Urban data and urban design: A data mining approach to architecture education

The configuration of urban projects using Information and Communication Technologies is an essential aspect in the education of future architects. Students must know the technologies that will facilitate their academic and professional development, as well as anticipating the needs of the citizens and the requirements of their designs. In this paper, a data mining approach was used to outline the strategic requirements for an urban design project in an architecture course using a Project-Based Learning strategy. Informal data related to an award-winning public space (Gillett Square in London, UK) was retrieved from two social networks (Flickr and Twitter), and from its official website. The analysis focused on semantic, temporal and spatial patterns, aspects generally overlooked in traditional approaches. Text-mining techniques were used to relate semantic and temporal data, focusing on seasonal and weekly (work-leisure) cycles, and the geographic patterns were extracted both from geotagged pictures and by geocoding user locations. The results showed that it is possible to obtain and extract valuable data and information in order to determine the different uses and architectural requirements of an urban space, but such data and information can be challenging to retrieve, structure, analyze and visualize. The main goal of the paper is to outline a strategy and present a visualization of the results, in a way designed to be attractive and informative for both students and professionals – even without a technical background – so the conducted analysis may be reproducible in other urban data contexts.     

An efficient educational data mining approach to support e-learning

The e-learning is a recent development that has emerged in the educational system due to the growth of the information technology. The common challenges involved in The e-learning platform include the collection and annotation of the learning materials, organization of the knowledge in a useful way, the retrieval and discovery of the useful learning materials from the knowledge space in a more significant way, and the delivery of the adaptive and personalized learning materials. In order to handle these challenges, the proposed system is developed using five different steps of knowledge input such as the annotation of the learning materials, creation of knowledge space, indexing of learning materials using the multi-dimensional knowledge and XML structure to generate a knowledge grid and the retrieval of learning materials performed by matching the user query with the indexed database and ontology. The process is carried out in two modules such as the server module and client module. The proposed approach is evaluated using various parameters such as the precision, recall and F-measure. Comprehensive results are achieved by varying the keywords, number of documents and the K-size. The proposed approach has yielded excellent results by obtaining the higher evaluation metric, together with an average precision of 0.81, average recall of 1 and average F-measure of 0.86 for K = 2.     

A new data aggregation approach for WSNs based on open pits mining

Wireless Networks - Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding...     

A waveform alignment approach to positioning airborneradar-sounding data

A low-cost approach has been developed for positioning airborne radar-sounding data obtained over regions lacking evident visual features but having well-defined topography. By constraining radar profilometry to match a record of actual topography along the flight track, the method compensates for spatial sampling errors caused by local ground-speed variations. Profile alignment is accomplished using dynamic waveform matching and yields a transformation of the sounding data to geographic coordinates     

A distributed evolutionary classifier for knowledge discovery in data mining

This paper presents a distributed coevolutionary classifier (DCC) for extracting comprehensible rules in data mining. It allows different species to be evolved cooperatively and simultaneously, while the computational workload is shared among multiple computers over the Internet. Through the intercommunications among different species of rules and rule sets in a distributed manner, the concurrent processing and computational speed of the coevolutionary classifiers are enhanced. The advantage and performance of the proposed DCC are validated upon various datasets obtained from the UCI machine learning repository. It is shown that the predicting accuracy of DCC is robust and the computation time is reduced as the number of remote engines increases. Comparison results illustrate that the DCC produces good classification rules for the datasets, which are competitive as compared to existing classifiers in literature.     

一种解决噪声连续波雷达泄漏的新方法

噪声准连续波雷达虽能有效解决发射泄漏问题,但其经调幅断续后的发射波形不再具有时域连续性的特点,不能充分发挥噪声连续波雷达的优势。为此提出在噪声连续波雷达中采用双频发射方式来对消泄漏信号。该雷达的发射信号采用双频发射方式,利用两个载频所调制的泄漏信号的脉压-快速傅里叶变换(FFT)频谱输出数据取模相等这一特点,将两个载频分离通道中取模后的脉压-FFT频谱数据相减,可有效消除泄漏信号及其产生的"噪声基底"。仿真结果表明:双频发射噪声连续波雷达在保留原有连续波雷达信号特点的基础上,其泄漏抑制能力要优于噪声准连续波雷达。     

A bayesian approach to classification of multiresolution remote sensing data

Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.     

A statistical approach to landmine detection using broadbandelectromagnetic induction data

The response of time-domain electromagnetic induction (EMI) sensors, which have been used almost exclusively for landmine detection, is related to the amount of metal present in the object and its distance from the sensor. Unluckily, there is often a significant amount of metallic clutter in the environment that also induces an EMI response. Consequently, EMI sensors employing detection, algorithms based solely on metal content suffer from large false alarm rates. To mitigate this false alarm problem for mines with substantial metal content, statistical algorithms have been developed that exploit models of the underlying physics. In such models it is commonly assumed that the soil has a negligible effect on the sensor response, thus the object is modeled in "free space." We report on studies that were performed to test, the hypotheses that for broadband EMI sensors: 1) soil cannot be modeled as free space when the buried object has low metal content and 2) advanced signal processing algorithms can be applied to reduce the false alarm rates. Our results show that soil cannot be modeled as free space and that when modeling soil correctly our advanced algorithms reduced the false alarm probability by up to a factor of 10 in blind tests     

A neural network approach to estimating rainfall from spacebornemicrowave data

Various techniques use microwave (MW) brightness temperature (BT) data, obtained from remote sensing orbiting platforms, to calculate rain rates. The most commonly used techniques are based on regressions or other statistical methods. An emerging tool in rainfall estimation using satellite data is artificial neural networks (NNs), NNs are mathematical models that are capable of learning complex relationships. They consist of highly interconnected, interactive data processing units. NNs are implemented in this study to estimate rainfall, and backpropagation is used as a learning scheme. The inputs for the training phase are BTs and the outputs are rainfall rates, all generated by three-dimensional (3D) simulations based on a 3D stochastic, space-time rainfall model, and a 3D radiative transfer model. Once training is complete the NNs are presented with multi-frequency and polarized (horizontal and vertical) BT data, obtained from the Special Sensor Microwave/Imager (SSM/I) instrument onboard the F10 and F11 polar-orbiting meteorological satellites. Hence, rainrates corresponding to real BT measurements are generated. The rainfall rates are also estimated using a log-linear regression model. Comparison of the two approaches, using simulated data, shows that the NN can represent more accurately the underlying relationship between BT and rainrate than the regression model, Comparison of the rates, estimated by both methods, with radar-estimated rainrates shows that NNs outperform the regression model. This study demonstrates the great potential of NNs in estimating rainfall from remotely sensed data     

A game theoretic approach to power aware wireless data access

We consider a basic scenario in wireless data access: a number of mobile clients are interested in a set of data items kept at a common server. Each client independently sends requests to inform the server of its desired data items and the server replies with a broadcast channel. We are interested in studying the energy consumption characteristics in such a scenario. First, we define a utility function for quantifying performance. Based on the utility function, we formulate the wireless data access scenario as a noncooperative game - wireless data access (WDA) game. Although our proposed probabilistic data access scheme does not rely on client caching, game theoretical analysis shows that clients do not always need to send requests to the server. Simulation results also indicate that our proposed scheme, compared with a simple always-request one, increases the utility and lifetime of every client while reducing the number of requests sent, with a cost of slightly larger average query delay. We also compare the performance of our proposed scheme with two popular schemes that employ client caching. Our results show that caching-only benefits clients with high query rates at the expense of both shorter lifetime and smaller utility in other clients.     

A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process

Reinforcement learning is considered as a strong method for learning in multiagent systems environments. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are much less experienced than others, or some state-action pairs are never visited during the learning phase. Further, before the learning process is completed, an agent cannot exhibit a certain behavior in some states that may be sufficiently experienced. This shows that learning in a partially observable and dynamic multiagent systems environment still constitutes a difficult and major research problem that is worth further investigation. Motivated by this, in this paper, a novel learning approach that integrates online analytical processing (OLAP)-based data mining into the process is proposed. First, a data cube OLAP architecture that facilitates effective storage and processing of the state information reported by agents is described. This way, the action of the other agent, even one not in the visual environment of the agent under consideration, can simply be estimated by extracting online association rules, a well-known data mining technique, from the constructed data cube. Then, a new action selection model that is also based on association rules mining is presented. Finally, states that are not sufficiently experienced by mining multiple-level association rules from the proposed data cube are generalized. Experiments conducted on a well-known pursuit domain show the robustness and effectiveness of the proposed learning approach.     

A simplified approach to optimum diversity combining andequalization in digital data transmission

We present simplified derivations of some results given by Balaban and Salt (see ibid., vol.40, no.5, p.885-894 and p.895-907, 1992). We propose an equivalent single channel representation of linear diversity channels. Based on J. Salz's earlier work (1973), we obtain a simple method to derive various forms of optimum diversity combining and equalization in digital data transmission     

Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

The use of social media has become an integral part of daily routine in modern society. Social media portals offer powerful public platforms where people can freely share their opinions and feelings about various topics with large crowds. In the current study, we investigated the public opinions and sentiments towards the Syrian refugee crisis, which has affected millions of people and has become a widely discussed, polarizing topic in social media around the world. To analyze public sentiments about the topic on Twitter, we collected a total of 2381,297 relevant tweets in two languages including Turkish and English. Turkish sentiments were considered important as Turkey has welcomed the largest number of Syrian refugees and Turkish tweets carried information to reflect public perception of a refugee hosting country first handedly. We performed a comparative sentiment analysis of retrieved tweets. The results indicated that the sentiments in Turkish tweets were significantly different from the sentiments in English tweets. We found that Turkish tweets carried slightly more positive sentiments towards Syrians and refugees than neutral and negative sentiments, nevertheless the sentiments of tweets were almost evenly distributed among the three major categories. On the other hand, the largest number of English tweets by a significant margin contained neutral sentiments, which was followed by the negative sentiments. In comparison to the ratio of positive sentiments in Turkish tweets, 35% of all Turkish tweets, the proportion of English tweets contained remarkably less positive sentiments towards Syrians and refugees, only 12% of all English tweets.     

A different approach to the analysis of data in life-tests of laser diodes

A different reading of the available I–V curves is proposed for laser diodes whose characteristics display some degradation. In particular, the usual monitoring of the optical power P and of the threshold current I_th is complemented by the inspection of two more parameters, which separately or jointly contribute to the general variation of I_th. These two parameters are related to the simplest laser model, made of an ideal diode that is voltage-clamped under operating conditions, and are completely defined by those same standard measurements that lead to evaluate I_th. A different definition of the failure modes, and a deeper insight in the possible failure mechanisms are derived. Combined voltage and optical power monitoring during constant-current life-tests is also proposed as a more discriminating measurement than usually considered.     

A novel phase congruency based algorithm for online data reduction in ambulatory EEG systems

Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data-selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithm achieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement over the state-of-the-art denoising method for phase congruency.     

A comprehensive, automated approach to determining sea icethickness from SAR data

Documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal changes and is not subject to limitations caused by using predefined parameters     

A model-based mixture-supervised classification approach in hyperspectral data analysis

It is well known that there is a strong relation between class definition precision and classification accuracy in pattern classification applications. In hyperspectral data analysis, usually classes of interest contain one or more components and may not be well represented by a single Gaussian density function. In this paper, a model-based mixture classifier, which uses mixture models to characterize class densities, is discussed. However, a key outstanding problem of this approach is how to choose the number of components and determine their parameters for such models in practice, and to do so in the face of limited training sets where estimation error becomes a significant factor. The proposed classifier estimates the number of subclasses and class statistics simultaneously by choosing the best model. The structure of class covariances is also addressed through a model-based covariance estimation technique introduced in this paper.     

A tensor decomposition approach to data compression and approximation of ND systems

The method of Proper Orthogonal Decompositions (POD) is a data-based method that is suitable for the reduction of large-scale distributed systems. In this paper we propose a generalization of the POD method so as to take the ND nature of a distributed model into account. This results in a novel procedure for model reduction of systems with multiple independent variables. Data in multiple independent variables is associated with the mathematical structure of a tensor. We show how orthonormal decompositions of this tensor can be used to derive suitable projection spaces. These projection spaces prove useful for determining reduced order models by performing Galerkin projections on equation residuals. We demonstrate how prior knowledge about the structure of the model reduction problem can be used to improve the quality of approximations. The tensor decomposition techniques are demonstrated on an application in data compression. The proposed model reduction procedure is illustrated on a heat diffusion problem.