellites. However, the HCMM data product, “apparent thermal inertia,” is potentially misleading in agricultural areas because surface evaporation reduces the amplitude of the soil heat flux compared to the amplitude in dry areas. Thus apparent thermal inertia should not be used in regions having variability in surface moisture.     

The Eurotra linguistic specifications: An overview

In this article, we outline the contents of the linguistic specifications of the Eurotra machine translation system. We start in sections 1 and 2 from some of the requirements placed by multilingual MT on the overall design of the linguistic components. We then move on to a characterization of the Eurotra interface structure (section 3), the nature of transfer (section 4), and trends towards more interlingual representations within the project (section 5). Thereafter, we concentrate on the contents of the various levels beside the interface structure (section 6) before giving a brief survey of word structure (section 7) and outlining some areas for further research (section 8).

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Discrimination discovery in scientific project evaluation: A case study

Discovering contexts of unfair decisions in a dataset of historical decision records is a non-trivial problem. It requires the design of ad hoc methods and techniques of analysis, which have to comply with existing laws and with legal argumentations. While some data mining techniques have been adapted to the purpose, the state-of-the-art of research still needs both methodological refinements, the consolidation of a Knowledge Discovery in Databases (KDD) process, and, most of all, experimentation with real data. This paper contributes by presenting a case study on gender discrimination in a dataset of scientific research proposals, and by distilling from the case study a general discrimination discovery process. Gender bias in scientific research is a challenging problem, that has been tackled in the social sciences literature by means of statistical regression. However, this approach is limited to test an hypothesis of discrimination over the whole dataset under analysis. Our methodology couples data mining, for unveiling previously unknown contexts of possible discrimination, with statistical regression, for testing the significance of such contexts, thus obtaining the best of the two worlds.     

Performance on agile teams: Relating iteration objectives and critical decisions to project management success factors

ContextWhile project management success factors have long been established via the golden triangle, little is known about how project iteration objectives and critical decisions relate to these success factors. It seems logical that teams’ iteration objectives would reflect project management success factors, but this may not always be the case. If not, how are teams’ objectives for iterations differing from the golden triangle of project management success factors?ObjectiveThis study identifies iteration objectives and the critical decisions that relate to the golden triangle of project management success factors in agile software development teams working in two-week iterations.MethodThe author conducted semi-structured interviews with members across three different agile software development teams using a hybrid of XP and Scrum agile methodologies. Iteration Planning and Retrospective meetings were also observed. Interview data was transcribed, coded and reviewed by the researcher and two independently trained research assistants. Data analysis involved organizing the data to identify iteration objectives and critical decisions to identify whether they relate to project management success factors.ResultsAgile teams discussed four categories of iteration objectives: Functionality, Schedule, Quality and Team Satisfaction. Two of these objectives map directly to two aspects of the golden triangle: schedule and quality. The agile teams’ critical decisions were also examined to understand the types of decisions the teams would have made differently to ensure success, which resulted in four categories of such decisions: Quality, Dividing Work, Iteration Amendments and Team Satisfaction.ConclusionThis research has contributed to the software development and project management literature by examining iteration objectives on agile teams and how they relate to the golden triangle of project management success factors to see whether these teams incorporate the golden triangle factors in their objectives and whether they include additional objectives in their iterations. What’s more, this research identified four critical decisions related to the golden triangle. These findings provide important insight to the continuing effort to better assess project management success, particularly for agile teams.     

SEBASS hyperspectral thermal infrared data: surface emissivity measurement and mineral mapping

This study focuses on mapping surface minerals using a new hyperspectral thermal infrared (TIR) sensor: the spatially enhanced broadband array spectrograph system (SEBASS). SEBASS measures radiance in 128 contiguous spectral channels in the 7.5- to 13.5-μm region with a ground spatial resolution of 2 m. In September 1999, three SEBASS flight lines were acquired over Virginia City and Steamboat Springs, Nevada. At-sensor data were corrected for atmospheric effects using an empirical method that derives the atmospheric characteristics from the scene itself, rather than relying on a predicted model. The apparent surface radiance data were reduced to surface emissivity using an emissivity normalization technique to remove the effects of temperature. Mineral maps were created with a pixel classification routine based on matching instrument- and laboratory-measured emissivity spectra, similar to methods used for other hyperspectral data sets (e.g. AVIRIS). Linear mixtures of library spectra match SEBASS spectra reasonably well, and silicate and sulfate minerals mapped remotely, agree with the dominant minerals identified with laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Though improvements in instrument calibration, atmospheric correction, and information extraction would improve the ability to map more pixels, these hyperspectral TIR data nevertheless show significant advancement over multispectral thermal imaging by mapping surface materials and lithologic units with subtle spectral differences in mineralogy.     

The PROBA-V mission: the space segment

PROBA-V (Project for On-Board Autonomy – Vegetation) is an ESA (European Space Agency) mission developed within the framework of the Agency’s General Support Technology Programme (GSTP) devoted to the observation of the Earth’s vegetation, providing data continuity with the SPOT (Satellite pour l’Observation de la Terre) 4 and 5 VEGETATION payloads as a gap-filler to the ESA Sentinel-3 mission. The PROBA-V space segment is based on a three-axis stabilized PROBA small-satellite platform of about 140 kg equipped with a state-of-the-art compact 4-band multi-spectral imager with a large field of view. The instrument’s optomechanics is based on three very compact TMA (three mirror anastigmat) telescopes placed on an optical bench. At an altitude of 820 km, the instrument is able to provide daily coverage of the Earth in three VNIR (visible and near-infrared) bands and one SWIR (short-wave infrared) spectral band, with a spatial resolution of up to 100 m × 100 m at nadir for the VNIR. The instrument raw data will be downlinked with an X-band transmitter to the ground reception station in Kiruna, Sweden. The mission control centre is located in Redu, Belgium. The image processing centre, the so-called ‘user segment’, automatically accesses the raw data and is responsible for the processing and the dissemination of the data products towards the user community. The PROBA-V spacecraft was launched on board the new European launcher Vega on 7 May 2013. It is designed for a nominal mission lifetime of 2.5 years with a possible extension to 5 years.     

Spindle thermal error prediction approach based on thermal infrared images: A deep learning method

It is essential to precisely model the spindle thermal error due to its dramatic influence on the machining accuracy. In this paper, the deep learning convolutional neural network (CNN) is used to model the axial and radial thermal errors of horizontal and vertical spindles. Unlike the traditional CNN model that relies entirely on thermal images, this model combines the thermal image with the thermocouple data to fully reflect the temperature field of the spindle. After pre-processing and data enhancement of the thermal images, a multi-classification model based on CNN is built and verified for accuracy and robustness. The experimental results show that the model prediction accuracy is approximately 90 %–93 %, which is higher than the BP model. When the spindle rotation speed changes, the model also shows good robustness. Real cutting tests show that the deep learning model has good applicability to the spindle thermal error prediction and compensation.     

Trace specifications: methodology and models

The authors summarize the trace specification language and present the trace specification methodology: a set of heuristics designed to make the reading and writing of complex specifications manageable. Also described is a technique for constructing formal, executable models from specifications written using the methodology. These models are useful as proof of specification consistency and as executable prototypes. Fully worked examples of the methodology and the model building techniques are included     

Temperature and emissivity separation from multispectral thermal infrared observations

Knowledge of the surface emissivity is important for determining the radiation balance at the land surface. For heavily vegetated surfaces, there is little problem since the emissivity is relatively uniform and close to one. For arid lands with sparse vegetation, the problem is more difficult because the emissivity of the exposed soils and rocks is highly variable. With multispectral thermal infrared (TIR) observations, it is possible to estimate the spectral emissivity variation for these surfaces. We present data from the TIMS (Thermal Infrared Multispectral Scanner) instrument, which has six channels in the 8- to 12-μm region. TIMS is a prototype of the TIR portion of the ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) instrument on NASA's Terra (EOS-AM1) platform launched in December 1999. The Temperature Emissivity Separation (TES) algorithm, developed for use with ASTER data, is used to extract the temperature and six emissivities from the six channels of TIMS data. The algorithm makes use of the empirical relation between the range of observed emissivities and their minimum value. This approach was applied to the TIMS data acquired over the USDA/ARS Jornada Experimental Range in New Mexico. The Jornada site is typical of a desert grassland where the main vegetation components are grass (black grama) and shrubs (primarily mesquite) in the degraded grassland. The data presented here are from flights at a range of altitudes from 800 to 5000 m, yielding a pixel resolution from 3 to 12 m. The resulting spectral emissivities are in qualitative agreement with laboratory measurements of the emissivity for the quartz rich soils of the site. The derived surface temperatures agree with ground measurements within the standard deviations of both sets of observations. The results for the 10.8- and 11.7-μm channels show limited variation of the emissivity values over the mesquite and grass sites indicating that split window approaches may be possible for conditions like these.     

Gender recognition from visible and thermal infrared facial images

Most present research of gender recognition focuses on visible facial images, which are sensitive to illumination changes. In this paper, we proposed hybrid methods for gender recognition by fusing visible and thermal infrared images. First, the active appearance model is used to extract features from visible images, as well as local binary pattern features and several statistical temperature features are extracted from thermal infrared images. Then, feature selection is performed by using the F-test statistic. Third, we propose using Bayesian Networks to perform explicit and implicit fusion of visible and thermal infrared image features. For explicit fusion, we propose two Bayesian Networks to perform decision-level and feature-level fusion. For implicit fusion, we propose using features from one modality as privileged information to improve gender recognition by another modality. Finally, we evaluate the proposed methods on the Natural Visible and Infrared facial Expression spontaneous database and the Equinox face database. Experimental results show that both feature-level and decision-level fusion improve the gender recognition performance, compared to that achieved from one modality. The proposed implicit fusion methods successfully capture the role of privileged information of one modality, thus enhance the gender recognition from another modality.     

Digital Rights Management: The Open Mobile Alliance DRM specifications

Ubiquitous access to the Internet, especially from mobile phones, offers the possibility of easy access for the user to desirable contents such as songs, videos, ringtones and books. However, this easy access also exposes the owners of this content to potential fraud and piracy. Digital Rights Management technologies are used to ensure that digital content can only be consumed on authorised devices that enforce copyright protection and allow the management of content throughout its entire life cycle. This article provides an overview of Digital Rights Management with particular focus on the Open Mobile Alliances DRM standards.     

The syntactically dangerous all and plural in specifications

The items on the private check list are specific problems involving the correct use of the natural language in which the RS is written. It includes incorrect grammar, incorrect word placement, and all kinds of ambiguities. The lists of grammatical and word-placement problems are similar despite the difference in the natural languages involved. The syntactic problems are symptoms of ambiguities in meaning - a grammatical problem occurs when part of a sentence disagrees with another, and each choice in the disagreement corresponds to a different meaning. The use of plural to describe a property of elements of a set or of sets makes it difficult to determine whether the property is that of each element or of the whole set. A specification inspector can certainly search for plural constructions in a specification to examine each for its danger. Best of all is for a specification writer not to write plural statements when describing properties of each element of a set.     

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semi-empirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sun-synchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R² = 0.88; root mean square (rms) error = 2.21 °C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed – a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various land-cover types. The standard error of this coefficient estimate was 0.002 mm h^?1 K^?1 for both time periods. A strong correlation (R² > 0.87; rms. error <0.17 mm h^?1) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.     

The concept of operations: The bridge from operational requirements to technical specifications

This paper describes the role of a Concept of Operations (ConOps) document in specification and development of a software‐intensive system. It also describes the process of developing a ConOps, its use and benefits, who should develop it, and when it should be developed. The ConOps described in this paper is compared to other forms of operational concept documents. A detailed outline for ConOps documents is provided in an appendix to the paper.     

基于可见光-热红外视觉监控的车间工人跌倒检测算法

为了解决工厂车间视觉监控存在噪声干扰、光线变化、目标遮挡等问题,提出一种基于多模态视觉监控的工人跌倒检测算法.首先,采用热像仪和可见光相机获取车间内全天候监控图像,结合自适应滤波模型对图像进行降噪处理,以抑制环境噪声对监控图像的干扰;然后,构建一种改进的人体姿态特征提取网络,通过融合串联时间帧合并模块和位姿残差模块,以简化目标检测的特征图尺度,实现监控图像中工人区域被部分遮挡时姿态的实时、可靠预测;最后,设计人体轴线倾角、人体外接矩形框长宽比以及双膝盖点移动速度作为工人跌倒判别性特征,进而实现车间内工人的跌倒判别.在自建数据集和公开数据集上对所提出方法进行验证,实验结果表明,所提出算法的跌倒检测精度分别为95.6%和96.3%,与对比算法相比具有更好的准确性和实时性.     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

The ALL-TIMES project: introduction and overview

Timing analysis is important when designing and verifying time-critical embedded systems. Tool support for timing analysis has existed for a number of years, but the tools have been mostly working in isolation resulting in less efficient timing analysis processes. The European FP7 project ALL-TIMES has addressed this issue by creating interface formats, tool chains, and integrated methodologies for timing analysis where the proper combination of tools and techniques can be used in a given situation. We give an introduction and overview of the ALL-TIMES project: its objectives, tools and partners, work done, and the results.