### Evolutionary pressures in emerging societies of secondary users in cognitive radio networks

- Research Article in 10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)
- Authors
- Anna Wisniewska, Bilal Khan, Ala Al-Fuqaha, Kirk Dombrowski, Mohammad Abu Shattal
- Abstract
Wireless communication is an increasingly ubiquitous and important aspect of the digital ecosystem. In the face of rapid growth in the population of Internet of Things reached 4+ billion devices in 2014, and is expected to continue to grow, reaching 25 billion by 2020, the limited capacity of radio…

more »Wireless communication is an increasingly ubiquitous and important aspect of the digital ecosystem. In the face of rapid growth in the population of Internet of Things reached 4+ billion devices in 2014, and is expected to continue to grow, reaching 25 billion by 2020, the limited capacity of radio spectrum is likely to reach saturation. In this paper, we show that evolutionary pressures in CR societies necessarily drive the emergence of more advanced sensing capabilities, and correspondingly more sophisticated models of resource sharing. We put forth four evolutionary stages for CR societies, based on well-established biological analogues, and demonstrate that at each stage of CR evolution, a subpopulation that is able to engage more advanced sensing capabilities and couse strategies is able to better extract greater utility from spectrum resources. In this manner, we see that each stage of CR evolution prepares the way for the next: the present societies of nonforagers facilitate the emergence of foragers; foragers give way to contention-sensing rational CR societies; these, in turn, will likely facilitate the emergence of sociality. Each evolutionary stage is enabled by advances in sensory capabilities, and gives rise to new sophisticated resource sharing schemes that yield more efficient utilization of radio spectrum for secondary users, regardless of primary user activity.

### A cellular model of swarm intelligence in bees and robots

- Research Article in 10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)
- Authors
- Martina Szopek, Martin Stefanec, Michael Bodi, Gerald Radspieler, Thomas Schmickl
- Abstract
We present here a simple cellular model of random motion and social interaction of young honeybees making swarm intelligent decisions in complex dynamic temperature fields. We model also behaviors of stationary robots that affect those bees. Our study looks for a first as-simple-as-possible approac…

more »We present here a simple cellular model of random motion and social interaction of young honeybees making swarm intelligent decisions in complex dynamic temperature fields. We model also behaviors of stationary robots that affect those bees. Our study looks for a first as-simple-as-possible approach towards modeling such a bio-hybrid system. Our model predicts observed collective behaviors qualitatively very well by modeling a correlated random walk and a simple social interaction mechanism. We found that even a very simple 2-dimensional cellular model with a limited state space of 16 bit per cell suffices. Ultimately, the simplicity of the model allows fast and distributed computation. This will allow us to search for interesting swarm intelligent robotic algorithms for creating novel bio-hybrid systems composed by real animals and autonomous rule-driven cellular robots by using stochastic optimization techniques.

### Hyperdimensional Computing for Noninvasive Brain–Computer Interfaces: Blind and One-Shot Classification of EEG Error-Related Potentials

- Research Article in 10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)
- Authors
- Abbas Rahimi, Pentti Kanerva, José del R. Millán, Jan M. Rabaey
- Abstract
Computing with high-dimensional (HD) vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as…

more »Computing with high-dimensional (HD) vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as brain--computer interfaces. We describe the use of HD computing to classify electroencephalography (EEG) error-related potentials for noninvasive brain--computer interfaces. Our algorithm encodes neural activity recorded from 64 EEG electrodes to a single temporal--spatial hypervector. This hypervector represents the event of interest and is used for recognition of the subject's intentions. Using the full set of training trials, HD computing achieves on average 5% higher accuracy compared to a conventional machine learning method on this task (74.5% vs. 69.5%) and offers further advantages: (1) Our algorithm learns fast by using 34% of training trials while surpassing the conventional method with an average accuracy of 70.5%. (2) Conventional method requires prior domain expert knowledge to carefully select a subset of electrodes for a subsequent preprocessor and classifier, whereas our algorithm blindly uses all 64 electrodes, tolerates noises in data, and the resulting hypervector is intrinsically clustered into HD space; in addition, most preprocessing of the electrode signal can be eliminated while maintaining an average accuracy of 71.7%.

### Fuzzy Logic Training for Predicting Age of Rats

- Authors
- Douglas Eric Dow, Isao Hayashi
- Abstract
Physiological measurements may contain data with nonlinear relations, non-normal distribution and large signal-to-noise ratio. Fuzzy logic has been utilized to analyze and classify physiological data. Age in mammals is reflected in physiological properties, such as skeletal muscle function. A fuzzy…

more »Physiological measurements may contain data with nonlinear relations, non-normal distribution and large signal-to-noise ratio. Fuzzy logic has been utilized to analyze and classify physiological data. Age in mammals is reflected in physiological properties, such as skeletal muscle function. A fuzzy logic algorithm with self-tuning mechanism was developed in this study to make a model from physiological measurements, and predict age. The system was developed using scalar number sets having linear and nonlinear relations. Then the system was applied toward data of body mass and skeletal muscle function to predict the age of rats as a scalar value. The algorithm was developed using the python programming language. The results of the developed fuzzy logic system were compared with other machine learning algorithms using the Weka platform. The developed fuzzy logic model had a lower mean for relative absolute error (RAE) for the tested set of linear and nonlinear relations compared to the results of the tested machine learning algorithms in Weka. For prediction of rat age, the RAE of the fuzzy logic system was 22% compared with values of 23-33% for the other tested algorithms. Further testing and development of the fuzzy logic system on physiological data relations will be necessary to verify these promising results.

### Efficient Feature Vector Clustering for Automatic Speech Recognition Systems

- Authors
- Lilia Lazli, Mounir Boukadoum, Otmane Ait Mohamed, Mohamed-Tayeb Laskri
- Abstract
In this paper, we present an efficient algorithm for the clustering of speech data. The algorithm based on regulating a similarity measure to set the number of clusters and the cluster boundaries, thus overcoming the shortcomings of conventional clustering algorithms such as k-Means and Fuzzy C-Mea…

more »In this paper, we present an efficient algorithm for the clustering of speech data. The algorithm based on regulating a similarity measure to set the number of clusters and the cluster boundaries, thus overcoming the shortcomings of conventional clustering algorithms such as k-Means and Fuzzy C-Means, which require a priori knowledge of the number of clusters, the use of similarity measure that follows the data distribution, and are sensitive to the choice of initial configuration, The algorithm performance was tested in an HMM/MLP automatic speech recognition system, with the results were compared with those obtained when using a combination of Fuzzy C-Means and genetic algorithms to do the clustering, showing better performance.

### Affinity Based Search Amount Control in Decomposition Based Evolutionary Multi-Objective Optimization

- Authors
- Hiroyuki Sato, Minami Miyakawa, Keiki Takadama
- Abstract
This work proposes a search amount control method on each search part of the Pareto front in decomposition based evolutionary multi-objective optimization. The conventional MOEA/DC decomposes the Pareto front with a set of weight vectors and pairs one solution with each weight vector to approximate…

more »This work proposes a search amount control method on each search part of the Pareto front in decomposition based evolutionary multi-objective optimization. The conventional MOEA/DC decomposes the Pareto front with a set of weight vectors and pairs one solution with each weight vector to approximate the entire Pareto front with the set of solutions. Well-matched pairs of weight vector and solution contribute to uniformly approximating the Pareto front, and mismatched pairs having a long distance between weight vector and solution in the objective space deteriorate the approximation quality and the search. To eliminate mismatched pairs and improve the search performance, this work proposes affinity based search amount control method for MOEA/DC. Experimental results using continuous WFG4 test problems with 2-5 objectives show that the proposed method improves the well-matched pair ratio in all pairs of weight vector and solution and the search performance.

### Polynomial Mean-Centric Crossover for Directed Mating in Evolutionary Constrained Multi-Objective Continuous Optimization

- Authors
- Minami Miyakawa, Hiroyuki Sato, Yuji Sato
- Abstract
This paper proposes a mean-centric crossover to improve the effectiveness of the directed mating utilizing useful infeasible solutions in evolutionary constrained multi-objective continuous optimization. The directed mating selects a feasible solution as the first parent and a solution dominating t…

more »This paper proposes a mean-centric crossover to improve the effectiveness of the directed mating utilizing useful infeasible solutions in evolutionary constrained multi-objective continuous optimization. The directed mating selects a feasible solution as the first parent and a solution dominating the first parent in the objective space from the population involving infeasible solutions as the second parent. Since infeasible solutions having better objective values than feasible ones have useful variables, it helps to improve the search performance. So far, the commonly used simulated binary crossover (SBX) have been employed to generate offspring from two parents selected by the directed mating. However, it is not clear that the commonly used SBX is appropriate also for parents selected by the directed mating. When the Pareto front exists on the boundary between the feasible and the infeasible regions in the variable space, a mean-centric crossover generating offspring around intermediate area of two parents would be more effective than SBX which is a parent-centric crossover generating offspring around two parents. This work proposes the polynomial mean-centric crossover (PMCX) and combines it with the directed mating. The experimental results show that the proposed PMCX achieves higher search performance than SBX on several test problems.

### On Arithmetic Functions in Actin Filament Networks

- Authors
- Andrew Schumann
- Abstract
This paper is devoted to actin filament networks as a computation medium. The point is that actin filaments are sensitive to outer cellular stimuli (attractants as well as repellents) and they appear and disappear at different places of the cell to change the cell structure, e.g. its shape. Due to …

more »This paper is devoted to actin filament networks as a computation medium. The point is that actin filaments are sensitive to outer cellular stimuli (attractants as well as repellents) and they appear and disappear at different places of the cell to change the cell structure, e.g. its shape. Due to assembling and disassembling actin filaments, Amoeba proteus can move in responses to different stimuli. As a result, Amoeba proteus can be considered a simple reversible logic gate, where outer cellular signals are its inputs and the amoeba motions are its outputs. In this way, we can implement the FREDKIN logic gate on the amoeba behaviours. The actin filament networks have the same basic properties as neural networks: lateral inhibition; lateral activation; recurrent inhibition; recurrent excitation; feedforward inhibition; feedforward excitation; convergence/divergence. These networks can embody arithmetic functions defined recursively and corecursively within p-adic valued logic. Furthermore, within these networks we can define the so-called diagonalization for deducing undecidable arithmetic functions.

### Quantitative Assessment of Ambiguities in Plasmodium Propagation in Terms of Complex Networks and Rough Sets

- Authors
- Krzysztof Pancerz
- Abstract
A Physarum machine is a biological computing device implemented in the plasmodium of Physarum polycephalum, a one-cell organism able to build large and manifold networks of protoplasmic veins for solving different computational tasks. In the paper, we propose to use complex networks as an underlyin…

more »A Physarum machine is a biological computing device implemented in the plasmodium of Physarum polycephalum, a one-cell organism able to build large and manifold networks of protoplasmic veins for solving different computational tasks. In the paper, we propose to use complex networks as an underlying model of plasmodium propagation in Physarum machines. For such models, we define a measure, derived from rough set theory, for quantitative assessment of the cohesion of plasmodium connections between distinguished regions of interest. Rough sets are an appropriate tool to deal with some ambiguities which appear in plasmodium propagation.

### Talmudic Foundations of Mathematics

- Authors
- Andrew Schumann, Alexander V. Kuznetsov
- Abstract
In this paper, we assume that the mathematicians in proving new signiﬁcant theorem, such as Fermat’s Last Theorem, deal with combining proof trees on tree forests by using the analogy as an inference metarule. In other words, the real mathematical proofs cannot be formalized as discrete sequences, …

more »In this paper, we assume that the mathematicians in proving new signiﬁcant theorem, such as Fermat’s Last Theorem, deal with combining proof trees on tree forests by using the analogy as an inference metarule. In other words, the real mathematical proofs cannot be formalized as discrete sequences, but they are concurrent and can by formalized as analog processes within a space with some topological properties. For the ﬁrst time, inference metarules in a topological space were proposed in the Talmud within a general Judaic approach to concurrent or even massive-parallel conclusions. The mathematician does not think sequentially like a logical automaton, but concurrently, also. Hence, we suppose that the proof technique of real mathematics cannot be formalized by discrete methods. It is just a hypothesis of the foundations of mathematics that we can use discrete tools so that mathematics can be reduced to logic. We show in the paper how the mathematical proof can be formalized just by analog computations, not discrete ones.