Muhammad Younus Javed

Simulation and performance comparison of four disk scheduling algorithms

Hard disks are being used to store huge amounts of information/data in all modern computers. Disk drives must provide faster access times in order to optimize the speed of I/O operations. In a multitasking system with many processes, disk performance can be improved by incorporating a scheduling algorithm for maintaining several pending requests in the disk queue. This paper describes the development of a simulator which uses four disk scheduling algorithms to measure their performance in terms of total head movement. These algorithms are: FCFS (First-Come, First-Served), SSTF (Shortest-Seek-Time-First), LOOK (Look-ahead) for both upward and downward directions, and C-LOOK (Circular LOOK). Five different types of test samples, containing from 8 to 50 request tracks, have been used to obtain simulation results. The developed simulator runs successfully in a multiprogramming environment and the tabulated results demonstrate that the LOOK (downward direction) algorithm provides the best results for given test samples due to the reduction of a large number of unnecessary head movements. As several wild swings are experienced by the FCFS scheme, it gives the worst scheduling performance. SSTF is much better compared to LOOK (upward direction) and C-LOOK. It has also been noticed that LOOK is more efficient than C-LOOK at all loads, whereas C-LOOK is better at high loads only, as it reduces the starvation problem. The performance of each algorithm, however, depends heavily on the number and type of requests.

Data compression through adaptive Huffman coding schemes

A number of data compression techniques have been introduced to reduce the text/data storage and transmission costs. This paper describes the development of a data compression system that employs adaptive Huffman method for generating variable-length codes. Construction of the tree is discussed for gathering latest information about the entered message. The encoder process of the system encodes frequently occurring characters with shorter bit codes and infrequently appearing characters with longer bit codes. Adaptive, sibling, swapping, escape code, and re-scaling mechanisms of the model are briefly explained as they are extremely useful in enhancing compression efficiency. The decoder process expands the encoded text back to the original text and works very much like the encoder process. Experimental results are tabulated which demonstrate that the developed system is very effective for compressing database files (provides compression ratio up to 52.51%) in a real-time environment.

Interference-aware multipath routing in wireless mesh network

The inherent mesh infrastructure of IEEE 802.11-based wireless mesh networks provides added support to construct multiple robust paths. However, based on geometric locations of wireless nodes, neighborhood interference and channel contention impose challenges on multipath routing schemes. A large body of research has been carried out to maximize aggregate end-to-end throughput using multipath routing; however, interference has not been accurately modeled in majority of the work. Based on the relative location of transmitters and receivers, information asymmetric non-coordinated interference introduces bottleneck links and significantly reduces the aggregate throughput. The impact of this interference is even observed on links several hops away. In this paper, multipath routing is integrated with topology control to manage such multilevel asymmetric interference. An optimization model has been presented with an objective to achieve optimized end-to-end throughput using multiple available paths, considering coordinated and asymmetric non-coordinated interference. The goal of this research is to develop a multipath routing strategy which can achieve better end-to-end throughput by purging badly affected asymmetric non-coordinated interfering links during path construction procedure. The proposed model and routing strategy have been tested through extensive simulations. The results clearly exhibit the efficacy of the proposed approach, which achieves better aggregate end-to-end throughput compared to existing multipath routing schemes.

DSont: DSpace to ontology transformation

Semantic web facilitates the effective sharing and reuse of existing information. Institutional repositories (IRs) are built to organize and manage the intellectual output of an institute. They generally use relational databases for maintaining metadata of digital documents. The focus of this research is to share the information of an existing IR with other information systems for discovering common interests. To process the data in semantic context, a relational database needs to be transformed into an ontology. The existing relation to ontology transformation systems produces odd results if they are applied on an IR database because its schema is meta-schema. The proposed system first creates an intermediate database, having a normalized schema for the data model of an institute preserved in an IR database and then transforms it into an ontology. Finally semantic correspondence is established between entities of source and target ontologies in order to integrate them. The system has been implemented and evaluated for its correct and lossless transformation. The results demonstrate that the transformation is correct and the information is preserved.

A statistical feature based decision tree approach for hand gesture recognition

A lot of work has been done in the field of sign language recognition all over the world. The main focus of such work is to make the life of vocally impaired people more comfortable. It also bridges the communication gap between the normal and the abnormal people. The deaf and dumb people need not only learn the standard sign language but the core issue is that they can communicate with the normal people of society. It is also not possible for all the normal people that they learn the sign language to understand whatever is said through gestures. So the communicational gap still stays there even after teaching deaf and dumb people with sign language. In this paper, an approach has been presented in which statistical features are extracted from the hand signs and are then fed to the decision tree for the recognition of the hand gestures. In this research the English alphabet gestures data set has been used and the recognized hand gestures are then represented as both the alphabetical and voice forms. This would help the impaired people to communicate with normal people in the way that they can understand.

Diversity based review of Multipath Routing Metrics of Wireless Mesh Networks

Wireless Mesh Networks (WMN) are considered as robust architecture to provide reliable connectivity to fixed and mobile users. The redundant infrastructure provided by WMN can be efficiently utilized using multipath routing. However, the performance gain of multipath routing mainly depends on efficient path selection protocols. The omni-directional wireless medium, unstable link quality and mutual path interference are some of those factors which greatly affect the performance of multipath routing protocols. Routing metrics play a pivotal role for the selection of reliable paths. This paper has investigated a variety of existing single and multi-radio routing metrics to analyze their limitations in finding multiple reliable paths around interfering neighborhood. The paper thus proposes a Diversity based Multipath Routing Metric (DMRM) which basis its calculation on diverse wireless network conditions. The routing metric not only captures interfering traffic load on the link but also considers link loss ratio, inter and intra-flow interference, multiple data rates of nodes and channel diversity during multipath selection. DMRM integrates multiple routing conditions to overcome the limitations of existing metrics. The proposed metric is integrated with Diversity-based Multipath Routing Protocol (DMRP). DMRP is designed to select optimal path in terms of packet delivery ratio, lower level of interference, fewer congested links and multipath load balancing to achieve higher throughput and reduced end-to-end packet delay.

Bounded degree energy aware topology discovery in ad hoc wireless sensor networks

In this paper, we present a strategy for topology discovery and reconfiguration in ad hoc wireless sensor networks. One of the most critical issues in designing a wireless sensor network is to optimize the number of clusterheads which consume more energy as compared to the ordinary nodes. In this paper, we briefly analyze the currently proposed topology discovery algorithms and then propose our algorithm called Bounded Degree Energy Aware Algorithm (BDEA) which elects clusterheads by weighted clustering approach. The proposed algorithm takes in to consideration the degree, energy, transmission range and mobility of a node. Load balancing is achieved by setting lower and upper bounds on the degree of a node for becoming a clusterhead. Communication cost is reduced by declaring some nodes as forwarding nodes. Energy is also conserved by setting a threshold on the amount of energy a node requires to become a clusterhead. Finally, we evaluate the performance of our proposed scheme using simulations. Performance is evaluated in terms of number of clusterheads, load balancing, reconfigurations, stability of clusters and network lifetime. Simulations show that BDEA has stability over varying transmission ranges. BDEA has a higher degree of load balancing and a longer network lifetime as compared to previously proposed topology discovery algorithms.

Transformation of DSpace Database into Ontology

Ontologies enable controlled sharing and exchange of information among autonomous data sources. DSpace is an open source software for institutional repositories. It employs a traditional database to store the metadata that provides detailed description about digital documents. We need to transform the DSpace database into an ontology in order to share and reuse its information. However, the existing transformation systems are not able to transform the DSpace database accurately into an ontology because its schema is meta-schema. The proposed system first creates a normalized relational database for the data model of an institute from DSpace database and then transforms it into an ontology. The system has been implemented and its results demonstrate that transformation is correct.

Feature based sliding window technique for face recognition

Human beings are commonly identified by biometric schemes which are concerned with identifying individuals by their unique physical characteristics. The use of passwords and personal identification numbers for detecting humans are being used for years now. Disadvantages of these schemes are that someone else may use them or can easily be forgotten. Keeping in view of these problems, biometrics approaches such as face recognition, fingerprint, iris/retina and voice recognition have been developed which provide a far better solution when identifying individuals. A number of methods have been developed for face recognition. This paper illustrates employment of Gabor filters for extracting facial features by constructing a sliding window frame. Classification is done by assigning class label to the unknown image that has maximum features similar to the image stored in the database of that class. The proposed system gives a recognition rate of 96% which is better than many of the similar techniques being used for face recognition.

Semi-automated approach for converting ERD to semi-star schema

Small and medium enterprises cannot afford the cost of creating a data warehouse and if they do so, they fail to maintain it due to high updation cost. To solve this problem, semi-star schema has already been proposed. Semi-star schema modeling is a new technique, so data warehouse designers may face problems in creating it. Some method based on source-driven approach is required to generate it because source driven approaches are stable, as far as the data is stable in a database. To avoid complexities of semi-star generation, a semi-automated approach to convert entity relation diagram to semi-star schema is presented in this paper. This approach uses some automation steps of entity relationship diagram to star schema conversion that calculates connection topology value and apply threshold on it and the rest of the steps are its own. Thus the complex process of data warehouse generation is simplified using approach presented here.