Physics-Based Technique for Protecting Privacy in Surveillance Videos

This paper describes a physics-based technique for protecting the privacy of people in videos as defined by the MediaEval 2013 Visual Privacy task. We propose a physics-based approach which estimates the full spectrum of the surface spectral reflectance from the video. Whereby the wavelength which corresponds to the global minimum of the spectral curve (an intrinsic feature of the material) at a pixel is calculated and converted to RGB values which are used to filter pixels that belong to a moving object. This effectively implements visual privacy protection by replacing foreground pixel colour by another which is related to intrinsic optical properties of the original pixel. Both objective and subjective evaluations are performed using both video analytics algorithm and user studies in order to evaluate the proposed technique

The Effect of Music Therapy on Anxiety and Vital Signs of Patients with Acute Coronary Syndrome: A Study in the Cardiac Care Unit of Vali-Asr Hospital, Eghlid, Iran

Background: Acute coronary syndrome is an emergency situation, characterized by a sudden decrease of blood flow to the heart and chest pain during a heart attack or unstable angina. High levels of anxiety increases mortality risk up to three times. The aim of this study was to determine the effect of music therapy on anxiety level and vital signs of patients with acute coronary syndrome admitted in the coronary care unit of Vali- Asr hospital, in Eghlid city. Methods: This clinical trial was conducted on 70 acute coronary syndrome patients who were eligible for the study during 2011-2012. Anxiety level was measured by the standard Spielberger Questionnaire and vital signs of patients were recorded before and after the intervention. Data were analyzed through SPSS18 and using mean, percentage, standard deviation, independent and paired t- test. Results: Music had no effect on vital signs but significantly reduced anxiety level (P=0.049). Anxiety was significantly higher in females, but showed no significant relationship with age and education. There was no significant relationship between age, sex and education with respiratory rate, heart rate and systolic or diastolic blood pressure. Conclusions: Music as an easy and low cost intervention without any complication can be used to reduce anxiety in patients in Coronary Care Units

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive Compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory

A Frequency Bin Analysis of Distinctive Ranges Between Human and Deepfake Generated Voices

Deepfake technology has advanced rapidly in recent years. The widespread availability of deepfake audio technology has raised concerns about its potential misuse for malicious purposes, and a need for more robust countermeasure systems is becoming ever more important. Here we analyse the differences between human and deepfake audio and introduce a novel audio pre-processing approach. Our analysis aims to show the specific locations in the frequency spectrum where these artefacts and distinctions between human and deepfake audio can be found. Our approach emphasises specific frequency ranges that we show are transferable across synthetic speech datasets. In doing so, we explore the use of a bespoke filter bank derived from our analysis of the WaveFake dataset to exploit commonalities across algorithms. Our filter bank was constructed based on a frequency bin analysis of the WaveFake dataset, we apply this filter bank to adjust gain/attenuation to improve the effective signal-to-noise ratio, doing so we reduce the similarities while accentuating differences. We then take a baseline performing model and experiment with improving the performance using these frequency ranges to show where these artefacts lie and if this knowledge is transferable across mel-spectrum algorithms. We show that there exist exploitable commonalities between deepfake voice generation methods that generate audio in the mel-spectrum and that artefacts are left behind in similar frequency regions. Our approach is evaluated on the ASVSpoof 2019 Logical Access dataset of which the test set contains unseen generative methods to test the efficacy of our filter bank approach and transferability. Our experiments show that there is enhanced classification performance to be gained from utilizing these transferable frequency bands where there are more artefacts and distinctions. Our highest-performing model provided a 14.75% improvement in Equal Error Rate against our baseline model

Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specifc geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network's lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node's duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performanc

Effect of Directional Antenna on the Doppler Spectrum in 3-D Mobile Radio Propagation Environment

In this paper, an analytical model is proposed for a macrocellular communication system with directional antennas at elevated base station (BS) to quantify the effect of directivity of the radiated waves from antennas on the Doppler spectrum in a 3-D radio propagation environment. The impacts of antenna beamwidth and motion of mobile station (MS) are thoroughly investigated on the statistical distribution of the power Doppler spectrum. Closed-form expressions for trivariate probability density functions (pdfs) of propagation path distance, power, and Doppler shift are derived. Furthermore, general expressions for joint and marginal pdfs of elevation angle of arrival, power, and Doppler shift are established. Finally, the obtained theoretical results, along with the observations that illustrate the effect of directivity of the antenna beamwidth and the direction of the MS's motion on the distribution characteristics of the power Doppler spectrum, are presented. It is established that for motion of the MS in all directions, the spread in distribution of the Doppler shift observed is significantly reduced due to the use of a directional antenna at the BS with a narrow beam directed toward the desired user. It is also observed that, for a sharp azimuthal beam of directional antenna, the multipath components corresponding to the scatterers in the elevation plane result in the reduction of Doppler shift with an increase in their vertical distance from the MS

Image Processing: Object Segmentation Using Full-Spectrum Matching of Albedo Derived from Colour Images

An image segmentation method has a training phase, and a segmentation phase. In the training phase a frame of pixel lated data from a camera is processed using information on camera characteristics to render it camera-independent. The camera independent data are processed using a chosen value of illuminant spectral characteristics to derive reflectivity data of the items in the image. Pixels of high reflectivity are established. Then, using data from the high reflectivity pixels, the actual illuminant spectral characteristics are established. The illuminant data are then processed to determine information on the illumination of the scene represented by the frame of pixel lated data to derive reflectivity data of the scene. The segmentation phase comprises operating on a subsequent frame of pixel lated data to render it camera-independent and using the determined illumination information to process the camera independent data to determine reflectivity data of the scene to derive a foreground mask

Image Processing: Object Segmentation Using Full-Spectrum Matching of Albedo Derived from Colour Images

An image segmentation method has a training phase, and a segmentation phase. In the training phase a frame of pixel lated data from a camera is processed using information on camera characteristics to render it camera-independent. The camera independent data are processed using a chosen value of illuminant spectral characteristics to derive reflectivity data of the items in the image. Pixels of high reflectivity are established. Then, using data from the high reflectivity pixels, the actual illuminant spectral characteristics are established. The illuminant data are then processed to determine information on the illumination of the scene represented by the frame of pixel lated data to derive reflectivity data of the scene. The segmentation phase comprises operating on a subsequent frame of pixel lated data to render it camera-independent and using the determined illumination information to process the camera independent data to determine reflectivity data of the scene to derive a foreground mask

Multi-class pattern learning using spread spectrum codes

A new pattern classification approach based on multi-user communication techniques is proposed for improving multi-class learning performance. The spreading gain of code division multiple access is used instead of the coding gain of error-correcting output codes to increase classification accuracy. Results show that spread spectrum codes give better classification accuracy than error-correcting output codes up to 14.25%