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The ability to obtain the accurate location of nodes in wireless sensor networks is crucial for practical applications. The sensed data is meaningless if it is not accompanied by its location. Range-free localization techniques are favored to overcome the hardware limitations of sensor nodes and to avoid the costly range-based techniques. DV-Hop is a range-free localization algorithm that is well-known for its simplicity. However, it suffers from low accuracy and poor stability. In this paper, an enhanced variant of the DV-Hop algorithm is used to estimate the distance between the unknown nodes and anchor nodes, then the position estimation phase is formulated as a minimization problem solved by means of the recently developed squirrel search algorithm (SSA). The SSA is utilized to find the locations of the unknown sensor nodes. Our proposed algorithm is thus called SSIDV-Hop algorithm. The performance of our proposed algorithm is compared to that of existing localization algorithms including the DV-Hop, PSODV-Hop, GADV-Hop, and DEIDV-Hop algorithms. Extensive simulations showed that our proposed algorithm is superior to other existing algorithms as it achieved higher localization accuracy, better stability and faster convergence rate.

Managing large personal image databases requires efficient privacy preserving indexing methods to allow for their outsourcing to possibly curious cloud servers. To construct a secure inverted index in this paper, first, visual words are extracted from stored images based on the Speeded-Up and Robust Features (SURF). Next, Order Preserving Encryption (OPE) is used to encipher the frequencies of occurrence of the extracted visual words. Another scale and rotation invariant feature, which is the local HSV histogram, is included for comparison. From the obtained results, it is apparent that SURF achieves more precise results. Aggregation of both features is considered to further improve the accuracy. The effects of the weighting scheme of the visual words and their number on the performance are investigated. Weighted term frequency inverse document frequency (tf-idf) together with the Jaccard similarity measure yield the best performance. OPE encryption is shown to have minor impact on the retrieval accuracy. To reduce encryption time, a lookup table is constructed. The inverted index reduces the search time significantly compared to a sequential search scheme as apparent from the results. A comparative study with recent related schemes demonstrates the competitiveness of the implemented system in terms of computational efficiency and accuracy.

Two new encryption algorithms for secure video transmission are proposed in this paper. The two algorithms employ different types of chaotic maps to generate the keystream for encrypting the video frames. Both algorithms involve a substitution step and a permutation step to achieve confusion and diffusion requirements. For efficient transmission, the video file is compressed before being encrypted. In the basic implementation of both algorithms, MPEG-2 standard is used for compression. However, the algorithms are shown to be compliant with other compression techniques. In the permutation step, the effect of the block size used in the shuffling process is examined. Smaller blocks result in increasing the processing time, while reducing both the correlation between adjacent pixels and the peak-signal-to noise ratio of an encrypted frame. The use of a Feistel structure is investigated to enhance security and its negative impact on the encryption time is demonstrated. The experimental results of the two proposed schemes confirm that they represent different tradeoffs between security and computational efficiency. Both schemes are sensitive to slight variations in the encryption key as apparent from the obtained differential measures. The conducted comparative study shows the competitiveness of the proposed schemes to existing schemes in literature.

A huge amount of personal multimedia data is generated daily and stored on cloud servers. This data needs to be secure from curious servers, while also being searchable by them. The visual features used in a content-based image retrieval (CBIR) system are encrypted to ensure privacy of the cloud users. This paper aims to investigate several features for use in building a privacy-preserving CBIR system. These features range from simple and fast statistical bit plane distribution features to the more sophisticated local features such as Speeded-Up and Robust Features (SURF). Moreover, both series and parallel aggregation approaches of combining several features are studied to improve retrieval efficiency. Feature vectors are encrypted using distance-preserving techniques. The accuracy of the retrieval system is examined under different combinations of encrypted visual features. The results are insightful offering interesting trade-offs between security, computational efficiency and accuracy.

Cognitive radio networks serve different secondary user applications which have distinct delay requirements. In order to better support the delay-sensitive applications, we consider secondary user traffic to be classified into M prioritized classes based on delay requirements. The priority of a secondary user increases within the same class after each interruption to reduce the cumulative handoff delay. A mixed preemptive/non-preemptive resume priority M/G/1 queueing model is assumed to analyze multiple spectrum handoffs. Closed form expressions are derived for both the extended data delivery time and dropping probability for each secondary user class. The analysis assumes a general target channel sequence, where a secondary user can select either to stay at his current channel or change it after each interruption. This general target channel sequence is optimized using a genetic algorithm in order to minimize the average extended data delivery time of all secondary user classes. Numerical results show that the proposed interruption-based priority mechanism outperforms the existing priority mechanisms in terms of average handoff delay and extended data delivery time in case of changing the target channel while preserves the extended data delivery time in case of always stay. Moreover, the optimized general target channel sequence gives better performance compared to the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard for different network cases.

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

This paper blends the ideas from recent researches into a simple, yet efficient image encryption scheme for colored images. It is based on the finite field cosine transform (FFCT) and symmetric-key cryptography. The FFCT is used to scramble the image yielding an image with a uniform histogram. The FFCT has been chosen as it works with integers modulo p and hence avoids numerical inaccuracies inherent to other transforms. Fractals are used as a source of randomness to generate a one-time-pad keystream to be employed in enciphering step. The fractal images are scanned in zigzag manner to ensure decorrelation of adjacent pixels values in order to guarantee a strong key. The performance of the proposed algorithm is evaluated using standard statistical analysis techniques. Moreover, sensitivity analysis techniques such as resistance to differential attacks measures, mean square error, and one bit change in system key have been investigated. Furthermore, security of the proposed scheme against classical cryptographic attacks has been analyzed. The obtained results show great potential of the proposed scheme and competitiveness with other schemes in literature. Additionally, the algorithm lends itself to parallel processing adding to its computational efficiency.

In this paper, the firefly algorithm with a modified parameterless penalty function is used to determine the optimal settings of Flexible AC Transmission System (FACTS) devices including the Thyristor controlled phase shifting transformer TCPST, Thyristor controlled series capacitor (TCSC) and Static VAR Compensator (SVC). Use of FACTS in a power system reduces the active power losses and improves the voltage profile. The proposed algorithm solves an optimal power flow (OPF) problem for the system without FACTS devices as an initial step to determine the optimal settings for the generators' voltages and active powers and to calculate the initial active power losses. The optimal locations for FACTS devices are heuristically chosen by ranking the lines and the buses of the power system according to the reduction in the power losses. Finally, the optimal settings of the FACTS devices are selected to minimize the active power losses and to enhance the voltage profile. The proposed algorithm is tested on the IEEE 30-bus and the IEEE 57-bus power systems and gives high quality solutions in a relatively small computational time.

In this article, a novel approximate analytical solution is presented for solving the standard viral dynamic model. Basically, the standard model is used to study viral dynamics in patients for a wide range of viruses like HIV, HPV, HBV, and HCV. In this research work, the standard model for hepatitis C virus (HCV) is considered in detail; however, the analysis and results can be applicable for all other viruses. This standard model is used to study viral dynamics in patients treated with direct-acting antiviral agents (DAAs). Power series solution combined with Laplace–Padé resummation method (PSLP) is used to obtain the approximate analytical solutions for the model. To test the capability as well as the validity of the proposed method, results are compared with available viral load data published in the literature and with published simulation results. Good fits are obtained in the comparison for all cases considered. Given that medical specialists and physicians are more interested in solutions that yield direct and simple predictions, it is expected that the proposed approximate analytical solution would be attractive to them and help them to obtain a straightforward and a proper estimation regarding the viral load due to variations in treatment and/or patient’s parameters.

Cognitive radio (CR) is a technology that aims to enhance the use of the underutilized spectrum. Spectrum handoff plays this role by allowing the unlicensed secondary users (SUs) to use the licensed frequency bands of the primary users (PUs). Moreover, it helps the SU to vacate the channel at the presence of the PU and find a suitable channel for the interrupted SU to continue his unfinished transmission. In this paper, an analytical model for the general case of non-identical channels in CR networks is introduced for both fixed and probabilistic sequence approaches for target channel selection. Moreover, a new balancing model is suggested to generate the probabilistic sequence. Both the fixed and probabilistic sequence approaches are optimized using the meta-heuristic methods of particle swarm and genetic algorithm in the seek of finding an optimal approach to minimize the extended data delivery time of the SUs. The results are based on the pre-emptive resume priority M/M/1 queueing network model and are shown for different network cases. Based on the optimization results, a fast hybrid approach is proposed and found to achieve near optimal solution.

This paper presents a new approach for secure hiding of textual data in a colored digital image. Use of images as a cover media in steganography is based on the deficiencies in the human visual system (HVS). The proposed technique employs two independent chaotic sequences for specifying the locations where the message bits are embedded in the cover image using an adapted version of the least significant bit (LSB) method. Message bits are embedded using the 3-3-2 LSB insertion method for the chaotically selected pixels of the cover image. This technique provides sufficient security as the same sequence of numbers cannot be generated without knowing the exact key; that is; the initial conditions of the two chaotic maps used in the index selection process. Moreover, the preliminary results ensure that eavesdroppers will not have any suspicion that there is a message hidden within the sent image since the peak signal to noise ratio (PSNR) is high and the mean-squared-error (MSE) is low. Furthermore, the length of the secret message is another important component of the key, which standard steganography detection methods cannot estimate correctly. Finally, the proposed approach provides better PSNR values and MSE values compared to other existing techniques as apparent from our experimental results.

Background and objective Selective electrical stimulation of target brain locations (stimulation focality) is a difficult problem because it comprises conflicting goals. The stimulating current density field needs to be strong enough to stimulate targeted locations but weak enough not to stimulate near-by non-targeted locations. The objective of this study is to suggest a methodology for improving electrical stimulation focality based on time-division multiplexing principle. Proposed methodology The complex problem of exciting a group of target locations is decomposed into a series of simpler problems in which a single location is targeted. Time-division multiplexing between the solutions of the simpler problems achieves seemingly parallel excitation of the selected target locations with minimal excitation of non-targeted locations. Results A high fidelity finite element-based simulation of a cortical vision prosthesis is used to demonstrate the proposed idea and highlight important facts about neurons dynamics that must be taken into consideration in order to design a successful time-division multiplexing based stimulation scheme. Conclusion The study offers a clear detailed procedure for designing focal electrical stimulation setups based on time division multiplexing principle. The included results and experiments prove that the proposed strategy is a step forward towards more focal stimulation setups.

This paper investigates several alternative schemes for hiding a grayscale image in a colored cover image in the spatial domain. First, we consider the use of one-dimensional (Tent map) versus two-dimensional (Baker's map) chaotic maps for the selection of the set of pixels where the secret message bits are to be embedded. The red color channel of the cover image for the formerly selected pixels is affected by the embedding process since the eye is not very sensitive for slight variations in this color channel. Next, two LSB embedding schemes are examined; namely, embedding one bit per pixel and embedding two bits per pixel. The preliminary results ensure that, to eavesdroppers, there will be almost no suspicion regarding the existence of a secret message hidden within the sent image since the peak-signal-to-noise-ratio (PSNR) is high and the mean-squared-error (MSE) is low for the different alternatives investigated. However, there is a clear impact on the time required for the embedding process if two-dimensional maps and/or one bit per pixel LSB technique are used.

This paper presents an efficient load flow algorithm, specifically tailored for Radial distribution networks, which employ special recursive techniques for load flow owing to their radial topology and high (R/X) ratios of its transmission lines. The proposed methodology models the load flow problem into a convex optimization problem, which is solved using primal-dual interior-point algorithm in polynomial time. Moreover, the time draining LU decomposition techniques and forward/backward substitution algorithms of the Jacobian matrix or admittance matrix essential for the traditional load flow methods are no longer needed. A comparison between the proposed algorithm and another validated algorithm in literature has been provided to verify the computational precision of the adopted algorithm. Moreover, the proposed algorithm has shown clear competitiveness in terms of computational efficiency.

This paper implements a mixed-integer quadratically constrained convex optimization program on distribution networks that have a radial structure topology for denigration of system's real power loss. The adopted mathematical model has been solved using Cplex optimization software. The global optimality of the obtained solution is guaranteed via convex relaxation of the continuous decision variables. This overcomes a major disadvantage of using artificial intelligence techniques, specifically meta-heuristic ones, that give no assurance on the solution quality or the optimality gap. Moreover, exact representation of the network losses through the assessment of the objective's function numerical-value is obtained. This overcomes the drawbacks of using linear approximations to power flow equations. The proposed algorithm has been evaluated on two IEEE standard systems and a realistic-sized network with a comparative study with other existing algorithms in the literature.