Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.     

Favorable Environment for a Nondendritic Morphology in Controlled Diffusion Solidification

The novelty of the controlled diffusion solidification (CDS) process is the mixing of two precursor alloys with different thermal masses to obtain the resultant desired alloy, which is subsequently cast into a near-net-shaped product. The critical event in the CDS process is the ability to generate a favorable environment during the mixing of the two precursor alloys to enable a well-distributed and copious nucleation event of the primary Al phase leading to a nondendritic morphology in the cast part. The turbulence dissipation energy coupled with the undercooling of the precursor alloy with the higher temperature enables the copious nucleation events, which are well distributed in the resultant mixture.     

Interference management for spectral coexistence in a heterogeneous satellite network

With the advent of the fifth generation of mobile radio communication by 2020, there will be many challenges such as increasing service demand with low delay in providing billions of end users called the satellite mobile users. It is expected that terrestrial communication systems will be faced with a dense network having many small cells anywhere and anytime. Therefore, there are some remote regions in the world where terrestrial systems cannot provide any services to end users. Furthermore, because of lack of spectral resources, it is very important that the spectrum is shared between satellite systems and terrestrial equipment by a suitable solution to interference management. In this paper, a heterogeneous satellite network that includes low earth orbit (LEO) satellite constellation and terrestrial equipment is proposed to provide low delay services. In this type of structure, interference management based on transmission power control between LEO satellite systems and mobile users is very important for obtaining high throughput. Moreover, in order to mitigate interference, transmission power control is shown based on noncooperative Stackelberg game under many subgames through pricing‐based algorithm and convex optimization method. Finally, the simulation results show that the performance of this study's system model will be improved through the proposed algorithm.     

Automatic quantification of changes in bone in serial MR images of joints

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.     

Design and Optimization of Full Comparator Based on Quantum‐Dot Cellular Automata

Quantum‐dot cellular automata (QCA) is one of the few alternative computing platforms that has the potential to be a promising technology because of higher speed, smaller size, and lower power consumption in comparison with CMOS technology. This letter proposes an optimized full comparator for implementation in QCA. The proposed design is compared with previous works in terms of complexity, area, and delay. In comparison with the best previous full comparator, our design has 64% and 85% improvement in cell count and area, respectively. Also, it is implemented with only one clock cycle. The obtained results show that our full comparator is more efficient in terms of cell count, complexity, area, and delay compared to the previous designs. Therefore, this structure can be simply used in designing QCA‐based circuits.     

Gaussian orthogonal matrix transform approach for PAPR reduction of optical OFDM signals

Optical orthogonal frequency division multiplexing (O-OFDM) has been widely adopted as a high-speed data transmission technique in visible light communication systems. This technique usually suffers from high peak-to-average-power ratio (PAPR). In this paper, a new PAPR reduction technique is proposed for O-OFDM signals. At the transmitter, a matrix transformation with the Gaussian elements is applied to the time-domain O-OFDM signal and at the receiver, the inverse matrix is used to recover the original signal. We show that the Gaussian orthogonal matrices can reconstruct the original signals without degrading the bit error rate (BER) performance. Gram-Schmidt technique is used to orthogonalize the Gaussian matrices. Computer simulations are conducted for 16-QAM baseband modulated symbols and about 3 dB PAPR reduction gain is achieved by the proposed approach compared with conventional O-OFDM.     

High Gain Modified Dual-Band and Dual-Circularly Polarized Array Antenna

Wireless Personal Communications - A novel design of double-layer dual-band circularly polarized array antennas (DDCPAAs) is presented in this paper. First, a DDCP single antenna is introduced as...     

Biometric authentication based on PCG and ECG signals: present status and future directions

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.