Gotowa bibliografia na temat „Over-constrained temporal networks”

Shastri & Ajjanagadde (1993) (S&A) argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence systems trivially solve the dynamic binding problem using computers' registers and pointers, it has remained a difficult problem for connectionist systems (see Fodor & Pylyshyn 1988). S&A's temporal synchrony solution to the dynamic binding problem using synchronous firing of argument units and the entities that are bound to them illustrates one way in which connectionist networks can do thisusing a constrained but important class of long-term knowledge rules. Their structured connectionist solution allows dynamic inferencing to proceed in parallel and therefore has a number of advantages for reflexive reasoning over most other connectionist and symbolic systems.

While deep neural network-based video denoising methods have achieved promising results, it is still hard to deploy them on mobile devices due to their high computational cost and memory demands. This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. Inspired by the facts that 1) consecutive video frames usually contain redundant temporal coherency, and 2) neural networks are usually over-parameterized, we propose a multi-input multi-output (MIMO) paradigm to process consecutive video frames within one-forward-pass. The basic idea is concretized to a novel architecture termed Recurrent Multi-output Network (ReMoNet), which consists of recurrent temporal fusion and temporal aggregation blocks and is further reinforced by similarity-based mutual distillation. We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image-Signal-Processor (ISP) noise. The experimental results show that ReMoNet is both effective and efficient on video denoising. Moreover, we show that ReMoNet is more robust under higher noise level scenarios.

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively.

A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models’ predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.

The success of deep learning in extending the frontiers of artificial intelligence has accelerated the application of AI-enabled systems in addressing various challenges in different fields. In healthcare, deep learning is deployed on edge computing platforms to address security and latency challenges, even though these platforms are often resource-constrained. Deep learning systems are based on conventional artificial neural networks, which are computationally complex, require high power, and have low energy efficiency, making them unsuitable for edge computing platforms. Since these systems are also used in critical applications such as bio-medicine, it is expedient that their reliability is considered when designing them. For biomedical applications, the spatio-temporal nature of information processing of spiking neural networks could be merged with a fault-tolerant 3-dimensional network on chip (3D-NoC) hardware to obtain an excellent multi-objective performance accuracy while maintaining low latency and low power consumption. In this work, we propose a reconfigurable 3D-NoC-based neuromorphic system for biomedical applications based on a fault-tolerant spike routing scheme. The performance evaluation results over X-ray images for pneumonia (i.e., COVID-19) detection show that the proposed system achieves 88.43% detection accuracy over the collected test data and could be accelerated to achieve 4.6% better inference latency than the ANN-based system while consuming 32% less power. Furthermore, the proposed system maintains high accuracy for up to 30% inter-neuron communication faults with increased latency.

Abstract. The ocean's role in modulating the observed 1–7 Pg C yr−1 inter-annual variability in atmospheric CO2 growth rate is an important, but poorly constrained process due to sparse spatio-temporal ocean carbon measurements. Here, we investigate and develop a non-linear empirical approach to predict inorganic CO2 concentrations (total carbon dioxide (CT) and total alkalinity (AT) in the global ocean mixed-layer from hydrographic properties (temperature, salinity, dissolved oxygen and nutrients). The benefit of this approach is that once the empirical relationship is established, it can be applied to hydrographic datasets that have better spatio-temporal coverage, and therefore provide an additional constraint to diagnose ocean carbon dynamics globally. Previous empirical approaches have employed multiple linear regressions (MLR), and relied on ad-hoc geographic and temporal partitioning of carbon data to constrain complex global carbon dynamics in the mixed-layer. Synthesising a new global CT/AT carbon bottle dataset consisting of ~33 000 measurements in the open ocean mixed-layer, we develop a neural network based approach to better constrain the non-linear carbon system. The approach classifies features in the global biogeochemical dataset based on their similarity and homogeneity in a self-organizing map (SOM; Kohonen, 1988). After the initial SOM analysis, which includes geographic constraints, we apply a local linear optimizer to the neural network which considerably enhances the predictive skill of the new approach. We call this new approach SOMLO, or self-organizing multiple linear output. Using independent bottle carbon data, we compare a traditional MLR analysis to our SOMLO approach to capture the spatial CT and AT distributions. We find the SOMLO approach improves predictive skill globally by 19% for CT, with a global capacity to predict CT to within 10.9 μmol kg−1 (9.2 μmol kg−1 for AT. The non-linear SOMLO approach is particularly powerful in complex, but important regions like the Southern Ocean, North Atlantic and equatorial Pacific where residual standard errors were reduced between 25–40% over traditional linear methods. We further test the SOMLO technique using the Bermuda Atlantic time-series (BATS) and Hawaiian ocean (HOT) datasets, where hydrographic data was capable of explaining 90% of the seasonal cycle and inter-annual variability at those multi-decadal time-series stations.

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.

This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.

Abstract. The ocean's role in modulating the observed 1–7 Pg C yr−1 inter-annual variability in atmospheric CO2 growth rate is an important, but poorly constrained process due to current spatio-temporal limitations in ocean carbon measurements. Here, we investigate and develop a non-linear empirical approach to predict inorganic CO2 concentrations (total carbon dioxide (CT) and total alkalinity (AT)) in the global ocean mixed layer from hydrographic properties (temperature, salinity, dissolved oxygen and nutrients). The benefit of this approach is that once the empirical relationship is established, it can be applied to hydrographic datasets that have better spatio-temporal coverage, and therefore provide an additional constraint to diagnose ocean carbon dynamics globally. Previous empirical approaches have employed multiple linear regressions (MLR) and relied on ad hoc geographic and temporal partitioning of carbon data to constrain complex global carbon dynamics in the mixed layer. Synthesizing a new global CT/AT carbon bottle dataset consisting of ~33 000 measurements in the open ocean mixed layer, we develop a neural network based approach to better constrain the non-linear carbon system. The approach classifies features in the global biogeochemical dataset based on their similarity and homogeneity in a self-organizing map (SOM; Kohonen, 1988). After the initial SOM analysis, which includes geographic constraints, we apply a local linear optimizer to the neural network, which considerably enhances the predictive skill of the new approach. We call this new approach SOMLO, or self-organizing multiple linear output. Using independent bottle carbon data, we compare a traditional MLR analysis to our SOMLO approach to capture the spatial CT and AT distributions. We find the SOMLO approach improves predictive skill globally by 19% for CT, with a global capacity to predict CT to within 10.9 μmol kg−1 (9.2 μmol kg−1 for AT). The non-linear SOMLO approach is particularly powerful in complex but important regions like the Southern Ocean, North Atlantic and equatorial Pacific, where residual standard errors were reduced between 25 and 40% over traditional linear methods. We further test the SOMLO technique using the Bermuda Atlantic time series (BATS) and Hawaiian ocean time series (HOT) datasets, where hydrographic data was capable of explaining 90% of the seasonal cycle and inter-annual variability at those multi-decadal time-series stations.

As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.

Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
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The paper introduces a new, intrinsically discrete, path planning and collision-avoidance problem, with multiple robots and multiple goals. The issue arises in the operation of the novel Swing and Dock (SaD) locomotion for a material handling system. Its agents traverse a base grid by sequences of rotations (swings) around fixed pins. Each agent must visit an array of goal positions in minimal time while avoiding collisions. The corresponding off-line path-planning problem is NP-hard. We model the system by an extended temporal graph and introduce two integer linear programming (ILP) formulations for the minimization of the makespan, with decision variables on the nodes and the edges, respectively. Both optimizations are constrained and favor idling over detours to reduce mechanical wear. The ILP formulations, tailored to the SaD system, are general enough to be applicable for many other single- and multi-agent problems over discretized networks. We have implemented the ILPs with a gurobi solver. Computational results demonstrate and compare the effectiveness of the two formulations.

Abstract This paper offers an integrative behavioral-based physics-inspired approach to model and control traffic congestion in an efficient manner While existing physics-based approaches commonly assign density and traffic flow states with the Fundamental Diagram, this paper specifies the flow-density relation using past traffic behavior (intent) recorded over a time sliding window with constant horizon length. With this approach, traffic coordination trends can be consistently learned and incorporated into traffic planning. This is integrated with mass conservation law (continuity) to model traffic coordination as a probabilistic process and obtain traffic feasibility conditions using linear temporal logic. By spatial discretization of a network of inter-connected roads (NOIR), the NOIR is represented by a graph with inlet boundary nodes, outlet boundary nodes, and interior nodes. The paper offers a boundary control approach to manage congestion through the inlet boundary nodes. More specifically, model predictive control (MPC) is applied to control traffic congestion through the boundary of the traffic network. Therefore, the optimal boundary in flow is assigned as the solution of a constrained quadratic programming problem with equality and inequality constrained. The simulation results shows that the proposed MPC boundary controller can successfully control the traffic through the inlet boundary nodes where traffic reaches the steady state condition.

The rapid urban development in the Hurghada area since the 1980s has dramatically enhanced the potential impact of human activities. To inventory and monitor this urban development effectively, remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply. In this study, data from three satellite datasets, Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), acquired during 1987, 2000 and 2005, respectively, were used to detect and evaluate Hurghada's urban expansion. Five change detection techniques were tested to detect areas of change. The techniques considered were image differencing, image ratioing, image overlay, multidate principal component analysis (PCA) and post-classification comparison. The post-classification comparison was found to be the most accurate procedure and produced three land use/land cover (LULC) maps of the years 1987, 2000 and 2005 with overall accuracies of 87.8%, 88.9% and 92.0%, respectively. The urban expansion analysis revealed that the built-up area has expanded by 40 km2 in 18 years (1987–2005). In addition, 4.5 km2 of landfill/sedimentation was added to the sea as a result of the coastal urban development and tourist activities. The booming coastal tourism and population pressure were considered to be the main factors driving this expansion, and some natural and artificial constraints constrained the physical shape of the city. The expansion is represented by urban fringe development, linear, infill and isolated models. Topography, lithology and structures were also analysed as possible factors that influenced the expansion. The understanding of the spatial and temporal dynamics of Hurghada's urban expansion is the cornerstone for formulating a view about the future urban uses and for making the best use of the limited resources that are available [1]. A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom type classification, the radiance values were transformed to depth invariant bottom indices to reduce the effect of the water column. Subsequently, a multi component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition [2]. Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird’s eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the north western Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities [3]. The coastline of Hurghada has experienced considerable environmental stress from tourist and residential recreational activities. Uncontrolled tourist development has already caused substantial damage to inshore reefs and imbalance in the hydrodynamic pattern of the coastal sediments. The objective of this paper is to investigate environmental changes using multitemporal, multispectral satellite data to identify changes at Hurghada caused by anthropogenic influences. Major detected changes include resort beaches, protection structures and landfill areas; these changes are mainly due to human intervention. Two Landsat Thematic Mapper (TM) images acquired in 1984 and 1997 are used for this analysis. The landfill areas formed during this period are calculated at about 2.15 Km2 . Whilst landfill creates new inexpensive land and improves access to the sea for tourists, it is the cause of environmental problems. In addition, land-use/land-cover and beach changes are determined over the 13-year period [4]. The Red Sea coastal zone is characterized by its sensitive, fragile, unique natural resources and habitats. In the Hurghada coastal region, major changes in the tourism industry have taken place in the last few decades. The detection of environmental changes, in a selected site of the Red Sea coastal zone, will be helpful to protect and develop this coastal environment. A methodology for separating natural and man-made changes in satellite images was developed. It was based on the following assumptions: (1) slow changes, which occur within the range of the class reflectance, represent a natural change rather than an anthropogenic one; (2) natural changes tend to be in the same land-use/land-cover class in each date, i.e. slow changes in the reflectance, not leading to changes in the type of land-use/land-cover class from the master image to the destination one; and (3) rapid changes in the reflectance of the Earth's objects are usually related to anthropogenic activities. This technique is used to identify and assess changes along the coast of Hurghada and Ras Abu Soma, the Red Sea. Results indicate serious human impacts and the necessity for control measures and monitoring. Recommendations are presented [5]. The rapid urban development of the Hurghada area began in early 1980 to build villages and huge tourist resorts and this has continued urban development and subsequent land filling and dredging of the shoreline and the destruction of coral so far. These coastal developments have led to an increase in shoreline land filling and dredging. Despite all the environmental laws of the organization to reduce infringement on the shoreline, the abuses are still ongoing. Change detection analysis using remote sensing is a very good tool to monitor the changes condition in urban development and shoreline. Four sensors was used in this study, three of them are, Landsat Multispectral Scanner (Landsat 1 MSS), Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and the another one is SPOT XS 4 (Originally Système Probatoire de l’Observation de la Terre), acquired during 1972, 1984, 1992, 2004 and 2011, respectively, were used to detect and evaluate Hurghada’s urban expansion and shoreline changes. After the images have been geometrically, radio-metrically and atmospherically corrected using ENVI 5.0 software, the digital number was transformed to the reflectance values and the images were ready to change detection process with the integration of geographic information system using Arc GIS 10 software. The results show that changes during the 39 years of the shoreline is 6.29 km2, (5.65 km2 accretion and 0.64 km2 erosion) and urban development is 16.47 km2 the road network is the 8.738 km2.

The rapid urban development in the Hurghada area since the 1980s has dramatically enhanced the potential impact of human activities. To inventory and monitor this urban development effectively, remote sensing provides a viable source of data from which updated land cover information can be extracted efficiently and cheaply. In this study, data from three satellite datasets, Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and Terra/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), acquired during 1987, 2000 and 2005, respectively, were used to detect and evaluate Hurghada's urban expansion. Five change detection techniques were tested to detect areas of change. The techniques considered were image differencing, image ratioing, image overlay, multidate principal component analysis (PCA) and post-classification comparison. The post-classification comparison was found to be the most accurate procedure and produced three land use/land cover (LULC) maps of the years 1987, 2000 and 2005 with overall accuracies of 87.8%, 88.9% and 92.0%, respectively. The urban expansion analysis revealed that the built-up area has expanded by 40 km2 in 18 years (1987–2005). In addition, 4.5 km2 of landfill/sedimentation was added to the sea as a result of the coastal urban development and tourist activities. The booming coastal tourism and population pressure were considered to be the main factors driving this expansion, and some natural and artificial constraints constrained the physical shape of the city. The expansion is represented by urban fringe development, linear, infill and isolated models. Topography, lithology and structures were also analysed as possible factors that influenced the expansion. The understanding of the spatial and temporal dynamics of Hurghada's urban expansion is the cornerstone for formulating a view about the future urban uses and for making the best use of the limited resources that are available [1]. A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom type classification, the radiance values were transformed to depth invariant bottom indices to reduce the effect of the water column. Subsequently, a multi component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition [2]. Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird’s eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the north western Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities [3]. The coastline of Hurghada has experienced considerable environmental stress from tourist and residential recreational activities. Uncontrolled tourist development has already caused substantial damage to inshore reefs and imbalance in the hydrodynamic pattern of the coastal sediments. The objective of this paper is to investigate environmental changes using multitemporal, multispectral satellite data to identify changes at Hurghada caused by anthropogenic influences. Major detected changes include resort beaches, protection structures and landfill areas; these changes are mainly due to human intervention. Two Landsat Thematic Mapper (TM) images acquired in 1984 and 1997 are used for this analysis. The landfill areas formed during this period are calculated at about 2.15 Km2 . Whilst landfill creates new inexpensive land and improves access to the sea for tourists, it is the cause of environmental problems. In addition, land-use/land-cover and beach changes are determined over the 13-year period [4]. The Red Sea coastal zone is characterized by its sensitive, fragile, unique natural resources and habitats. In the Hurghada coastal region, major changes in the tourism industry have taken place in the last few decades. The detection of environmental changes, in a selected site of the Red Sea coastal zone, will be helpful to protect and develop this coastal environment. A methodology for separating natural and man-made changes in satellite images was developed. It was based on the following assumptions: (1) slow changes, which occur within the range of the class reflectance, represent a natural change rather than an anthropogenic one; (2) natural changes tend to be in the same land-use/land-cover class in each date, i.e. slow changes in the reflectance, not leading to changes in the type of land-use/land-cover class from the master image to the destination one; and (3) rapid changes in the reflectance of the Earth's objects are usually related to anthropogenic activities. This technique is used to identify and assess changes along the coast of Hurghada and Ras Abu Soma, the Red Sea. Results indicate serious human impacts and the necessity for control measures and monitoring. Recommendations are presented [5]. The rapid urban development of the Hurghada area began in early 1980 to build villages and huge tourist resorts and this has continued urban development and subsequent land filling and dredging of the shoreline and the destruction of coral so far. These coastal developments have led to an increase in shoreline land filling and dredging. Despite all the environmental laws of the organization to reduce infringement on the shoreline, the abuses are still ongoing. Change detection analysis using remote sensing is a very good tool to monitor the changes condition in urban development and shoreline. Four sensors was used in this study, three of them are, Landsat Multispectral Scanner (Landsat 1 MSS), Landsat Thematic Mapper (Landsat 5 TM), Landsat Enhanced Thematic Mapper Plus (Landsat 7 ETM+) and the another one is SPOT XS 4 (Originally Système Probatoire de l’Observation de la Terre), acquired during 1972, 1984, 1992, 2004 and 2011, respectively, were used to detect and evaluate Hurghada’s urban expansion and shoreline changes. After the images have been geometrically, radio-metrically and atmospherically corrected using ENVI 5.0 software, the digital number was transformed to the reflectance values and the images were ready to change detection process with the integration of geographic information system using Arc GIS 10 software. The results show that changes during the 39 years of the shoreline is 6.29 km2, (5.65 km2 accretion and 0.64 km2 erosion) and urban development is 16.47 km2 the road network is the 8.738 km2.