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As days rolls internet information grows exponentially and the personalized recommendation system has its vibrant area of research providing relevant information to the user needs. Hybrid recommender systems combines both implicit and explicit feedbacks from user by integrating collaborative and content based recommender system. However, the real time hybrid systems are not focusing on current temporal context of the user on providing the recommendation. This paper proposed a hybrid movie recommendation system that considers recent transactions and also demographic attributes for recommending a item to the target user. The demographic attributes aids in overcoming the cold start problem. The results of the experimental study on movie lens dataset clearly indicate that the proposed system was found to be effective by considering the recent transactions with higher ratings.

Tuberculosis is a life-threatening disease that mainly affects underdeveloped as well as developing nations. While lethal it is often resistive to antibiotics and the safest way to treat a patient is to detect the disease’s presence as soon as possible. Various techniques have been developed to diagnose tuberculosis and radiography of the chest is one of such methods that works well for over a decade.. Though an effective method still the success depends on the medical officer who examines the chest X-rays. Thus ,this paper proposes an approach for detecting X-ray abnormalities using deep learning. The systems output is assessed on two open Montgomery and Shenz en chest X-ray datasets and accuracy of 84 percent is achieved.

In restructured power system, Generation Companies (GENCOs) has an opportunity to sell power and reserve in power market to earn profit by market clearing process. Defining unit commitment problem in a competitive environment to maximize the profit of GENCOs while satisfying all the network constraints is called Profit Based Unit Commitment problem (PBUC). The main contribution of this paper is modeling and inclusion of Market Clearing Price (MCP) in PBUC problem. In Day market, MCP is determined by market operator which provides maximum social welfare for both GENCOs and Consumers.On other hand this paper proposes a novel combination of solution methodology: Improved Pre-prepared power demand (IPPD) table and Analytical Hierarchy method (AHP) for solving the optimal day ahead scheduling problem as an another contribution. In this method, the status of unit commitment is obtained by IPPD table and AHP provides an optimal solution to PBUC problem. Minimizing total operating cost of thermal units to provide maximum profit to GENCOs is called an optimal day ahead scheduling problem. Also it will be more realistic to redefine this problem to include multiple distributed resources and Electric vehicles with energy storage. Because of any uncertainties or fluctuation of renewable energy resources (RESs), Electric vehicles (EV) can be used as load, energy sources and energy storage. This would reduce cost, emission and to improve system power quality and reliability. So output power of solar (PS), wind output power (PW) and Electric Vehicles power (PEV) are modeled and included into day ahead scheduling problem.The proposed methodology is tested on a standard thermal unit system with or without RESs and EVs. Cost and emission reduction in a smart grid by maximum utilization of EVs and RESs are presented in this literature. It is indicated that the proposed method provides maximum profit to GENCOs when compared to other methodologies such as Memory Management Algorithm, Improved Particle Swarm Optimization (PSO), Muller method, Gravitational search algorithm etc.

The biggest challenge in collaborative filtering recommendation system research is data sparsity ; it mainly occurs as user rates very few items from widely available options. Data Imputation techniques address the data sparsity problem by filling the missing values and then predicting the likeliness of the user. Most of the existing imputation systems assign high ratings to the items or incorporate additional information to enhance the performance of collaborative filtering recommendations. This paper proposes an association rule-based imputation method (RUBLE) to improve the top- $N$ prediction performance of the collaborative filtering recommendation. The proposed method identifies the unfavorable items of each user using the association rule mining technique and imputes them with low values. The proposed method not only addresses the sparsity problem but also provides a better quality of recommendation by eliminating the unfavorable items in top- $N$ predictions. Existing collaborative methods can quickly adapt to the proposed method as it is method agnostic. The experimental results show that the proposed method enhances the accuracy of the traditional recommender methods by two times on average and significantly outperforms existing imputation based approaches.

Over the period of technological development, the way in which the human interact with the computing devices is evolving continuously from command line to natural user interface. At the beginning, only commands were used to instruct the computers which were enriched after the development of graphical user interface (GUI) in the year 1980 with the usage of keyboard and pointing devices for computing interactions. Though GUI made easier interactive computing environment for almost all the users of that generation, it does not offer natural way of interaction. Current technologies proposed the next generation interface called as natural user interface (NUI) where the human’s intuitive actions like touch, speech, thoughts and gestures become an input for computing interactions that aim to offer better user experience. In general, the NUI is made possible through small or large physical body movements intending to inform a message or information non-verbally and naturally consenting its opportunity in wide range of rehabilitative health applications. In this paper, the recent advancement of computing interfaces and NUI sensor-based technologies to enhance and manage human physical kinematics are reviewed. The current trends of using sensors and technologies in real-time applications of health care therapy are presented with a design of experimental sensor-based gamified therapy for hand rehabilitation.

The evolution of wireless networks from the first to fourth generation has made smart devices and technologies a significant part of our daily activities. The 5G network is a ground-breaking technology that proposes to transform the way individuals use the internet, via the creation of enabling atmosphere. 5G promises faster and superior quality with better security guarantee in comparison to preceding technologies. The software defined networking (SDN) on the other hand is an enabling technology needed to actualise the huge promises of 5G network. With extensive network in 5G, a centralised controller approach in SDN has limitations related to the performance and scalability. Several studies have advocated the use and placement of multiple controllers to improve scalability in SDN. Controller placement problem (CPP) becomes more of a challenge, especially when network nodes/links and controllers have some constraint metrics such as latency, network load and distance. This paper proposes a multicriteria clustering method that places the controllers based on a predefined constraint metrics between the controllers and the switches while minimising the overall latency. The results showed the proposed technique improved nodes distribution than existing solutions for a dense network anticipated in 5G scenario.

In Cognitive Radio Wireless Mesh Networks (CR-WMNs) the end-to-end path selection is more challenging due to its heterogeneous nature. To utilize the network resources efficiently, a cognitive user must find the best available path from the source node to the end node. The factors which may affect the path in CR-WMNs are mobility, bandwidth, throughput and number of users. In this paper, all these parameters have been considered in fuzzy logic based mechanism to select an end-to-end optimal path. In this technique, the mentioned parameters of all available paths are mapped in a fuzzy logic system to go through fuzzy rules to find the optimal path. The results emphasized on improvement of the path analyzing process and path selection. The proposed algorithm is simulated in Matlab. The simulation results demonstrate the efficiency of the fuzzy system.

Extensive research has been carried out on multilevel inverters (MLIs) during recent years, as they are used in many applications. The main problem in MLI is high number of Total Harmonic Distortions (THD's) in output at low voltage levels and switching losses because of the use of large number of switches to produce high quality output. This paper presents the design of 63-level Reduce Switch Asymmetrical Cascaded H-Bridge Multi Level Inverter (RS-ASCHBMLI) that can produce high quality output with less THD, without utilizing high number of switches. Inverter design uses asymmetric (unequal) voltage sources as an input. Switching control unit utilizes voltage Reference technique to control level generating circuitry. Level creating circuitry produces only positive levels of voltage across H-Bridge terminals. H-Bridge is used to generate polarities. THD obtain through this technique is 1.02 % by using only nine semiconductor switches.

Lazy Learning Associative Classification (LLAC) is a promising approach in the field of data mining. It is one of the associative classification methods in which it delays the processing of training datasets until it receives the test instance for the class prediction. Lazy learning associative classification can be constructed in two phases. Subset generation is the first phase and the subset evaluation is the second phase. In the past decades, many lazy learning associative classification methods have been proposed. These algorithms utilize several different methods for subset generation and subset evaluation. This paper focuses on comparative study of different lazy learning associative classification methods.

This article describes how physical therapy rehabilitation promotes functional ability of the disabled people to improve quality of life using Range of Motion exercises. The conventional rehabilitation seems to be effective; however, the efficiency of the treatment sessions is not guaranteed resulting in longer recovery period. Thus, there is a need of self-motivating and engaging training solution to support rehabilitation and enhance continuous assessment of disabled patients. The proposed framework is “AR-NUI-REHAB-MDSS,” augmented reality (AR) using natural user interface (NUI) based physical therapy rehabilitation with personalized exercise rendering and monitoring system for patients and mobile decision support system (MDSS) for therapists leading to a global solution for remote assistance. NUI allows human computer interaction intuitively through human body gestures. AR provides an entertaining environment for treatments with less-assistance. MDSS enables therapist to customize treatment plans at dynamic environments. Upper limb is considered as its functional recovery is significant and more challenging.

Purpose The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information. Design/methodology/approach The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods. Findings The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods. Research limitations/implications In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency. Originality/value The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

Home automation system has reached popularity due to advancement in communication technology where appliances are controlled over internet. It has been developed to support elderly and disabled people. Smart phones are used to receive input which is processed by Google application. In this system, android has been used to capture input voices which are transferred to Ardunio Uno. The received input signals are processed using Bluetooth interfaced Ardunio Uno which is used to control fan and light using relay module. Android operating system is used to achieve automation. Mobile phone and Bluetooth communication protocol are used to control various devices. Home automation aims to supply improved comfort, convenience and security. It helps the elderly and disabled by providing quality of life instead of requiring caregivers or institutional care.

Furtherance in Information Technology has progressed usage of Web services to extensive and exhaustive. From the gigantic volume of data, predicting optimal Web page is the cumbersome process. This paper presents an innovative methodology of minifying Web page recommendation systems by employing hybrid Levenberg–Marquardt firefly neural network algorithm along with improved fuzzy c-means clustering. Hybrid Levenberg–Marquardt firefly neural network algorithm is used to categorize the prospective and non-prospective consumer data of the Web log where improved fuzzy c-means clustering clusters the prospective data. Before a user begins the path of navigating through Internet for fetching relevant Web page, the cluster recommends the most relevant Web pages by analyzing the interests of similar users. A comparative analysis of proposed system with prevailing fuzzy k-means clustering technique exhibits that the performance of projected system is better.

Estimating the cost based on the damage for a given vehicle for insurance processing is a significant area and having huge scope for automation. At the point when a car gets harmed in a mishap, the insurer of the corresponding vehicle needs to take care of the expense. Human intervention in this process is costly and takes more time for visual inspection. By utilizing the advanced deep learning procedures, it is quite easy to prepare a model that will recognize the damaged car parts and estimate the cost accordingly. Convolutional neural network (CNN) is one kind of artificial intelligence technique commonly used for pattern recognition and classification. To get an accurate result, the model should be capable of anticipating what sort of maintenance precisely to follow. The model will be prepared by training the variety of images collected with different viewpoints of the same damaged vehicle before making the final predictions. We used transfer learning-based pre-trained VGG16 network architecture for this purpose. Though these algorithms are giving accurate predictions, memetic algorithms can still optimize the results. In addition to this, two other applications for detecting the car existence in a given image using deep learning useful for automated self-driving applications are presented.

With the improvement of web sources and the progression of science and innovation, a huge amount of data has been generated and stored. A proper and accurate classification system is in need for those datasets, so that its information can be utilized in the betterment of the society. In this paper, a new method for classification system is presented by integrating lazy learning associative classification and kNN algorithm. The proposed lazy learning associative classifier generates a good quality nearest neighbor class association rules based on the test query and predicts the class label. The experimental results shows that the propsed lazy kNN associate classifer shows betters accuracy when compared with existing lazy learning associative classier.

The dynamic spectrum nature of cognitive radio challenges the connectivity and the stability of cognitive radio ad hoc networks (CRAHNs). Clustering is considered as an appropriate technique to overcome these issues. Various algorithms for clustering formation in CRAHN have been proposed, in which different system models and metrics are considered. This paper introduces an extensive survey of the most important published algorithms. This survey classifies the proposed algorithms based on their objectives. Moreover, it presents a detailed description of their techniques, evaluations of their performance, and discussion of the features and shortcomings of each algorithm. Furthermore, it provides and discusses the open issues for future research.

In nanoscale communication, diffusion-based molecular communication (DBMC) in which information is encoded into molecule patterns by a transmitter nanomachine, has emerged as a promising communication system, particularly for biomedical and healthcare applications. Although, numerous studies have been conducted to evaluate and analyze DBMC systems, investigation on DBMC system through a multilayer channel has received less attention. The aims of this paper are to formulate channel characteristics and to evaluate the performance of multilayer DBMC channel in terms of delay spread and capacity. In this paper, the propagation of molecules over an $n-$ layer channel is assumed to follow the Brownian motion and subjected to Fick’s law of diffusion. Fourier transform is used to convert time to frequency domain functions. Besides, the multilayer channel is considered as a linear and deterministic channel. For the performance evaluation, the air-water-blood plasma medium representing the simplified multilayer diffusion model in the respiratory system was chosen. It was found that a high channel capacity can be achieved with wide transmission bandwidth, short transmission distance, and high averaged transmitted power. In addition, the findings showed that channel delay spread increases as both the transmission distance, and the pulse duration increased. By setting the symbol duration greater than the pulse duration or delay spread, an inter-symbol interference problem due to previous molecules transmission can be mitigated. These findings can be used as a guide in the development and fabrication of future artificial nanocommunication and nanonetworks systems involving multilayer transmission medium.

One of the algorithms, which prudently denote better outcomes than the traditional associative classification systems, is the Lazy learning associative classification (LLAC), where the processing of training data is delayed until a test instance is received, whereas in eager learning, before receiving queries, the system begins to process training data. Traditional method assumes that all items within a transaction is same, which is not always true. This paper recommends a new framework called lazy learning associative classification with WkNN (LLAC_WkNN) which uses weighted kNN method with LLAC, that gives a subset of rules when LLAC is applied to the dataset. In order to predict the class label of the unseen test case, the weighted kNN (WkNN) algorithm is then applied to this generated subset. This creates the enhanced accuracy of the classifier. The WkNN also gives an outlier more weight. By applying Dual Distance Weight to LLAC named as LLAC_DWkNN, this limitation of WkNN is resolved. LLAC_DWkNN gives less weightage to outliers, which improve the accuracy of the classifier, further. This algorithm has been applied to different datasets and the experiment results demonstrate that the proposed method is efficient as compared to the traditional and other existing systems.

Early detection of any sort of disease is mandatory for effective medical treatment. Medical diagnosis relies heavily on Data Mining for automated disease classification and detection. It relies on data mining algorithms to examine medical data. Liver diseases have become more common these days with many new patients being diagnosed with Heptasis B and C. Early diagnosis of Liver Disorder is essential for treatment. It can be achieved by setting up intelligent systems for early diagnose and prognosis of Liver diseases. The existing automated classification systems lack accuracy in results when compared to surgical biopsy. We propose a new hybrid model for liver syndrome classification for analysis of the patient's medical data via hybrid artificial neural network. The medical records are classified based on the possibility of existence of disease. The proposed method uses M-PSO for feature selection of input variables and M-ANN algorithm for disease classification. The presented hybrid approach significantly improves the accuracy compared to existing classification algorithms. The results of the algorithm were examined and evaluated using Spark tool in this work.

This paper presents an experimental quantitative analysis of the dynamic behavior of very high frequency (VHF) III at 174---230 MHz band for opportunistic access in Malaysia. In order to investigate the variations of the VHF channels with respect to all operating stations in the country, measurements were conducted at two locations, namely, Malaysian Institute of Microelectronic Systems and University of Tenaga Nasional, Malaysia. The experimental result shows that the overall spectrum utilization for the two locations is just about 32 and 7 % respectively. These experimental results reflect a low utilization of the VHF spectrum band which consequentially indicates the ample availability of TV white space which can be utilized via opportunistic access for services like the Internet and other services. Primarily, this work is an attempt to obtain a set of generic reference values for evaluation of spectrum usage, which can be used for other potential bands for the future deployment of secondary user applications in Malaysia. It also provides useful information to the regulator in considering the deployment of opportunistic spectrum access, which can significantly impact both economic and social communication in the country.

Cognitive radio is a system to utilize spectrum holes efficiently as a solution of spectrum scarcity. The availability of channels for secondary users is determined in the spectrum sensing phase by energy detection. Energy levels of sampled primary user’s (PU’s) signal can be measured by wavelet transform with more accuracy compared with Fourier-based methods. Wavelet packet-based spectrum sensing measures the energy level at each subcarrier and sets the decision threshold. However, at the first step of energy detection for wideband spectrum sensing, high-rate analog-to-digital converter (ADC) sampling requires a large dynamic range and high-speed signal processors. In this paper, compressed sampling for PU’s signal acquisition is proposed to reduce the rate of sampling and solve the implementation complexity of ADC. The simulation results verify that this mechanism is promising to estimate the power spectrum density (PSD) of PU’s signals. The graphs prove low side-lobes of the detected PSD and acceptable probability of detection and false alarm due to the target values and certain compression ratio.

The field of sensor based human computer interaction and robotics is rapidly developing. As there is a continuous advancement in the techniques followed, the way of using every day technological products is also improving proportionally. From man-powered to machine controlled environment the importance is given to the way we make use of a technology. The idea is about using a Microsoft Kinect device to implement a suitable interface between a machine and human where users can control machines (for e.g. Computer, robot etc.). Microsoft developed this device as a medium of gaming, but later its applications improved to various fields of research and development. The actual need of such a technology is to reduce human efforts over controlling machines and this will be the first step towards the concept of artificial intelligence. The skeletal tracking ability of Kinect provides an efficient way of controlling a device through gestures. The Kinect sensor uses a depth map and colour image as well. Gesture based human computer interaction is an emerging domain of research, which can change the way we look at the machines and what they are capable of. Once devices like Kinect is made essential in everyday machines, we can create a smarter environment where human's role will be played by machines.

The aim of this study is to introduce a conceptual framework for the new approach Intelligent Proactive Pre-emptive Unified Spectrum Handoff (IP PRO-PUSH) algorithm is presented, which is a highly potential intelligent technology to intelligently address the spectrum scarcity challenges in Cognitive Radio Mobile Ad hoc Networks (CR-MANETs). The fact that spectrum is a limited natural resource poses technology challenge to ensure the available frequency is used efficiently and able to cope with new services which definitely requires more bandwidth in the future. However, over the past years, traditional approaches to spectrum management have been challenged by new insights into actual use of spectrum. In order to improve the utilization of the overall radio spectrum, the Cognitive Radio (CR) is a useful solution to this low utilization of the radio spectrum. The variation in spectrum band is called spectrum mobility. The objective of the current study is to investigate the Intelligent-Based (IB) cognitive radio learning for IP Pro-PUSH and local routing based on spectrum mobility in mobile Cognitive Radio Ad hoc Networks (CR-MANET) along with a conceptual framework and its connections.

This study aims to evaluate, categorize and compare the Vehicular Ad-hoc Network (VANET) broadcasting protocols. Massive amount of VANET broadcasting protocols have been proposed in the literature. Aiming efficiency, reliability, scalability and reach-ability each of them adopts certain techniques to provide a certain level of functionality. This study distinguishes the VANET routing protocols in several categorizes according to the applications it may serve. By focusing into broadcasting protocols, the study further divides the reviewed algorithms according to the techniques they used to initiate the communication, which would be either through beaconing, handshaking, or instant broadcasting. These protocols are further classified according to the criteria that have been used to select the next forwarder. The criteria usually influenced by the targeted performance of the technique. Such criteria may include furthest node from the sender, the node with the best link quality, endanger nodes, nodes with high probability of forwarding and backbone nodes. Performance metrics that are used for quantitative evaluation are suggested.

This paper deliberates for a multimedia transmission scheme combining multiple description coding (MDC) and network coding (NC). Our goal is to take advantage from the property of MDC to provide quantized and compressed independent and identically distributed (iid) descriptions and also from the benefit of network coding, which uses network resources efficiently to recover lost data in the network. Recently, p-cycle NC has been introduced to recover and protect any lost or distorted descriptions at the receiver part exactly without need of retransmission. So far, MDC have not been explored using this type of NC. Compressed and coded descriptions are transmitted through the network where p-cycle NC is applied. P-cycle based algorithm is proposed for single and multiple descriptions lost. Results show that in the fixed bit rate, the PSNR (Peak Signal to Noise Ratio) of our reconstructed image and also subjective evaluation is improved significantly compared to previous work which is averaging method joint with MDC in order to conceal lost descriptions.

Mobile Multi-hop Relay (MMR) WiMAX network uses Relay stations (RSs) to extend the cell coverage, and enhances the link quality and the throughput. In MMR WiMAX networks, the number of hops between the Subscriber stations (SSs) and the Base station (BS) is allowed to be more than two hops when Non transparent RS (NT-RS) is used. However, this requires modification to the frame structure of NT-RS to reduce the delay of relaying the data packets across multiple hops. Therefore, this paper presents a new NT-RS frame structure aimed to decrease the multi-hop relaying delay in order to improve the performance of the data transmission over MMR WiMAX network. The proposed frame structure serves the sub-ordinate SSs as well as NT-RSs in the access zones, while using the relay zones to communicate with its super-ordinate stations. The performance of the proposed frame structure is tested through a simulation work. The results showed that,the forwarding delay is reduced, and hence the link layer and the Transmission control protocol (TCP) throughput are improved significantly. Key words: IEEE 802.16j, transmission control protocol (TCP) performance, non transparent relay stations (NT-RS), frame structure.

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Experimental results show that the proposed system generates less number of high quality rules which improves the classification accuracy.

Classification and association rule mining are the two important tasks addressed in the data mining literature. Associative classification method applies association rule mining technique in classification and achieves higher classification accuracy. Associative classification method typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence generating a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but indeed a challenging task. This paper proposes an efficient information gain based associative classification method using genetic network programming, which generates sufficient number of rules to construct the accurate classifier. Experimental results show that, the proposed method outperforms the existing genetic network based associative classification method and traditional decision tree classification algorithm.

Performance of the students is the most important element for any educational institution. Knowing the areas where student lags proficiency helps the student as well as the teacher to improve and make the education system to be in par with this competitive world. Aim of this paper is to provide a framework that can analyse the performance of the students. This framework was experimented with a first semester course which has 5,962 students registered. Students were given a problem, taught programming concepts relevant to solving it and they are made to experiment them immediately on a machine. Three categories of exercises practice problems; assessment problems and challenging task were given to the students through an online portal. Rubrics were designed to evaluate the performance of students. A scalable MapReduce-based analytics model using Spark framework was developed and results were visualised. Then K-means clustering algorithm was also applied to group the students based on their performance.

In recent years, there is an increased in floor vibration problems of structures like residential and commercial building. Vibration is defined as a serviceability issue related to the comfort of the occupant or damage equipment. Human activities are the main source of vibration in the building and it could affect the human comfort and annoyance of residents in the building when the vibration exceed the recommend level. A new building, Madrasah Tahfiz located at Yong Peng have vibration problem when load subjected on the first floor of the building. However, the limitation of vibration occurs on building is unknown. Therefore, testing is needed to determine the vibration behaviour (frequency, damping ratio and mode shape) of the building. Heel-drop with pace 2Hz was used in field measurement to obtain the vibration response. Since, the heel-drop test results would vary in light of person performance, test are carried out three time to reduce uncertainty. Natural frequency from Frequency Response Function analysis (FRF) is 17.4Hz, 16.8, 17.4Hz respectively for each test.

Advances in computer technologies and internet have accumulated data at an exceptional speed. Majority of data available is in textual form and might contain billions of significant observations and thousands of features. In the era of Big data, text categorization is the primary step for handling and organizing textual data. Mining of knowledge from unstructured data is a more challenging task and proper representation of unstructured data will significantly improve the performance of the knowledge extraction process. Bag of words is a basic text representation technique, but has not been much successful due to the properties synonymy and polysemy of the English language. These problems could be reduced when documents are represented as a bag of phrases. Finding the best subset of features for a problem in a large domain becomes intractable and many such problems have been proved to be NP-Hard. Optimization algorithms are designed to approach such NP-Hard problems to find nearly optimal solutions. This chapter discusses about using MapReduce programming model for statistical feature selection when documents are represented as a bag of syntactic phrases and designing a parallel ant colony optimization algorithm for feature selection.

The important aspect of information gathering is to know what people think about particular topic. With the growth of IT and Internet, resource of opinion-rich text goes on increasing and thus the importance of text classification is increased in order to determine opinion about a subject. The main goal of sentiment analysis is to define sentiment or polarity of a sentence or a piece of text. Polarity means to check whether a particular piece text is positive, negative or neutral. The goal is to extract useful data and present it in a clear way so that one can get a general idea about people’s opinions. While doing Text analytics on posts or tweets obtained from social networking site, along with text some useful information is available know as Side Data. Such side data may be found in the form of access behavior of user from web logs, referred links to sites, special symbols, emotions, etc. This side data is helpful in predicting the polarity of the text. Further, the process of classification can be done using SparkR to improve processing speed and to analyze huge amount of data.

The need for reliable data delivery at the transport layer for video transmission over IEEE 802.15.4 Wireless Sensor Networks (WSNs) has attracted great attention from the research community due to the applicability of multimedia transmission for many applications. The IEEE 802.15.4 standard is designed to transmit data within a network at a low rate and a short distance. However, the characteristics of WSNs such as dense deployment, limited processing ability, memory, and power supply provide unique challenges to transport protocol designers. Additionally, multimedia applications add further challenges such as requiring large bandwidth, large memory, and high data rate. This chapter discusses the challenges and evaluates the feasibility of transmitting data over an IEEE 802.15.4 network for different transport protocols. The analysis result highlights the comparison of standard transport protocols, namely User Datagram Protocol (UDP), Transport Control Protocol (TCP), and Stream Control Transmission Protocol (SCTP). The performance metrics are analyzed in terms of the packet delivery ratio, energy consumption, and end-to-end delay. Based on the study and analysis that has been done, the standard transport protocol can be modified and improved for multimedia data transmission in WSN. As a conclusion, SCTP shows significant improvement up to 18.635% and 40.19% for delivery ratio compared to TCP and UDP, respectively.

Recommender system (RS) is a revolutionary technique which has transformed the applications from content based to customer centric. It is the method of finding what the customer wants, it can either be data or an item. The ability to collect and compute information has enabled the emergence of recommendation techniques, and these techniques provides a better understanding of users and clients. The innovation behind recommender frameworks has advanced in the course of recent years into a rich accumulation of tools that induces the researcher and scientist to create precise recommenders. This article provides an outline of recommender systems and explains in detail about the collaborative filtering. It also defines various limitations of traditional recommendation methods and discusses the hybrid extensions by merging spatial properties of the user (item-based collaborative filtering) with users personalized preferences (user-based collaborative filtering). This hybrid system is applicable to a broader range of applications. It helps the user to find the items of their interest quickly and more precisely.

The objective of current study is to compare a new model for media mix problem with popular model named as simple log linear model. A modified approach proposed to improve the results of media mix model from simple log linear method includes the simultaneous effect of different media variables on sales. The combined effect caused by various media variables shows a synergy in the curve for sales and hence considering it makes the model much effective and accurate.

Delay Tolerant Network (DTN) is known as the solution to an Internet network where connectivity is an issue. There are existing project which uses smartphone as a physical transport of data between these intermittent networks. However, each smart phone needs to connect to each other via an infrastructure which will result in lower successful transfer rate. An automated mechanism is proposed for Ad Hoc connection between the smart-phones is to ensure connectivity which leads to higher successful transfer rate. This report presents the automated mechanism called Auto Mobile Ad Hoc Network in Delay Tolerant Network that is able to provide better reliability for data transmitted through DTN. This mechanism also allows application of the current Delay Tolerant Network to be connected to other networks and other nodes automatically using Ad Hoc mode.

There is an extensive research interest in cognitive radio mobile ad hoc networks (CR-MANETs) to improve the spectrum efficiency by developing innovative design techniques through various layers of the protocol stacks. This paper presents the optimisation of CR-MANETs by exploiting the efficient usage of the available wireless spectrum through a framework for spectrum-aware handoff. In this paper, the concept of integrated handoff management in CR-MANETs is considered. An analytical model for a spectrum handoff scheme is introduced based on spectrum mobility in which secondary users (SUs) will move to another unused spectrum band, giving priority to a Primary User (PU), while satisfying its communication quality of service (QoS). The main contribution is using the Markov chain to model the evolution of the network (node position, node speed, channel quality, etc.) and to propose the combined spectrum handoff and routing. The performance of the network is analyzed based on the Markov chain. The comparison results from both analytical modelling and simulation clearly show an improvement in the performance of the SU network in terms of the route maintenance probability and the SU throughput. It is also proved that not only the PU activity affects the performance of the handoff management scheme but also the channel transmission range and the node mobility have a significant effect on the performance of the management scheme.

In this paper, Proactive Unified Spectrum Handoff (PUSH) scheme in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route is considered in which the channel availability depends on the Primary User's (PU's) activity, secondary user's (SU's) mobility, and the channel heterogeneity. First, an analytical model for Unified Spectrum Handoff (USH) scheme is introduced in which the SUs will move to another unused spectrum band, giving priority to a PU. Then, the PUSH method is proposed in which a handoff threshold is used to initiate the handoff proactively. When a channel handoff cannot be performed due to the SU's mobility, a local flow handoff (LFH) is performed. The proposed PUSH algorithm uses the cognitive link availability prediction while considering the PU interference boundary to estimate the maximum link accessibility period. Based on the analytical model, the channel heterogeneity and the SU's mobility affected the performance of the handoff management method rather than the PU's activity. Both the simulation and analytical results demonstrated an improvement of route maintenance probability based on the LFH and cognitive link availability prediction mechanisms in PUSH.

The evolution of wireless communication had evoked the demands for more radio spectrum. Hence, radio spectrum becomes a scarce resource for the wireless applications. To overcome this problem, Cognitive Radio (CR) emerged as an intelligent wireless technology that allow SU dynamically access into the opportunity licensed spectrum. Through spectrum sensing, spectrum holes are identified and thus interference to licensed user can be avoided, which is a crucial task in CR. However, conventional spectrum sensing methods such as Energy Detection (ED) is less sensitive to the changes of PU compared to Edge Detection. Hence, Edge Energy Detection (EED) spectrum sensing technique is proposed which combines ED and Edge Detection using hard information rules. The proposed spectrum sensing technique is performed experimentally in GNU Radio and Universal Software Radio Peripheral version 2 (USRP2) platform. The performance of EED is evaluated and compared to conventional ED and Edge Detection. The results showed that EED achieved improved sensing performance than conventional ED and Edge Detection.

With the rapid growth of wireless multimedia applications over the wireless Internet in recent years, the demand for radio spectral resources has increased significantly. In order to meet the quality of service, delay, and large bandwidth requirements, various techniques such as source and channel coding, distributed streaming, multicast have been considered. In this paper, we propose a technique for multimedia transmission over the secondary user network, which uses Dynamic Spectrum Access (DSA). Sensing the radio frequency environment and avoiding busy channel by hopping to vacant channel are the major part in DSA. We address the impact of this technique to multimedia data such as audio signal and image since random hopping and different rate of hopping from one channel to another channel may influence the signal quality. With the help of Software Defined Radio (SDR), we have adopted a flexible and reconfigurable platform for this project. Simulation results are presented based on the received data transmitted through this mechanism.

In cognitive radio networks, the secondary users (SUs) switch the data transmission to another empty spectrum band to give priority to primary users (PUs). In this paper, channel switching in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route is modeled. The probability of channel availability in this route is calculated based on the PU's activity, SU's mobility, and channel heterogeneity. Based on the proposed model, the channel and link availability time are predicted. These predictions are used for channel assignment in the proposed channel allocation scheme. A handoff threshold as well as a life time threshold is used in order to reduce the handoff delay and the number of channel handoffs originating from the short channel usage time. When the channel handoff cannot be done due to the SU's mobility, the local flow handoff is performed to find an appropriate node in the vicinity of a potential link breakage and transfer the current data flow to it. The local flow handoff is performed to avoid possible flow disruption and also to reduce the delay caused by the link breakage. The study reveals that the channel heterogeneity and SU's mobility must be considered as important factors, which affect the performance of the handoff management in the CR-MANETs. The results emphasize on the improvement of the route maintenance probability after using the local flow handoff. It is also stated that the amounts of handoff requirement and handoff delay are decreased after using the predicted channel usage life time and handoff threshold time.

The paper introduces proactive integrated handoff management (IHM) scheme in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route. This scheme considers the primary user (PU) activity, secondary user (SU) mobility and channel heterogeneity in the spectrum handoff decision. To reduce the handoff delay, handoff thresholds are used. When the spectrum handoff cannot be done due to the SU's mobility, a local flow handoff (LFH) is performed to find a node in the vicinity of a potential link breakage where the data flow is transferred to the node. The proposed proactive IHM scheme predicts the cognitive link availability that considers the interference to PUs to estimate the maximum link availability time. The availability time is then used in the channel allocation scheme. The results emphasized on improvement of the route maintenance probability using LFH. It is also verified that the number of handoff needed is significantly decreased using the proposed proactive IHM scheme.

SystemC has gained wide acceptance in the design of VLSI SoCs. At the same time there exists a large number of legacy IP cores described in VHDL whose reuse and integration into SystemC ecosystem is highly demanded. However, there is a lack of any standard approach in this regard. This paper proposes an open-source framework and methodology to convert RTL VHDL IP cores to cycle-accurate SystemC designs. The SystemC output is emphasized to be human-readable and providing for clear correspondence to the source VHDL code, thus allowing further manual code changes and debug. The described framework has been implemented based on an open-source zamiaCAD platform and has been successfully applied to translate various VHDL benchmark designs.

The present development of high data rate wireless applications has led to extra bandwidth demands. However, finding a new spectrum bandwidth to accommodate these applications and services is a challenging task because of the scarcity of spectrum resources. In fact, the spectrum is utilized inefficiently for conventional spectrum allocation, so Federal Communications Commission has proposed dynamic spectrum access mechanism in cognitive radio, where unlicensed users can opportunistically borrow unused licensed spectrum, which is a challenge to obtain contiguous frequency spectrum block. This also has a significant impact on multicarrier transmission systems such as orthogonal frequency division multiplexing OFDM and multicarrier code division multiple access MC-CDMA. As a solution, this paper develops non-contiguous OFDM NC-OFDM and non-contiguous MC-CDMA NC-MC-CDMA cognitive system. The implementation of NC-OFDM and NC-MC-CDMA systems provides high data rate via a large number of non-contiguous subcarriers without interfering with the existing transmissions. The system performance evaluates NC-OFDM and NC-MC-CDMA for mobile scenario where each propagation path will experience Doppler frequency shift because of the relative motion between the transmitter and receiver. The simulation results of this paper proved that NC-OFDM system is a superior candidate than NC-MC-CDMA system considering the mobility for cognitive users. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile WiMAX has the capability to support high transmission rate at vehicle speed and provides quality of service (QoS) for different applications. However, allocating the limited resources in such wireless network to different application with variant QoS is highly complex and challenging. Many resource allocation schemes have been proposed for different wireless based network. In most cases the proposed algorithms are found to have higher complexity. In this paper, a less complex algorithm for subcarrier allocation is proposed. The proposed subcarrier allocation scheme uses biologically inspired optimization technique to reduce the overall complexity and provides adjustable quality of service for transmission in different applications. The proposed subcarrier allocation technique has reduced the overall computational time by 89.8% compared to root-finding method and 33.8% when compared to linear search technique.

The current trend and demand of wireless services requires more efficient spectrum usage. With Cognitive Radio (CR) technology, spectrum efficiency will be increased by exploiting unused licensed or primary users spectrum. Spectrum sensing mechanism is one of the main cores in CR system. To combat the problems of shadowing, multipath fading, and hidden nodes, cooperative spectrum sensing has been proposed. Testbed prototyping for sensing is a challenging task. The demand for cooperative sensing to solve the above mentioned problems leads to proposals of PHY and MAC layer solutions. Therefore, in this paper, we demonstrate an experimental platform using USRP board that can facilitate the development of PHY and MAC layers functionality for cooperative spectrum sensing. In this paper, TDMA-based MAC protocol for cooperative sensing mechanism is proposed. The observation of the preliminary experimental measurement shows significant performance in term of feasibility of cooperative decision mechanism in wireless condition.

In cognitive radio (CR), CR users or also known as secondary users (SUs) are considered as “visitors” to the licensed spectrum. During the case when the primary user (PU) claims the band of the spectrum that is occupied by the SU, the SU's ongoing call must be transferred to another unused spectrum band. The variation in spectrum band is called spectrum mobility. Spectrum mobility leads to a new handoff in cognitive radio networks, namely spectrum handoff, in which the SUs handover their spectrum band and continue their ongoing calls on an unused spectrum band with different frequency upon the appearance of the PU. In this paper, we investigate a new concept of integrated handoff management in mobile cognitive radio Ad hoc networks (CR-MANET), along with a conceptual framework, its connections and related handoff algorithm.