On the acoustic levitation stability behaviour of spherical and ellipsoidal particles

We present here an in-depth analysis of particle levitation stability and the role of the radial and axial forces exerted on fixed spherical and ellipsoidal particles levitated in an axisymmetric acoustic levitator, over a wide range of particle sizes and surrounding medium viscosities. We show that the stability behaviour of a levitated particle in an axisymmetric levitator is unequivocally connected to the radial forces: the loss of levitation stability is always due to the change of the radial force sign from positive to negative. It is found that the axial force exerted on a sphere of radius ${R}_{s}$ increases with increasing viscosity for ${R}_{s} / \lambda \lt 0. 0125$ ( $\lambda$ is the acoustic wavelength), with the viscous contribution of this force scaling with the inverse of the sphere radius. The axial force decreases with increasing viscosity for spheres with ${R}_{s} / \lambda \gt 0. 0125$ . The radial force, on the other hand, decreases monotonically with increasing viscosity. The radial and axial forces exerted on an ellipsoidal particle are larger than those exerted on a volume-equivalent sphere, up to the point where the ellipsoid starts to act as an obstacle to the formation of the standing wave in the levitator chambe

Study of the urban evolution of Brasilia with the use of LANDSAT data

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were focused in a whole and dynamic way by the utilization of MSS-LANDSAT images for June 1973, 1978 and 1983. In order to aid data interpretation, a registration algorithm implemented at the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained permitted an evaluation of the urban growth of Brasilia, taking as reference the proposed stated for the construction of the city

Urban land use of the Sao Paulo metropolitan area by automatic analysis of LANDSAT data

The separability of urban land use classes in the metropolitan area of Sao Paulo was studied by means of automatic analysis of MSS/LANDSAT digital data. The data were analyzed using the media K and MAXVER classification algorithms. The land use classes obtained were: CBD/vertical growth area, residential area, mixed area, industrial area, embankment area type 1, embankment area type 2, dense vegetation area and sparse vegetation area. The spectral analysis of representative samples of urban land use classes was done using the "Single Cell" analysis option. The classes CBD/vertical growth area, residential area and embankment area type 2 showed better spectral separability when compared to the other classes

Reprodução induzida e criação de larvas de híbridos interespecíficos e intragenérico da ordem siluriforme (Pisces).

O presente trabalho tem como objetivo analisar a resposta à indução artificial da reprodução em exemplares de cachapinta, híbrido resultante do cruzamento de Pseudoplatystoma corruscans X Pseudoplatystoma reticulatum no cruzamento intergenérico entre fêmea deste produto híbrido e macho de jundiá (Leiarius marmoratus), para verificar a produção de gametas viáveis, quantidade de larvas eclodidas, percentual de sobrevivência e crescimento das larvas e consequentemente a produção de exemplares para estudos genéticos.Organizado por: Sílvio Ricardo Maurano; AQUACIÊNCIA 2012

Effect of ammonia load on efficiency of nitrogen removal in an SBBR with liquid-phase circulation

The removal of biological nitrogen from a synthetic wastewater with different ammonium nitrogen concentrations (50 and 100 mgN-NH4+/L) by a nitrification and denitrification process using a sequencing batch biofilm reactor (SBBR) with liquid-phase circulation was studied. The system with a total working volume of 4.6 L (3.7 L in the reactor and 0.9 L in the reservoir) treated 2.1 L of synthetic wastewater in 12-h cycles. As inoculum two types of biomass were used: an anaerobic/anoxic one from an up-flow anaerobic sludge blanket reactor (UASB) and an aerobic one from a prolonged aeration activated sludge system. The system, maintained at 30 ± 1 ºC, operated in batch mode followed by fed-batch mode and was aerated intermittently. During fed-batch operation the reactor was fed with an external carbon source as electron donor in the denitrifying step and with no aeration. When the reactor was fed with 50 mgN-NH4+/L, efficiencies of removal of ammonium nitrogen and total nitrogen from the effluent were 93.8 and 72.2%, respectively, and nitrite, nitrate and organic nitrogen concentrations were 0.07, 6.4 and 0.5 mg/L, respectively. On the other hand, when the influent ammonium nitrogen concentration was 100 mgN-NH4+/L, residual nitrite and nitrate were 0.17 and 20.4, respectively, and no N-Org was found in the effluent. It should be mentioned that residual nitrate remained unaltered at the different C/N ratios used. Consequently, efficiency of total nitrogen removal was reduced to 66.7%, despite efficiency of ammonium nitrogen removal exceeding 90%. These results show the potential of the proposed system in removing ammonium nitrogen from liquid effluents with a moderate ammonium nitrogen concentration.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Application of LANDSAT data to the study of urban development in Brasilia

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were examined in a whole and dynamic way by the utilization of MSS-LANDSAT images for June (1973, 1978 and 1983). In order to aid data interpretation, a registration algorithm implemented in the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained in this work permitted an evaluation of the urban growth of Brasilia, taking as reference the proposal stated for the construction of the city in the Pilot Plan elaborated by Lucio Costa

Land use in the Paraiba Valley through remotely sensed data

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results

Signal enhancement and efficient DTW-based comparison for wearable gait recognition

The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals