Excipient-excipient interaction in the design of sustained-release theophylline tablets: in vitro and in vivo evaluation

Sustained-release (SR) theophylline (TPH) tablets were prepared by applying the moisture-activated dry granulation method. The interaction between the excipients sodium alginate (SAL) and calcium gluconate (CG) was the base for the formation of a cross-linked matrix that may regulate TPH release from the formulated tablets. The prepared granules showed good physical characteristics concerning the flow properties and compressibility, with the angles of repose in the range 29-31, and the compressibility indices ranged between 15% and 25%. The granules had low friability values (3.0%-4.2%), depending on SAL:CG ratios. The corresponding tablets showed good physical properties, with a lower rate of drug release compared with the commercial TPH tablets (Quibron®). The release of TPH from the prepared tablets was not markedly affected by either the concentration of added dry binder (carbopol 934) or the tablet hardness, indicating that the rate-determining step in drug release was the diffusion through the produced calcium alginate matrix. Tablets formulated with equal ratios of CG and SAL that showed good physical properties and slow TPH release were chosen for bioavailability studies in beagle dogs, and results were compared with those for Quibron. The in vivo data showed a comparable plasma concentration profile for both tablet formulations, with prolonged appearance of drug in the plasma in detectable amounts for up to 24 h. The formulated tablets showed 104.65% bioavailability relative to that of the commercial tablets. The rate and extent of absorption of TPH showed no significant differences from that of the commercial tablets. Moreover, no significant differences were found in the pharmacokinetic parameters related to the rate and extent of TPH absorption from the prepared and commercial tablets.     

Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target’s next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20 % measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.     

Error-resilient pattern classification using a combination of spreading and coding gains

An approach that aims to enhance error resilience in pattern classification problems is proposed. The new approach combines the spread spectrum technique, specifically its selectivity and sensitivity, with error-correcting output codes (ECOC) for pattern classification. This approach combines both the coding gain of ECOC and the spreading gain of the spread spectrum technique to improve error resilience. ECOC is a well-established technique for general purpose pattern classification, which reduces the multi-class learning problem to an ensemble of two-class problems and uses special codewords to improve the error resilience of pattern classification. The direct sequence code division multiple access (DS-CDMA) technique is a spread spectrum technique that provides high user selectivity and high signal detection sensitivity, resulting in a reliable connection through a noisy radio communication channel shared by multiple users. Using DS-CDMA to spread the codeword, assigned to each pattern class by the ECOC technique, gives codes with coding properties that enable better correction of classification errors than ECOC alone. Results of performance assessment experiments show that the use of DS-CDMA alongside ECOC boosts error-resilience significantly, by yielding better classification accuracy than ECOC by itself.     

Analytical framework for adaptive compressive sensing for target detection within wireless visual sensor networks

Multimedia Tools and Applications - Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specific geographical region. This paper addresses the...