This paper is intended to investigate intensely the impact of multipossible hand-hold positions on the electromagnetic (EM) interaction of handset antennas and a human by using a finite-difference time-domain (FDTD) method. Candy-bar handsets with different external and internal antenna positions operating in the GSM900, GSM1800/DCS, and UMTS/IMT-2000 bands are hereby simulated with configuration of the most parts in order to achieve the commercially available handset model design. Homogeneous and heterogeneous phantoms both are used to simulate the human head, whereas, a semirealistic model with three different tissues is designed to simulate a human hand holding a set. Both of the antenna performance including the total isotropic sensitivity (TIS) and the specific absorption rate (SAR) in tissues are examined for the different suggested applicable cases, where various positions of antenna, handset and hand are considered in simulations. This simulation study determines that both of the antenna performance and the SAR in tissues significantly alter owing to the positioning of the handset against user’s head at different hand levels; where a maximum alteration is observed due to the exposure of handset with internal antenna, as compared with the handset having external antenna.

This paper presents a through investigation into the effect of the external antenna and printed circuit board (PCB) positions in cellular hand-sets on the coupling between the handset and human-head at GSM-900 frequency standard. The antenna performance and the specific absorption rate (SAR) induced in a human head were computed using a Finite Difference Time-Domain based platform (SEMCAD). A numerical correct model of a European female head was adopted and a semi-realistic hand model was designed during the simulation. The results showed that the optimum position for the antenna and PCB in hand-set close to head is the far right-corner for the right-hand users and the far left-corner for the left-hand users, where a minimum SAR in head is achieved.

This paper investigates the impact of the hand-hold positions on both antenna performance and the specific absorption rate (SAR) induced in the user’s head. A cellular handset with external antenna operating at GSM-900 frequency is modeled and simulated using a finite difference time-domain (FDTD)-based platform SEMCAD-X. A specific anthropomorphic mannequin (SAM) is adopted to simulate the user’s head, whereas a semirealistic CAD-model of three-tissues is designed to simulate the user’s hand. The results show that in case of the handset in hand close to head at different positions; the antenna total efficiency gets reduced to (14.5% - 5.9%) at cheek-position and to (27.5% to 11.8%) at tilt-position. The peak averaged SAR1g values in head close to handset without hand, are 4.67 W/Kg and 2.66 W/Kg at cheek and tilt-position, respectively. Due to the presence of hand, the SAR1g in head gets reduced to (3.67-3.31 W/Kg) at cheek-position and to (1.84-1.64 W/Kg) at tilt-position, depending on the hand-hold position.

This paper presents empirical methods for enhancing the accuracy of inductive learning systems. It

addresses the problems of: learning propositional production rules in multi-class classification tasks in

noisy domains, maintaining continuous learning when confronted with new situations after the learning

phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation processes, allows accurate

classifications to be made on real-world data sets. The paper presents the system ARIS which

implements this approach, and it is shown that the resulting classifications are often more accurate than

those made by the non-refined knowledge bases.

The core design decision that lies behind ARIS is that it employs an ordering of the rules according to

their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights for the rule’s

conditions and to combine them. This model focuses the analysis of the knowledge base and assists the

refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments that

appear to be most consistent with the refinement data set. The design framework of ARIS consists of a

tabular model for expressing rule weights, and the relationship between refinement cases and the rules

satisfied for each case to focus the refinement process. The system has been used to refine knowledge

bases created by ARIS itself, as well as to refine knowledge bases created by the RIPPER and C4.5

systems [6,25] in ten selected domains. Two main advantages have been observed. First, the ability to

gradually improve the knowledge base as the refinement proceeds. Second, is the ability to learn strong

rules utilising condition’s weight using minimum covering algorithm. Thus, by taking account of this

information we improve the applicability and quality of refinement

Configurable Computer Design for Guidance and Control Algorithms

In this paper mappings of several different algorithms from the fields of operating systems and multimedia are discussed, together with the underlying system architecture. The system architecture is based on a board with four FPGA circuits. For the purpose of this research, a simulator of the system has been developed, and different interconnection networks, memory, and bus architectures have been tested. The results of the simulation, as well as analytical results, are presented.