Dynamic range reduction inspired by photoreceptor physiology

A common task in computer graphics is the mapping of digital high dynamic range images to low dynamic range display devices such as monitors and printers. This task is similar to the adaptation processes which occur in the human visual system. Physiological evidence suggests that adaptation already occurs in the photoreceptors, leading to a straightforward model that can be easily adapted for tone reproduction. The result is a fast and practical algorithm for general use with intuitive user parameters that control intensity, contrast, and level of chromatic adaptation, respectively.     

Graph multiset transformation: a new framework for massively parallel computation inspired by DNA computing

In this paper, graph multiset transformation is introduced and studied as a novel type of parallel graph transformation. The basic idea is that graph transformation rules may be applied to all or at least some members of a multiset of graphs simultaneously providing a computational step with the possibility of massive parallelism in this way. As a consequence, graph problems in the class NP can be solved by a single computation of polynomial length for each input graph.     

Influences on the formation and evolution of Physarum polycephalum inspired emergent transport networks

The single-celled organism Physarum polycephalum efficiently constructs and minimises dynamical nutrient transport networks resembling proximity graphs in the Toussaint hierarchy. We present a particle model which collectively approximates the behaviour of Physarum. We demonstrate spontaneous transport network formation and complex network evolution using the model and show that the model collectively exhibits quasi-physical emergent properties, allowing it to be considered as a virtual computing material. This material is used as an unconventional method to approximate spatially represented geometry problems by representing network nodes as nutrient sources. We demonstrate three different methods for the construction, evolution and minimisation of Physarum-like transport networks which approximate Steiner trees, relative neighbourhood graphs, convex hulls and concave hulls. We extend the model to adapt population size in response to nutrient availability and show how network evolution is dependent on relative node position (specifically inter-node angle), sensor scaling and nutrient concentration. We track network evolution using a real-time method to record transport network topology in response to global differences in nutrient concentration. We show how Steiner nodes are utilised at low nutrient concentrations whereas direct connections to nutrients are favoured when nutrient concentration is high. The results suggest that the foraging and minimising behaviour of Physarum-like transport networks reflect complex interplay between nutrient concentration, nutrient location, maximising foraging area coverage and minimising transport distance. The properties and behaviour of the synthetic virtual plasmodium may be useful in future physical instances of distributed unconventional computing devices, and may also provide clues to the generation of emergent computation behaviour by Physarum.     

Image encryption using DNA complementary rule and chaotic maps

This paper proposes a novel confusion and diffusion method for image encryption. One innovation is to confuse the pixels by transforming the nucleotide into its base pair for random times, the other is to generate the new keys according to the plain image and the common keys, which can make the initial conditions of the chaotic maps change automatically in every encryption process. For any size of the original grayscale image, after being permuted the rows and columns respectively by the arrays generated by piecewise linear chaotic map (PWLCM), each pixel of the original image is encoded into four nucleotides by the deoxyribonucleic acid (DNA) coding, then each nucleotide is transformed into its base pair for random time(s) using the complementary rule, the times is generated by Chebyshev maps. Experiment results and security analysis show that the scheme can not only achieve good encryption result, but also the key space is large enough to resist against common attacks.     

A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision

We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.     

Ocean modelling studies inspired by the Seasat altimeter

Abstract

Some recent ocean modelling studies are reviewed which illustrate the ways in which dynamic ocean circulation models can be used to advise on the optimum deployment of, and help analyse the data from, future satellite altimeters for studies of ocean dynamics. At the time of the launch of Seasat the ocean modelling community were unaware of the promise of altimetry. It is a tribute to the enormous success of the Seasat mission that now, ten years on, the modelling community eagerly awaits the launch of a new generation of altimeters.     

Assessment of the slip-resistance of floors in the laboratory and in the field: Two complementary methods for two applications

There are numerous methods available to measure the slip-resistance of different floor-coverings. The INRS has developed two distinct methods for the evaluation of the slip resistance of a given surface within the framework of its studies on the prevention of slips:

• - One method that can be used to compare new surfaces. It uses a static device developed at the INRS and it is based on the evaluation of a coefficient of dynamic friction between a sample of a new oiled surface and an elastomer. This method is well-adapted to the needs of standardisation work;

• - Another method that can be used to evaluate slippage in the field where the surfaces are often worn and polluted with a specific product. It uses a portable device developed in Sweden and it is based on the continuous evaluation of a coefficient of dynamic friction over a variable distance between the surface to be tested and an elastomer.

These two methods which present well-correlated results are described in this publication, and their distinctly different uses will be underlined.     

HiveSec: security in resource-constrained wireless networks inspired by beehives and bee swarms

Cryptographic algorithms rely on the strengths of all their fundamental components and expect them to be harmonious in accomplishing desired levels of security in applications. In order for a security solution to be sophisticated and to provide high security (measured in terms of the security goals it satisfies), the solution needs to typically involve complex mathematical operations and/or multiple stages of operation. While these might offer increased security, such solutions might not be applicable to all systems. We refer to resource-constrained wireless networks, such as radio frequency identification and wireless body area networks, where the resources available on-chip are often decided by the balance between device costs, requirements of longevity and usability. The constraints, thus, require designing solutions that use simple logical operations and are based on reuse of functions, while introducing sufficient unpredictability to increase security. In this paper, we present a key management and message signature generation scheme called HiveSec, whose design is inspired by the symmetry in beehives and the nature of bee swarms, and which offers security through unpredictability and reduced resource usage. We validate our work through simulation studies and security analysis.     

Image encryption scheme based on Chaos and DNA plane operations

Multimedia Tools and Applications - This paper proposes a novel image encryption algorithm based on chaos and DNA planes operations by utilizing one time pad. The plain image is firstly shuffled by...     

SMART: Speedup Job Completion Time by Scheduling Reduce Tasks

Journal of Computer Science and Technology - Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time...     

Biologically inspired visual computing: the state of the art

Visual information is highly advantageous for the evolutionary success of almost all animals.This information is likewise critical for many computing tasks,and visual computing has achieved tremendous successes in numerous applications over the last 60 years or so.In that time,the development of visual computing has moved forwards with inspiration from biological mechanisms many times.In particular,deep neural networks were inspired by the hierarchical processing mechanisms that exist in the visual cortex of primate brains(including ours),and have achieved huge breakthroughs in many domainspecific visual tasks.In order to better understand biologically inspired visual computing,we will present a survey of the current work,and hope to offer some new avenues for rethinking visual computing and designing novel neural network architectures.     

Lion pride optimizer:An optimization algorithm inspired by lion pride behavior

In this paper,we report a novel optimization algorithm,lion pride optimizer(LPO),which is inspired by lion pride behavior.The framework is mainly based on lion prides’ evolution process and group living theory.In a lion pride,brutal competition of individuals happens among male lions both within and among prides;on the other hand,each member plays an important role in the persistence of a lion pride.Based on this framework,concepts from lion prides behavior,e.g.,the strongest males occupy nearly all mating resources,and if a new cohort of males is able to take over a pride,they will seek to kill young cubs sired by their predecessors,are employed metaphorically to design optimum searching strategies for solving continuous optimization problems.From the studies of the algorithm property,it is found that the LPO algorithm is not sensitive to most parameters,which shows the robustness of the algorithm and the parameters are not problemdependent.Central tendency of the algorithm is not found.It is found that the pride update strategy and brutal competition of individuals are two main factors that contribute to the performance of LPO.According to the test results on 23 famous benchmark functions,the LPO algorithm has better performance than the other seven state-of-the-art algorithms on both unimodal and multimodal benchmark functions;in the test of high-dimensional multimodal problems,LPO outperforms the other five algorithms on all benchmark functions.     

Animal migration optimization: an optimization algorithm inspired by animal migration behavior

In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.     

A mobile robot mapping model inspired from the place cells functionality of hippocampus based on dimension reduction technique

ABSTRACT

In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering of places. The proposed Place cell model has two types of inputs: Grid cells input and input from the lateral entorhinal cortex (LEC). The LEC is modelled based on the dimension reduction technique. Therefore, the data that causes locations different to be inserted into the place cell from this layer. Another contribution is proposing a new unsupervised dimension reduction method based on k-means. The method can find perpendicular independent dimensions. Also, the distance of cluster centres found in these dimensions is maximised. The method was compared with LDA and PCA in standard functions. Although LDA is a supervised method, the result showed that the proposed unsupervised method outperformed. To evaluate the place cells model, sequences of images collected by a mobile robot was used and similar results to real place cells achieved.     

Multiport MIMO antennas with mutual coupling reduction techniques for modern wireless transreceive operations: A review

The present technology fulfills the requirement of high data rate and high channel capacity using multiple input multiple output (MIMO) technology. The MIMO capacity of the system is increased linearly but due to the multiple antennas placed near to each other, problem of mutual coupling exists, which degrades the maximum achievable performance of the system. The problems of multipath propagation can be solved using MIMO system. The isolation improvement methods decrease the mutual coupling among antenna elements, and improve the gain and efficiency of the system. In this paper, decoupling network isolation approach, parasitic element approach, defected ground structure, Neutralization line, isolation improvement based on metamaterials, isolation improvement using PIN diode, varactor diode, and feeding structure have been incorporated, and their merits and demerits have been discussed. The effect of different permittivity material on antenna parameters has also included.     

Collective robotic search using hybrid techniques: Fuzzy logic and swarm intelligence inspired by nature

This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm.     

Integrative connectionist learning systems inspired by nature: current models, future trends and challenges

The so far developed and widely utilized connectionist systems (artificial neural networks) are mainly based on a single brain-like connectionist principle of information processing, where learning and information exchange occur in the connections. This paper extends this paradigm of connectionist systems to a new trend—integrative connectionist learning systems (ICOS) that integrate in their structure and learning algorithms principles from different hierarchical levels of information processing in the brain, including neuronal-, genetic-, quantum. Spiking neural networks (SNN) are used as a basic connectionist learning model which is further extended with other information learning principles to create different ICOS. For example, evolving SNN for multitask learning are presented and illustrated on a case study of person authentification based on multimodal auditory and visual information. Integrative gene-SNN are presented, where gene interactions are included in the functioning of a spiking neuron. They are applied on a case study of computational neurogenetic modeling. Integrative quantum-SNN are introduced with a quantum Hebbian learning, where input features as well as information spikes are represented by quantum bits that result in exponentially faster feature selection and model learning. ICOS can be used to solve more efficiently challenging biological and engineering problems when fast adaptive learning systems are needed to incrementally learn in a large dimensional space. They can also help to better understand complex information processes in the brain especially how information processes at different information levels interact. Open questions, challenges and directions for further research are presented.