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Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed detection algorithm deal with distributed predicates under the possibly modality. We compared the performance of the proposed distributed predicates detection algorithm with several other detection algorithms. The experimental results reveal the effectiveness of the suggested distributed predicates detection algorithm.

In this paper, we propose a novel end-to-end learnable architecture based on Dense Convolutional Networks (DCN) for the classification of electrocardiogram (ECG) signals. This architecture is based on two main modules: the first is a generative module and the second is a discriminative one. The task of the generative module is to convert the one dimensional ECG signal into an image by means of fully connected, up-sampling, and convolution layers. The discriminative module takes as input the generated image and carries out feature learning and classification. To handle the data imbalance problem characterizing the ECG data, we propose to use the focal loss (FL) that is based on the idea of reshaping the standard cross-entropy loss such that it reduces the loss assigned to well-classified ECG beats. In the experiments, we validate the method using the well-known MIT-BIH arrhythmia database in four different scenarios, using four classes in the first scenario, five in the second and 12 in the third. Finally, supraventricular versus the other three and ventricular versus the other three from the scenario with four classes are used as the fourth scenario. The results obtained show that the method proposed here achieves a significant accuracy improvement over all previous state-of-the-art methods.

Ear print is an imminent biometric modality that has been attracting increasing attention in the biometric community. However, compared with well-established modalities, such as face and fingerprints, a limited number of contributions has been offered on ear imaging. Moreover, only several studies address the aspect of ear characterization (i.e., feature design). In this respect, in this paper, we propose a novel descriptor for ear recognition. The proposed descriptor, namely, dense local phase quantization (DLPQ) is based on the phase responses, which is generated using the well-known LPQ descriptor. Furthermore, local dense histograms are extracted from the horizontal stripes of the phase maps followed by a pooling operation to address viewpoint changes and, finally, concatenated into an ear descriptor. Although the proposed DLPQ descriptor is built on the traditional LPQ, we particularly show that drastic improvements (of over 20%) are attained with respect to this latter descriptor on two benchmark data sets. Furthermore, the proposed descriptor stands out among recent ear descriptors from the literature.

Numerous applications, such as material handling, manufacturing, security, and automated transportation systems, use mobile robots. Autonomous navigation remains one of the primary challenges of the mobile robot industry; many new control algorithms have been recently developed that aim to overcome this challenge. These algorithms are primarily related by their adoption of new strategies for avoiding obstacles and minimizing the travel time to a target along an optimal path. In this paper, we introduce four different navigation systems for an autonomous mobile robot (PowerBot) and compare them. The four systems are based on a fuzzy logic controller (FLC). The FLC of one system is tuned by an inexperienced human (naive), while the three other FLCs are optimized through a genetic algorithm (GA), particle swarm optimization (PSO), and a human expert. We hope the comparison answers the question of which is the best controller. In other words, “who can win?,”the naive, the GA, the PSO, or the expert, in fine tuning the membership functions of the navigation and obstacle avoidance behavior of the mobile robot? To answer this question, we used four different techniques for optimization (the naive FLC, GA, PSO, and FLC-expert) and used many criteria for comparison, whereas other research papers have dealt with two techniques at a time.