Structural representation of the system architecture 

Contexts in source publication

Context 1

... in order to recognise the prediction criteria of the diagnoses of the emotional situations of disabled persons. The research results present further development of a multi-layered model of this framework, with integration of the evaluation of localization possibilities and decision support system constructions. The knowledge of decision support systems is represented by fuzzy neural control of the speed of two wheelchair-type robots working in real time providing movement support for disabled individuals. The method of fuzzy reasoning using fuzzy logical Petri nets [15] is described in order to define the physiological state of disabled individuals by recognition of their emotions. The proposed reinforcement framework is based on the interaction of intelligent remote bio-robots, localization services, embedded decision support systems and data stored in a data warehouse (Fig. 1). The data warehouse is based on distributed information systems with important personal data of the patients and sensor monitoring data. The framework includes the adaptive moving wheelchair-type robot which is remotely communicating with a wearable human affect sensing bio-robot. To record, for reasons of e-health care, relevant episodes based on humans affect stages [2], the context aware sensors are incorporated into the design of the Human Affect Sensing Bio Robot-x (HASBR- x ) for every disabled individual, and into the local Intelligent Decision Making Agent-x (IDMA- x ) for every intelligent support providing robot. This framework allows a multi-sensor data fusion before the transmission of the data to the Remote Control Server (RCS) to minimize the TCP/IP (UDP) bandwidth usage. Multi-agent based adaptive motion control of both robots is based on an adaptive Fuzzy Neural Network Control (FNNC) approach. The architecture of the FNNC controller represents an approach of Adaptive Neural Fuzzy Inference System (ANFIS) that combines the fields of fuzzy logic and neural networks [3] (Fig. ...

Context 2

... and centralization of the standard operational processes, providing a single integration platform for the system. System services can be distributed to different hosts and be independent and loosely coupled [3]. This architecture style is used by many major software companies and it’s well proven. There is available the successor of this architectural style – Service Component Architecture. In the future it may completely replace SOA. The concept of the SOA does not apply any restrictions on services internal implementation, so the services can be treated as “black boxes” which implement certain functionality. Therefore, to evaluate the complexity of the services, Functional Points metric should be used. So, Functional Points should be measured for each service. Based on these measures the total number of Functional Points for the architecture can be calculated. The values of Functional Points might be used for obtaining performance and quality metrics. LOC values might be also obtained based on Functional Points. Since every single service in this architecture should be loosely coupled, it is essential to measure the interconnection between architecture services. Services with a low coupling have a low impact on changes in other services and could be easily reused. As long as every single service in this architecture should implement a unique functionality, it is essential to measure cohesion for each service. High cohesion should indicate the right functionality splitting between the services. A service with high cohesion has focused functionality; it’s easy maintainable and comprehensible. Let’s suggest that the system engineer has developed the following structural representation of system architecture based on SOA (see Figure 1). This system uses third-party services to obtain some information which is used for traffic jam determination (from three different providers) and map data. On the other hand, the system provides the information regarding traffic congestion to several consumers. They access that information with the help of Web applications and Web services. In addition to the interface services, which provide and consume the information, there are another three services. Two of them analyze the received events and determine an existence or absence of traffic congestion on specific roads. The remaining service has the database for storing current information regarding roads and it provides this information to other ...

Context 3

... estimate the effectiveness of e-mail gateway protection system scale model is made (see Figure ...

Context 4

... set of 67 rules from [6] and corresponding transitions of logical Petri nets are used in the transforming of arousal-valence space into five modelled emotional states for converting arousal and valence into boredom, challenge, excitement, frustration, and fun. Table 1, shows some models of logical Petri nets applied to the transforming of 89 of fuzzy inference rules for constructing a real time support information system for bio robots of the model of Fig.1. A set of 22 rules and corresponding transitions is proposed for determining galvanic skin response (GSR), heart rate (HR), heart rate variability high (HRV H ), heart rate variability low (HRV L ), skin temperature of head (ST H ), and skin temperature of finger (ST into arousal and valence. The set of 67 rules from [6] and corresponding transitions of logical Petri nets are proposed for determining the transformation of arousal-valence space into five modelled emotional states to convert arousal and valence into boredom, challenge, excitement, frustration, and fun (Table 3). Table 3. Examples of description of rules from the set of 67 rules from [6] Such rules are constructed as the schema of transitions of Logical Petri Nets proposed for determining the transformation of arousal-valence space into five modelled emotional states to convert arousal and valence into boredom, challenge, excitement, frustration, and fun. To determine the emotions of users during their relaxation state, agents HARA-1, HARA-2, IDMA-1, and IDMA-2, presented in Fig. 1, were programmed using the following reasoning algorithm of fuzzy logical Petri nets. This algorithm receives a fuzzy Petri net as an input and creates a set of linguistic descriptions corresponding to each output place of a fuzzy Petri net. Human arousal recognition agents HARA-1 and HARA-2 from Fig. 1 were programmed to use these reasoning algorithms to create some friendly advice for disabled individuals. An approach for developing the interaction architecture of mobile devices and remote server systems with additional functionalities for contextual information transmission is proposed. Some ...

Context 5

... set of 67 rules from [6] and corresponding transitions of logical Petri nets are used in the transforming of arousal-valence space into five modelled emotional states for converting arousal and valence into boredom, challenge, excitement, frustration, and fun. Table 1, shows some models of logical Petri nets applied to the transforming of 89 of fuzzy inference rules for constructing a real time support information system for bio robots of the model of Fig.1. A set of 22 rules and corresponding transitions is proposed for determining galvanic skin response (GSR), heart rate (HR), heart rate variability high (HRV H ), heart rate variability low (HRV L ), skin temperature of head (ST H ), and skin temperature of finger (ST into arousal and valence. The set of 67 rules from [6] and corresponding transitions of logical Petri nets are proposed for determining the transformation of arousal-valence space into five modelled emotional states to convert arousal and valence into boredom, challenge, excitement, frustration, and fun (Table 3). Table 3. Examples of description of rules from the set of 67 rules from [6] Such rules are constructed as the schema of transitions of Logical Petri Nets proposed for determining the transformation of arousal-valence space into five modelled emotional states to convert arousal and valence into boredom, challenge, excitement, frustration, and fun. To determine the emotions of users during their relaxation state, agents HARA-1, HARA-2, IDMA-1, and IDMA-2, presented in Fig. 1, were programmed using the following reasoning algorithm of fuzzy logical Petri nets. This algorithm receives a fuzzy Petri net as an input and creates a set of linguistic descriptions corresponding to each output place of a fuzzy Petri net. Human arousal recognition agents HARA-1 and HARA-2 from Fig. 1 were programmed to use these reasoning algorithms to create some friendly advice for disabled individuals. An approach for developing the interaction architecture of mobile devices and remote server systems with additional functionalities for contextual information transmission is proposed. Some ...

Context 6

... using the algorithm of shingles, a situation may arise where the normal mail delivery will be adopted as SPAM. To solve this problem, we propose to use "white" lists. This algorithm was realized on the python language as software modules functioning in windows and UNIX environment. On the scale model (Figure 1) the testing of realized filter has been carried out. The results of the testing are seen on the graph (Figure 3). The graph shows that though the offered generalized algorithm of filtering enlarges the load on server by 7-12%, it substantially improves “false negative” and “false positive” indicates by 8.5%. Yet, the offered generalization method of filtering has few drawbacks: • If there is only one letter or there are several of them (on condition of big except of letters) from the started distribution, they won’t be recognized as SAPM; • The given method can create delay of letters up to 5 min; • The load on server processor increases; To assess filter false activation the following formula has been ...

Context 7

... parameters. SIP-based location information is ordered using the SUBSCRIBE message, and it notifies about status changes of the object by means of a NOTIFY message. Different sources of information such as a mobile phone or other equipment can give additional information provided to a server about time moments if the sensor information is used. This function is performed by the SIP PUBLISH message functional interoperability. The notification is a user agent that generates NOTIFY requests for the purpose of notifying subscribers about the state of a resource. Typically notifiers also accept SUBSCRIBE requests to create subscriptions. Notification is the act of sending a NOTIFY message to a subscriber to inform the subscriber of the state of a resource. A subscriber is an agent that receives NOTIFY requests; these NOTIFY requests contain information about the state of a resource the subscriber is interested in. Typically subscribers also generate SUBSCRIBE requests and then send them to notification actions to create subscriptions. When a change in the subscribed state occurs, the notification immediately constructs and sends a NOTIFY request to inform subscribers of changes in the state to which the subscriber has a subscription. Mobile services can provide data about the changing position of a user’s terminal in geographical dimensions. Mobile Web services may be added to different terminals and a relationship will be possible if the interface is the same. Such a realization is inappropriate in our case because it will not have the possibility of building up sessions between moving terminals. We have to use the mobile Web services between the terminals as P2P that can use SIP sessions. The end points of mobile Web services are SIP URI. Web service end-points are two points of the URI which consist of the IP addresses. There are many different methods of recognizing physical state or behaviour by using data from a wearer's emotion recognition sensors [5-9]. A modified Arousal – Valence model from [6] was used to discover information in real time in order to provide some friendly advice to a person with movement disabilities. The framework presented in Figure 1 uses four emotion recognition sensors for each disabled individual: ECG (Electrocardiogram), SCR (skin conductance response), STH (skin temperature of head), and STF (skin temperature of finger) to provide HR(heart rate), HRVH(heart rate variability for the range of 0.15 to 0.4 Hz), HRVL (heart rate variability for the range of 0.015 to 0.15 Hz), SCR, STH , and STF inputs for defining fuzzy values of arousal and valence (Fig. ...

Context 8

... phases of transport vehicle automatic monitoring are examined for the purpose of solving our problem [3]. The generalized monitoring algorithm can be seen on Figure 1. In this phase we need to capture a frame. It can be obtained from camera or video file, which is chosen by user. OpenCV library (Open Source Computer Vision Library) was chosen for convenient frame capture from both. Given library contains different modules for computer vision, video recording and capturing and image processing. For initial processing it is required to create a module for webcam management. This module will be used to capture frames directly from the webcam. For algorithm start it is required to have two sequential frames. Preparation for captured frame analysis is held in this phase. For image contrast amplification multiplication method is used. It helps to increase the brightness value of each point on all image channels. Algorithm for preliminary processing is presented on the formula ...

Context 9

... overviewed and evaluated. Several typical traffic scenarios are discussed as well as corresponding approaches for anisotropic coverage evaluation and optimal sensor placement. For preventing of potentially dangerous transport situations it is essential to know relative position and velocity, direction and acceleration of transport traffic participants .Such kind information can be obtained either explicitly or implicitly, depending on type of information sensors employed. Taking into consideration specifics of given task, following sensor types could be applicable for ITS: microwave radars, ultrasonic and infrared range sensors. Sensing range of these sensors can reach up to 250 meters distance. Sensing zone, as a rule, represents a sector of a disk. For instance, FMCW radar systems have an aperture angle within 60 о in horizontal plain and 5 о in vertical plane. Comprehensive facilities open up since vehicles more and more widely are being provided with the global positioning system receivers, giving the coordinates of the vehicle, equipped with such a receiver. In any case, Prevention of accidents is reached by sharing of the information from the both vehicle onboard and transportation infrastructure sensors. It is obvious that all objects involved should be equipped with communications facilities. Two types of communications are possible, namely, Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) communications. In the V2V, vehicles are equipped with sensors in order to exchange information that is crucial to avoid severe situations like traffic jam avoidance. In V2I, information flow from vehicles to sensors installed on roadway infrastructure. This communication is necessary in propagating awareness about traffic conditions, especially on highways, to support safer commuting. There are several typical traffic scenarios [6], which may lead to potential accidents and then it is shown how the potential accidents can be avoided, if the vehicles involved in these situations are equipped with sensors and have communicating capabilities to exchange data with other vehicles in the inter-vehicle network. Considering the typical traffic situation depicted on Figure 1, three vehicles A, B and C are shown on the road. Initially, assume that the vehicles are not equipped with any sensors and are not part of any sensor network. Vehicle A and vehicle C cannot see each other. If vehicle A tries to overtake vehicles B, it may collide with the approaching vehicle C. These types of accidents can be avoided with the help of sensors and intelligent sensor networks. Now, consider that vehicles A, B and C are equipped with front- collision avoidance sensors using millimetre-wave radar or scanning laser. They also must have communication capability, so that they can exchange sensors’ data among themselves. Vehicles B and C can sense each other and so can vehicles A and B. So vehicle A knows that there is a vehicle approaching i.e. vehicle C and it will not overtake. Ramps, being the entries and exits of freeway systems, usually are places where many high velocity accidents take place because of the merging traffic and variation of the vehicle speed. ...

Context 10

... computing metrics are a system of indexes, allowing estimate the advantages, got at task solution concurrently on n processors, as compared to the serial solution of the same task on single processor. On the other hand, they permit to judge about the validity of usage of given processor number for solving of concrete task. Under parallel computing will understand the sequence of steps, where each step consists of i operations, implemented simultaneously by a set of i processors, working in parallel. The basis for definition of the mentioned metrics form followings characteristics: n — number of processors, used for organization of parallel calculations; O ( n ) — amount of calculations, expressed through the number of operations, executable by n processors during the task solution; T ( n ) — total time of calculations (task solution) with the use of n processors. We will arrange, that time changes discretely, and a processor executes any operation in time slice. The followings correlations are hereupon valid for time and volume of calculations: T (1) = O (1), T ( n ) ≤ O ( n ) . The last correlation formulates assertion: time of calculations can be shortened due to distributing of calculation volume on a few processors . The number of processors used by a program at a particular point in time defines the degree of parallelism P ( t ). The plot of parameter P against time is called the parallelism profile for the program. Changes in the P ( t ) depend on many factors (algorithm, available resources, compiler optimisations, etc.). A typical parallelism profile for a divide-and-conquer algorithm is shown in Fig. ...

Context 11

... far as this metrics ties up speedup, efficiency and compression metrics, it is more objective index of performance improvement due to parallel calculations, and can be considered as an common measure (integral index) defining the whole system performance. For an example will define the numerical values of metrics in respect to the task, used for parallelism profile concept illustration (Fig. 1). Supposing that the best algorithm for a successive and parallel calculation match, have: n = 8; T (1) = O (1) = O (8) = 93; T (8) = 25. ...