Teddy Mantoro

Securing E-mail Communication Using Hybrid Cryptosystem on Android-based Mobile Devices

One of the most popular internet services is electronic mail (e-mail). By using mobile devices with internet connection, e-mail can be widely used by anyone to exchange information anywhere and anytime whether public or confidential. Unfortunately, there are some security issues with email communication; email is sent in over open networks and e-mail is stored on potentially insecure mail servers. Moreover, email has no integrity protection so the body can be undectected altered in transit or on the e-mail server. Email also has no data origin authentication, so people cannot be sure that the emails they receive are from the e-mail address owner. In order to solve this problem, this study proposes a secure method of e-mail communication on Android-based mobile devices using a hybrid cryptosystem which combines symmetric encryption, asymmetric encryption and hash function. The experimental results show that the proposed method succeeded in meeting those aspects of information security including confidentiality, data integrity, authentication, and non-repudiation.

3D Maps in Mobile Devices: Pathway Analysis for Interactive Navigation Aid

Pathway analysis provided by current 3D maps in mobile devices that are intended for an interactive navigation aid, is simulated for what-if experiments against task and functional analysis, based on the problems faced from both technical and user-practices views. The aim of a navigation aid, in general, is to provide an optimal route from the current position to the destination. Unfortunately, the problem of most mobile devices GPS signal accuracy and the display of pathways on 3D maps in the small screen of mobile devices affects the pathway architectural design from generating accurate initial positions to destinations. This paper presents both conceptual and experimental analysis of pathway determination designed for 3D maps in mobile devices for an interactive navigation aid, which is going to be added to an existing individual cognitive map. The analytical outcomes are aimed at providing how environmental conditions come to be detected and how problems are resolved in helping people to navigate in unfamiliar locations by having positions and paths corrected to a reasonable degree of accuracy in order to overcome the problems of generating in-accurate locations and the weaknesses of conventional 2D maps, which requires users to interpret its various symbols and legends. Bent functions and fuzzy logic type 2 are used for simulating signal deviations from the precise values and Voronoi diagram/Delaunay triangulation are used for establishing experimental paths and locations. Finally, this technique will contribute to a well-defined positioning and pathway establishment of 3D maps in mobile devices for navigation aid.

License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm CRLSA was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.

Hajj Crowd Tracking System in a Pervasive Environment

Hajj is a huge congregation of muslims from all over the world which happens annually in Makkah, Kingdom of Saudi Arabia (KSA). It is one of the pillars of Islam and every able Muslim must perform this act at least once in their lifetime. Many challenges are faced by the organizers as well by the pilgrims during this massive religious gathering. The cases of missing Hajj pilgrims are not uncommon and although several tracking and navigation devices have been introduced, there is still a need for a better solution in overcoming the issue. There are several factors that prevent a widespread use of the system, such as the operational costs, availability of the connections and the use in uncommon platform. This paper proposes a framework for tracking Hajj pilgrims in a crowded pervasive environment using a system called HajjLocator. A discussion on the prototype of HajjLocator, as a system to track and monitor pilgrims while performing Hajj and to save alive, SOS mechanism, is also presented in this paper.

Optimal translation of English to Bahasa Indonesia using statistical machine translation system

Finding optimal translation is not an easy task as it requires in-depth knowledge of the language to re-encode the meaning into the target language. This paper explores the translation process of a statistical machine translation system from English to Bahasa Indonesia by considering four weight variables i.e. translation model, language model, distortion (reordering) and for word penalty. This translation approach does not require in-depth knowledge of the linguistic properties of the languages. The well-behaved aligned parallel corpus as the training data for the machine translation is used to increase the BLEU and NIST scores in getting better quality translations. One way to enhance the corpus is by increasing the number of words and/or sentences it contains. In this study, the better evaluation score is achieved when we alter the weights of some translation parameters. Our study shows that both the weights and well-behaved aligned parallel corpus play significant roles in improving the translation quality which presented by higher score of NIST and BLEU. Our results show that this approach has better performance than a popular Rule Based Machine Translation (RBMT) system.

Performance Analysis of Lossless Compression Algorithms on Medical Images

Medical images play importance role in medical science. Through medical images, doctor can do more accurate diagnoses and treatment for patients. However, medical images consume large data size; therefore, data compression is necessary to be applied in medical images. This study presents a comparison of lossless compression techniques: LOCO-I, LZW, and Lossless JPEG algorithms which tested on 5 modalities of medical images. All the algorithms are theoretically and practically a lossless image compression which has MSE equal to 0. Moreover, LZW offers higher compression ratio and faster decompression process but slower compression process than the other two algorithms. Lossless JPEG has the lowest compression ratio and requires more time both to compress and decompress the images. Therefore, in general, in this study, LZW is the best algorithm to implement in compressing medical images.

Energy-Distance Aware Clustering Scheme (E-DACS) for Wireless Sensor Networks

In this paper, popular clustering protocols for Wireless Sensor Networks (WSNs) are investigated. There are many issues and challenges that effect network lifetime. One of these critical issues is high power consumption which reduces network lifetime. The aim of this paper is to propose Energy-Distance Aware Clustering (E-DACS) approach that considers sensor energy level and distance during cluster heads selection to increase the throughput. In this method, cluster head selected among other nodes in the cluster by optimal criteria that calculate the distance to each node, distance to base station, and residual energy. Performance, network efficiency and maximizing network lifetime are measured by simulation, analysis and comparison with other well-known mechanisms.

Residual Energy Effects on Wireless Sensor Networks (REE-WSN)

This study presents the effect of residual energy of nodes in Wireless Sensor Networks (WSNs). There are many issues and challenges that still need to be solved in this area especially for energy contrain. This factor considered during clustering and routing approached to maximize network lifetime. To prolong network lifetime, there is a need for efficient power control mechanisms to reduce power consumption in sensor nodes. At the same time, energy efficient techniques should be employed at all layers of the network. This paper proposed an approach for WSNs energy conservation in order to maximize network lifetime by investigating Residual Energy Effects on Wireless Sensor Networks (REE-WSN). The performance and efficiency of REE-WSA are measured through simulation results and analysis.

VisUN-3D: User navigation with visualized 3D maps for mobile users

Mis-orientation in unfamiliar domain is a common problem for new visitors when they visit a new location. This study proposes a unique solution by visualizing the real world to 3D model similarly (congruent) while the visitor on the move. Our approach provides visualization of 3D maps in virtual 3D workspace environments which assist a user to navigate to a target location to meet with others. This paper presents a mobile based prototype with a 3D model for navigating users in an unfamiliar environment. This prototype can accommodate more than 2 users to navigate in a 3D-walk space in a real time. It shows the users their where-about in the form of visualized 3D maps. The 3D map also shows to the user her/hir location on the scene relative to the location of another user that she/he wants to meet on the same image plane. This method of relative location will help the users to navigate around the location to reach their target easier.

Real-time activity recognition in mobile phones based on its accelerometer data

Context awareness is one of the important keys in a pervasive and ubiquitous environment. Activity recognition by utilizing accelerometer sensor is one of the context aware studies that has attracted many researchers, even up until today. Inspired by these researches, we came out with this presented study, which is a continuation of our previous workswhere we explore the possibility of using accelerometer embedded in smartphones in recognizing basic user activity through client/server architecture. In this paper, we present our work in exploring the influence of training data size on recognition accuracy in building classifier model by studying two algorithms, Naïve Bayes and Instance Based classifier (IBk, k=3). The result shows that 13 out of 18 possible combinations for both algorithms gave 90% training data size as the best accuracy, thus proving the assumption that bigger size of training data gives better classification accuracy compared to small sized training data, in most cases. Based on the outcome from the study, it is then implemented in Actiware, which is an activity aware application prototype that uses built in accelerometer sensor in smartphones to perform real-time/online activity recognition. The recognition process is done by utilizing available phone resources locally, without the involvement of any external server connection. ActiWare manages to exhibit encouraging result by recognizing basic user activities with relatively small confusion when tested.