A Generalized Modular Multilevel Current Source Inverter

This paper proposes a novel topology of multilevel current source inverter which is suitable to apply in low/medium voltage. The proposed topology is capable of producing desirable bidirectional output current levels. Furthermore, it can employ symmetrical DC current sources as well as asymmetrical ones which is a significant advantage. Asymmetrical mode makes it possible to generate a great number of output levels by appropriate selection of DC current source magnitude, needless to make changes in the hardware of the inverter. As a result, various methods are presented to compute the magnitude of needed DC current sources. In comparison to the conventional H-Bridge inverter (CHB), the proposed inverter has lessened the number of required DC current sources, switches as well as related gate driver circuits. The reduced number of required components has leads to cost and volume advantages. In addition, the control layout has become simpler. Reduction of power loss as a result of reduced number of on-state switches is the other merit of the proposed inverter. To evaluate the efficiency of the proposed inverter, its simulation and experimental results are extracted including results of various methods of determining DC current source magnitude

Variability in gene cassette patterns of class 1 and 2 integrons associated with multi drug resistance patterns in Staphylococcus aureus clinical isolates in Tehran-Iran

Background: To investigate antibiotic resistance, the occurrence and distribution of class 1 and 2 integrons in multidrug- resistant Staphylococcus aureus isolates from hospitals in Tehran, Iran. The isolates were examined for susceptibility to antimicrobial agents. The mecA gene, class 1 and 2 integrons were detected by PCR. Integrase positive strains were further analysed for the presence of resistance gene cassettes using specific primers and were sequenced. Results: Among 139S.aureus isolates, 109 (78.4 ) and 112 (80.5 ) strains were considered as multidrug resistant and mecA positive, respectively. Class 1 integrons and internal variable regions were found in 72.6 (101/139) and 97 (98/101) and class 2 integrons and variable regions also in 35.2 (49/139) and 65.3 (32/49) of S.aureus clinical isolates, respectively. Twelve distinct cassette arrays were found, containing genes encoding resistance to β-lactams, aminoglycosides, streptothricin, trimethoprim, chloramphenicol,a putative glucose dehydrogenase precursor and a protein with unknown function. Gene cassette arrays aadB, aadA2 and dhfrA1-sat2-aadA1 were common in S.aureus isolates. We detected a completely new gene cassettes which contained aadB, oxa2, aacA4, orfD-aacA4-catB8, aadB-catB3, orfD-aacA4 and aadB-aadA1-cmlA6 of class 1 and dhfrA1-sat2-aadA1, dhfrA11, dhfrA1-sat2 of class 2 integrons. Conclusions: This is the first study to report carriage of class 1 and 2 integrons and associated gene cassettes among in S.aureus isolates from Iran. © 2015 Mostafa et al

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Photography-based taxonomy is inadequate, unnecessary, and potentially harmful for biological sciences

The question whether taxonomic descriptions naming new animal species without type specimen(s) deposited in collections should be accepted for publication by scientific journals and allowed by the Code has already been discussed in Zootaxa (Dubois & Nemésio 2007; Donegan 2008, 2009; Nemésio 2009a–b; Dubois 2009; Gentile & Snell 2009; Minelli 2009; Cianferoni & Bartolozzi 2016; Amorim et al. 2016). This question was again raised in a letter supported by 35 signatories published in the journal Nature (Pape et al. 2016) on 15 September 2016. On 25 September 2016, the following rebuttal (strictly limited to 300 words as per the editorial rules of Nature) was submitted to Nature, which on 18 October 2016 refused to publish it. As we think this problem is a very important one for zoological taxonomy, this text is published here exactly as submitted to Nature, followed by the list of the 493 taxonomists and collection-based researchers who signed it in the short time span from 20 September to 6 October 2016