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This paper presents the status regarding electric vehicles (EVs) and public charging stations managed by Cluj-Napoca City Hall. The types of charging stations and the quantity of the used energy were stated. The behavior of EV owners in charging their vehicles was also studied. The presented work proposes an extension of the EV charging infrastructure to support current and future EV owners. Simulations were carried out in Simulink to create an overview of the existing infrastructure. The simulation results showed the amount of greenhouse gas (CO2 emissions) reduced by using EVs. An estimated number of kilometers traveled with EVs was calculated by tracking the loads in all public charging stations.

The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorithm and analyzed its performance in terms of execution time and quality of the approximation. Using the datasets DUC 2005 and Cornell Newsroom Summarization Dataset, we have shown empirically that the algorithm can provide competitive results for single document summarization and multi-document query-based summarization. On the Cornell Newsroom Summarization Dataset, it ranked second among the unsupervised methods tested. For the more challenging task of multi-document query-based summarization, the method was tested on the DUC 2005 Dataset. Our algorithm surpassed the other reported methods with respect to the ROUGE-SU4 metric, and it was at less than 0.01 from the top performing algorithms with respect to ROUGE-1 and ROUGE-2 metrics.

Telephone banking or brokering, building accesssystems or forensics are some of the areas in which speakerrecognition is continuously developing. Fundamental frequencyrepresents an important speech feature used in theseapplications. In this paper we present a study of the effect ofemotional state of a speaker upon the variation of thefundamental frequency of the speech signal. Human beings arequite frequently overwhelmed by various emotions and most ofthe time one can not really control these emotional states. Forthe purpose of our work we have used the Berlin emotionalspeech database which contains utterances of 10 speakers indifferent emotional situations: happy, angry, fearful, bored andneutral. The mean fundamental frequency and also the standarddeviation for every speaker in all the emotional states werecomputed. The results show a very strong influence of theemotional state upon frequency variation.

In this paper we discuss whether a given finite set ofcomplex numbers can be ordered such that the derived sequenceis minimum-phase. Knowing that every sequence can be extendedto a minimum-phase sequence, we conclude that a setcan always be converted to a minimum-phase sequence using anadditional sample. We characterize several types of sets that canbe ordered in minimum-phase sequences with no additionalsample. These sets do not span the sets of all real or complexsequences of finite length.

BACKGROUND: An estimated 5.4 million Canadian adults have been diagnosed with sleep apnea or are at high risk of experiencing obstructive sleep apnea (OSA). There are no recent Canadian data regarding access to and predictors of referral for diagnostic testing in these populations.

The blind deconvolution of ultrasound sequences in medical ultrasound technique is still a major problem despite the efforts made. This paper presents a blind noninverse deconvolution algorithm to eliminate the blurring effect, using the envelope of the acquired radio-frequency sequences and a priori Laplacian distribution for deconvolved signal. The algorithm is executed in two steps. Firstly, the point spread function is automatically estimated from the measured data. Secondly, the data are reconstructed in a nonblind way using proposed algorithm. The algorithm is a nonlinear blind deconvolution which works as a greedy algorithm. The results on simulated signals and real images are compared with different state of the art methods deconvolution. Our method shows good results for scatters detection, speckle noise suppression, and execution time.