Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

Disassembly and Mislocalization of AQP4 in Incipient Scar Formation after Experimental Stroke

There is an urgent need to better understand the mechanisms involved in scar formation in the brain. It is well known that astrocytes are critically engaged in this process. Here, we analyze incipient scar formation one week after a discrete ischemic insult to the cerebral cortex. We show that the infarct border zone is characterized by pronounced changes in the organization and subcellular localization of the major astrocytic protein AQP4. Specifically, there is a loss of AQP4 from astrocytic endfoot membranes that anchor astrocytes to pericapillary basal laminae and a disassembly of the supramolecular AQP4 complexes that normally abound in these membranes. This disassembly may be mechanistically coupled to a downregulation of the newly discovered AQP4 isoform AQP4ex. AQP4 has adhesive properties and is assumed to facilitate astrocyte mobility by permitting rapid volume changes at the leading edges of migrating astrocytes. Thus, the present findings provide new insight in the molecular basis of incipient scar formation.     

Simulation of SiGe:C HBTs using neural network and adaptive neuro-fuzzy inference system for RF applications

In this article, the small-signal equivalent circuit model of SiGe:C heterojunction bipolar transistors (HBTs) has directly been extracted from S-parameter data. Moreover, in this article, we present a new modelling approach using ANFIS (adaptive neuro-fuzzy inference system), which in general has a high degree of accuracy, simplicity and novelty (independent approach). Then measured and model-calculated data show an excellent agreement with less than 1.68?×?10^?5% discrepancy in the frequency range of higher than 300 GHz over a wide range of bias points in ANFIS. The results show ANFIS model is better than ANN (artificial neural network) for redeveloping the model and increasing the input parameters.     

Outage performance of downlink communications in cognitive-based two-tier networks: cooperative and non-cooperative femtocells

Femtocell deployment, which is a promising approach to the coverage and capacity improvement of indoor communications, suffers from cross-tier interference. Therefore to make the femtocell technology practical this issue needs to be addressed appropriately. One serious type of cross-tier interference occurs in downlink communication, in which a macrocell user is located far from its macro base station. In this setup, the communication of the adjacent femto access points with their users makes the macrocell user experience a low SINR. This paper considers this scenario and shows how cognitive-enabled femto access points can cope with cross-tier interference. More precisely, we compute the outage probability of macro users in a two-tier network when femto access points use the energy detection-based spectrum sensing technique to find the unoccupied frequency subband. To improve the outage probability of macro users, we also study the effectiveness of cooperation among neighbor femto access points. In all cases, the analytical expressions are validated by computer simulations which confirm the accuracy of the used approximations.

     

The DDoS attacks detection through machine learning and statistical methods in SDN

The Journal of Supercomputing - The distributed denial-of-service (DDoS) attack is a security challenge for the software-defined network (SDN). The different limitations of the existing DDoS...