Search results for: Temporal trend

Authors: Rafat Alshorman, Walter Hussak

Abstract:

In mobile environments, unspecified numbers of transactions arrive in continuous streams. To prove correctness of their concurrent execution a method of modelling an infinite number of transactions is needed. Standard database techniques model fixed finite schedules of transactions. Lately, techniques based on temporal logic have been proposed as suitable for modelling infinite schedules. The drawback of these techniques is that proving the basic serializability correctness condition is impractical, as encoding (the absence of) conflict cyclicity within large sets of transactions results in prohibitively large temporal logic formulae. In this paper, we show that, under certain common assumptions on the graph structure of data items accessed by the transactions, conflict cyclicity need only be checked within all possible pairs of transactions. This results in formulae of considerably reduced size in any temporal-logic-based approach to proving serializability, and scales to arbitrary numbers of transactions.

Keywords: multi-step transactions, serializability, directed graph.

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Authors: Constantin Pehoiu

Abstract:

School physical education, through its objectives and contents, efficiently valorizes the pupils- abilities, developing them, especially the coordinative skill component, which is the basis of movement learning, of the development of the daily motility and also of the special, refined motility required by the practice of certain sports. Medium school age offers the nervous and motor substratum needed for the acquisition of complex motor habits, a substratum that is essential for the coordinative skill. Individuals differ as to the level at which this function is performed, the extent to which this function turns an individual into a person that is adapted and adaptable to complex and various situations. Spatio-temporal orientation, together with movement combination and coupling, and with kinesthetic, balance, motor reaction, movement transformation and rhythm differentiation form the coordinative skills. From our viewpoint, these are characteristic features with high levels of manifestation in a complex psychomotor act - valorizing the quality of one-s talent - as well as indices pertaining to one-s psychomotor intelligence and creativity.

Keywords: development, lesson, spatio-temporal orientation, physical education.

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Authors: Shigeaki Sakurai, Makino Kyoko, Shigeru Matsumoto

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This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The trend rules represent changes of numerical values related to evaluation objects. In the prediction phase, the method applies new text sequential data to the trend rules and evaluates which evaluation objects are attractive. This paper verifies the effect of the proposed method by using stock price sequences and news headline sequences. In these sequences, each stock brand corresponds to an evaluation object. This paper discusses validity of predicted attractive evaluation objects, the process time of each phase, and the possibility of application tasks.

Keywords: Trend rule, frequent pattern, numerical sequential data, text sequential data, evaluation object.

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: Long-short-term memory, re-identification, security critical application, spatial-temporal awareness.

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Authors: Wanli Ma, Dat Tran, Dharmendra Sharma

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The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.

Keywords: Artificial Immune Systems, ComputationalIntelligence, Negative Selection, Pattern Discovery.

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Authors: Ines Hamdi, Mohamed Ben Ahmed

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The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower

Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.

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Authors: Jiahua Zhang, Qing Chang, Fengmei Yao

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Studying on the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics and the adaptation of ecosystems to global change. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset and climate data were used to analyze the dynamics of grassland phenology as well as their correlation with climatic factors in different eco-geographic regions and elevation units across the Tibetan Plateau. The results showed that during 2003–2012, the start of the grassland greening season (SOS) appeared later while the end of the growing season (EOS) appeared earlier following the plateau’s precipitation and heat gradients from southeast to northwest. The multi-year mean value of SOS showed differences between various eco-geographic regions and was significantly impacted by average elevation and regional average precipitation during spring. Regional mean differences for EOS were mainly regulated by mean temperature during autumn. Changes in trends of SOS in the central and eastern eco-geographic regions were coupled to the mean temperature during spring, advancing by about 7d/°C. However, in the two southwestern eco-geographic regions, SOS was delayed significantly due to the impact of spring precipitation. The results also showed that the SOS occurred later with increasing elevation, as expected, with a delay rate of 0.66 d/100m. For 2003–2012, SOS showed an advancing trend in low-elevation areas, but a delayed trend in high-elevation areas, while EOS was delayed in low-elevation areas, but advanced in high-elevation areas. Grassland SOS and EOS changes may be influenced by a variety of other environmental factors in each eco-geographic region.

Keywords: Grassland, phenology, MODIS, eco-geographic regions, elevation, climatic factors, Tibetan Plateau.

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Authors: Miriam Sosa-Díaz, Faustino Sánchez-Garduño

Abstract:

In population dynamics the study of both, the abundance and the spatial distribution of the populations in a given habitat, is a fundamental issue a From ecological point of view, the determination of the factors influencing such changes involves important problems. In this paper a mathematical model to describe the temporal dynamic and the spatiotemporal dynamic of the interaction of three populations (pollinators, plants and herbivores) is presented. The study we present is carried out by stages: 1. The temporal dynamics and 2. The spatio-temporal dynamics. In turn, each of these stages is developed by considering three cases which correspond to the dynamics of each type of interaction. For instance, for stage 1, we consider three ODE nonlinear systems describing the pollinator-plant, plant-herbivore and plant-pollinator-herbivore, interactions, respectively. In each of these systems different types of dynamical behaviors are reported. Namely, transcritical and pitchfork bifurcations, existence of a limit cycle, existence of a heteroclinic orbit, etc. For the spatiotemporal dynamics of the two mathematical models a novel factor are introduced. This consists in considering that both, the pollinators and the herbivores, move towards those places of the habitat where the plant population density is high. In mathematical terms, this means that the diffusive part of the pollinators and herbivores equations depend on the plant population density. The analysis of this part is presented by considering pairs of populations, i. e., the pollinator-plant and plant-herbivore interactions and at the end the two mathematical model is presented, these models consist of two coupled nonlinear partial differential equations of reaction-diffusion type. These are defined on a rectangular domain with the homogeneous Neumann boundary conditions. We focused in the role played by the density dependent diffusion term into the coexistence of the populations. For both, the temporal and spatio-temporal dynamics, a several of numerical simulations are included.

Keywords: Bifurcation, heteroclinic orbits, steady state, traveling wave.

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Authors: Y. Huang, Q. J. Tian, L. T. Du, J. Liu, S. S. Li

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Understanding how precipitation inter-annually changes and its implication in agricultural drought and production change in winter wheat (Triticum aestivum L.) growth season is critical for crop production in China. MODIS Temperature-Vegetation Dryness Index (TVDI) and daily mean precipitation time series for the main growth season(Feb. to May) of winter wheat from 2000 to 2010 were used to analyze the distribution of trends of precipitation, agricultural drought and winter wheat yield change respectively, and relationships between them in North China region(Huang-huai-hai region, HHH region), China. The results indicated that the trend of precipitation in HHH region past 11 years was increasing, which had induced generally corresponding decreasing trend of agricultural drought and increasing trend of wheat yield, while the trend of drought was spatially diverse. The study could provide a basis for agricultural drought research during winter wheat season in HHH region under the ground of climate change.

Keywords: drought, MODIS, precipitation change, TVDI, winter wheat production

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Authors: Milad Kurdi, Hadi Farhadian, Teymour Eslamkish

Abstract:

This study examined the hydro-geochemistry of Qare-Sou catchment and Gorgan Gulf in order to determine the spatial distribution of major ions. In this regard, six hydrometer stations in the catchment and four stations in Gorgan Gulf were chosen and the samples were collected. Results of spatial and temporal distribution of major ions have shown similar variation trends for calcium, magnesium, and bicarbonate ions. Also, the spatial trend of chloride, sulfate, sodium and potassium ions were same as Electrical Conductivity (EC) and Total Dissolved Solid (TDS). In Nahar Khoran station, the concentrations of ions were more than other stations which may be related to human activities and the role of geology. The Siah Ab station’s ions showed high concentration which is may be related to the station’s close proximity to Gorgan Gulf and the return of water to Qare-Sou River. In order to determine the interaction of water and rock, the Gibbs diagram was used and the results showed that water of the river falls in the rock range and it is affected more by weathering and reaction between water and stone and less by evaporation and crystallization. Assessment of the quality of river water by using graphic methods indicated that the type of water in this area is Ca-HCO₃-Mg. Major ions concentration in Qare-Sou in the universal average was more than but not more than the allowed limit by the World Health Organization and China Standard Organization. A comparison of ions concentration in Gorgan Gulf, seas and oceans showed that the pH in Gorgan Gulf was more than the other seas but in Gorgan Gulf the concentration of anion and cation was less than other seas.

Keywords: Hydro-geochemistry, Qare-Sou River, Gorgan Gulf, major ions, Gibbs diagram, water quality, graphical methods.

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Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

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Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may damage ToM by affecting on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks, (1) each patient group performed worse than HC; (2) there were no significant differences between LTLE and RTLE groups; and (3) the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: Cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind.

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Authors: Tri Sulistyaningtyas, Yani Suryani, Dana Waskita, Linda Handayani Sukaemi, Ferry Fauzi Hermawan

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The enthusiasm of foreigners studying the Indonesian language by Foreign Speakers (BIPA) was documented in a sitcom "International Class". Tone and stress when they speak the Indonesian language is unique and different from Indonesian pronunciation. By using the Praat program, this research aims to describe prosodic Indonesian language which is spoken by ‘International Class” actors consisting of Abbas from Nigeria, Lee from Korea, and Kotaro from Japan. Data for the research are taken from the video sitcom "International Class" that aired on Indonesian television. The results of this study revealed that pitch movement that arises when pronouncing Indonesian sentences was up and down gradually, there is also a rise and fall sharply. In terms of stress, respondents tend to contain a lot of stress when pronouncing Indonesian sentences. Meanwhile, in terms of temporal structure, the duration pronouncing Indonesian sentences tends to be longer than that of Indonesian speakers.

Keywords: Melodic structure, temporal structure, prosody, experimental phonetics, international class.

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Authors: Khaled E. A. Negm

Abstract:

A neuron can emit spikes in an irregular time basis and by averaging over a certain time window one would ignore a lot of information. It is known that in the context of fast information processing there is no sufficient time to sample an average firing rate of the spiking neurons. The present work shows that the spiking neurons are capable of computing the radial basis functions by storing the relevant information in the neurons' delays. One of the fundamental findings of the this research also is that when using overlapping receptive fields to encode the data patterns it increases the network-s clustering capacity. The clustering algorithm that is discussed here is interesting from computer science and neuroscience point of view as well as from a perspective.

Keywords: Temporal Neurons, RBF Recognition, Perturbation, On Line Recognition.

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Authors: Fantazi Walid, Ezzedine Tahar, Bargaoui Zoubeida

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This research presents the main ideas to implement an intelligent system composed by communicating wireless sensors measuring environmental data linked to drought indicators (such as air temperature, soil moisture , etc...). On the other hand, the setting up of a spatio temporal database communicating with a Web mapping application for a monitoring in real time in activity 24:00 /day, 7 days/week is proposed to allow the screening of the drought parameters time evolution and their extraction. Thus this system helps detecting surfaces touched by the phenomenon of drought. Spatio-temporal conceptual models seek to answer the users who need to manage soil water content for irrigating or fertilizing or other activities pursuing crop yield augmentation. Effectively, spatiotemporal conceptual models enable users to obtain a diagram of readable and easy data to apprehend. Based on socio-economic information, it helps identifying people impacted by the phenomena with the corresponding severity especially that this information is accessible by farmers and stakeholders themselves. The study will be applied in Siliana watershed Northern Tunisia.

Keywords: WSN, database spatio-temporal, GIS, web-mapping, indicator of drought.

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Authors: M.Jalali Azizpour, S.Norouzi, , H.Mohammadi Majd, D.Sajedipour , R.Mohammadi Sadr, M.Derakhshan Mehr, S.A Shoabi, R.Mohammadi

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In this paper, the techniques for estimating the residual stress in high velocity oxy fuel thermal spray coatings have been discussed and compared. The development trend and the last investigation have been studied. It is seemed that the there is not effective study on the effect of the peening action in HVOF analytically and numerically.

Keywords: HVOF, WC-Co, Residual stress, Compressive stress

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Authors: María L. Jalón, Feargal Brennan

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A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response.

Keywords: Non-stationary stochastic optimization, oscillating water column, temporal variability, wave energy.

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Authors: Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

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In this paper, we propose an improved fast search algorithm using combined histogram features and temporal division method for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Another one is ordinal feature which is robust to color distortion. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 30 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 120ms, and Equal Error Rate (ERR) of 1% is achieved, which is more accurately and robust than conventional fast video search algorithm.

Keywords: Fast search, Adjacent pixel intensity differencequantization (APIDQ), DC image, Histogram feature.

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: HTM, Real time anomaly detection, ECG, Cardiac Anomalies.

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Authors: Yi-Shiang Shiu, Meng-Lung Lin, Kang-Tsung Chang, Tzu-How Chu

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Most paddy rice fields in East Asia are small parcels, and the weather conditions during the growing season are usually cloudy. FORMOSAT-2 multi-spectral images have an 8-meter resolution and one-day recurrence, ideal for mapping paddy rice fields in East Asia. To map rice fields, this study first determined the transplanting and the most active tillering stages of paddy rice and then used multi-temporal images to distinguish different growing characteristics between paddy rice and other ground covers. The unsupervised ISODATA (iterative self-organizing data analysis techniques) and supervised maximum likelihood were both used to discriminate paddy rice fields, with training areas automatically derived from ten-year cultivation parcels in Taiwan. Besides original bands in multi-spectral images, we also generated normalized difference vegetation index and experimented with object-based pre-classification and post-classification. This paper discusses results of different image classification methods in an attempt to find a precise and automatic solution to mapping paddy rice in Taiwan.

Keywords: paddy rice fields; multi-temporal; FORMOSAT-2images, normalized difference vegetation index, object-basedclassification.

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Authors: Mohammad Borhani, Ahmad Jamshidzaei, Mehdi Koohsari

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The study of climate has captivated human interest throughout history. In response to this fascination, individuals historically organized their daily activities in alignment with prevailing climatic conditions and seasonal variations. Understanding the elements and specific climatic parameters of each region, such as precipitation, which directly impacts human life, is essential because, in recent years, there has been a significant increase in heavy rainfall in various parts of the world attributed to the effects of climate change. Climate prediction models suggest a future scenario characterized by an increase in severe precipitation events and related floods on a global scale. This is a result of human-induced greenhouse gas emissions causing changes in the natural precipitation patterns. The present study focused on examining the trend of monthly, seasonal, and annual precipitation in Sistan and Baluchestan provinces. The study employed data obtained from 13 precipitation measurement stations managed by the Iran Water Resources Management Company, encompassing daily precipitation records spanning the period from 1997 to 2016. The results indicated that the total monthly precipitation at the studied stations in Sistan and Baluchestan province follows a sinusoidal trend. The highest intense precipitation was observed in January, February, and March, while the lowest occurred in September, October, and then November. The investigation of the trend of seasonal precipitation in this province showed that precipitation follows an upward trend in the autumn season, reaching its peak in winter, and then shows a decreasing trend in spring and summer. Also, the examination of average precipitation indicated that the highest yearly precipitation occurred in 1997 and then in 2004, while the lowest annual precipitation took place between 1999 and 2001. The analysis of the annual precipitation trend demonstrates a decrease in precipitation from 1997 to 2016 in Sistan and Baluchestan province.

Keywords: Climate change, extreme precipitation, greenhouse gas, trend analysis.

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Necati Içer

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Crop-hail insurance plays a vital role in managing risks and reducing the financial consequences of hail damage on crop production. Predicting insurance premium rates with short-term data is a major challenge in numerous nations because of the unique characteristics of hailstorms. This study aims to suggest a feasible approach for establishing equitable premium rates in crop-hail insurance for nations with short-term insurance data. The primary goal of the rate-making process is to determine premium rates for high and zero loss costs of villages and enhance their credibility. To do this, a technique was created using the author's practical knowledge of crop-hail insurance. With this approach, the rate-making method was developed using a range of temporal and spatial factor combinations with both hypothetical and real data, including extreme cases. This article aims to show how to incorporate the temporal and spatial elements into determining fair premium rates using short-term insurance data. The article ends with a suggestion on the ultimate premium rates for insurance contracts.

Keywords: Crop-hail insurance, premium rate, short-term insurance data, spatial and temporal parameters.

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Authors: Aissa Saoudi, Hassane Essafi

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In this work we will present a new approach for shot transition auto-detection. Our approach is based on the analysis of Spatio-Temporal Video Slice (STVS) edges extracted from videos. The proposed approach is capable to efficiently detect both abrupt shot transitions 'cuts' and gradual ones such as fade-in, fade-out and dissolve. Compared to other techniques, our method is distinguished by its high level of precision and speed. Those performances are obtained due to minimizing the problem of the boundary shot detection to a simple 2D image partitioning problem.

Keywords: Boundary shot detection, Shot transition detection, Video analysis, Video indexing.

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Authors: Daria N. Podvigina, Tatiana V. Chernigovskaya

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We have studied the temporal characteristics of bistable perception of the stimuli of two types: one involves alterations in a perceived depth and another one has an ambiguous content. We used the Necker lattice and lines of shadowed circles ambiguously perceived either as spheres or holes as stimuli of the first type. The Winson figure (the Eskimo/Indian picture) was a stimulus of the second type. We have analyzed how often the reversals occurred (reversal rate) and for how long each of the two interpretations, or percepts, was observed during one presentation (stability durations). For all three ambiguous images the reversal rate and the stability durations had similar values, which provide another evidence for a significant role of top-down processes in multistable perception.

Keywords: Multistable perception, perceived depth, reversal rate, top-down processes.

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Authors: Farhad Asadi, Hossein Sadati

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In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Hung Tuan Nguyen

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In this paper, with the purpose of further reducing the complexity of the system, while keeping its temporal and spatial focusing performance, we investigate the possibility of using optimal one bit time reversal (TR) system for impulse radio ultra wideband multi-user wireless communications. The results show that, by optimally selecting the number of used taps in the pre-filter the optimal one bit TR system can outperform the full one bit TR system. In some cases, the temporal and spatial focusing performance of the optimal one bit TR system appears to be compatible with that of the original TR system. This is a significant result as the overhead cost is much lower than it is required in the original TR system.

Keywords: Time reversal, optimal one bit, UWB, multi-user interference, inter symbol interference

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Authors: Tahseen A. Jilani, Aqil S. Burney, C. Ardil

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Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.

Keywords: Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.

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Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici

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Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.

Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.

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Authors: Amir Gandomkar, Tahereh Soltani Gord faramarzi, Parisa Safaripour Chafi, Abdol-Reza Amani

Abstract:

Temperature, humidity and precipitation in an area, are parameters proved influential in the climate of that area, and one should recognize them so that he can determine the climate of that area. Climate changes are of primary importance in climatology, and in recent years, have been of great concern to researchers and even politicians and organizations, for they can play an important role in social, political and economic activities. Even though the real cause of climate changes or their stability is not yet fully recognized, they are a matter of concern to researchers and their importance for countries has prompted them to investigate climate changes in different levels, especially in regional, national and continental level. This issue has less been investigated in our country. However, in recent years, there have been some researches and conferences on climate changes. This study is also in line with such researches and tries to investigate and analyze the trends of climate changes (temperature and precipitation) in Sefid-roud (the name of a river) basin. Three parameters of mean annual precipitation, temperature, and maximum and minimum temperatures in 36 synoptic and climatology stations in a statistical period of 49 years (1956-2005) in the stations of Sefid-roud basin were analyzed by Mann-Kendall test. The results obtained by data analysis show that climate changes are short term and have a trend. The analysis of mean temperature revealed that changes have a significantly rising trend, besides the precipitation has a significantly falling trend.

Keywords: Trend, Climate changes, Sefid-roud, Mann-Kendall

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Authors: Maryam Vafadar, Alireza Behrad, Saeed Akbari

Abstract:

Nowadays, with the emerging of the new applications like robot control in image processing, artificial vision for visual servoing is a rapidly growing discipline and Human-machine interaction plays a significant role for controlling the robot. This paper presents a new algorithm based on spatio-temporal volumes for visual servoing aims to control robots. In this algorithm, after applying necessary pre-processing on video frames, a spatio-temporal volume is constructed for each gesture and feature vector is extracted. These volumes are then analyzed for matching in two consecutive stages. For hand gesture recognition and classification we tested different classifiers including k-Nearest neighbor, learning vector quantization and back propagation neural networks. We tested the proposed algorithm with the collected data set and results showed the correct gesture recognition rate of 99.58 percent. We also tested the algorithm with noisy images and algorithm showed the correct recognition rate of 97.92 percent in noisy images.

Keywords: Back propagation neural network, Feature vector, Hand gesture recognition, k-Nearest Neighbor, Learning vector quantization neural network, Robot control, Spatio-temporal volume, Visual servoing

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