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In this paper, we develop an integrative synchronization and imaging algorithm for bistatic synthetic aperture radar (BSAR) using Global Navigation Satellite Systems as transmitters and a fixed receiver. Due to the lack of a dedicated synchronization link, the usage of navigation signals and the system complexity, traditional synchronization and imaging solutions are not applicable for GNSS-based BSAR. To resolve this problem, this paper focuses on the integrative signal processing algorithm for GNSS-based BSAR. Firstly, to remove the effect of navigation signals, synchronization pre-processing is developed. Then the principle of measuring errors neutralization is theoretically proved by considering the geometric peculiarity of the system. Based on this principle, an integrative synchronization and imaging algorithm is proposed. Finally, the validity of the developed algorithm is confirmed by experimentally obtained data.

In this paper, a hybrid damage detection technique involving a combination of variational mode decomposition (VMD) and frequency domain decomposition (FDD) has been applied to study the effectiveness of damage detection in presence of heavily noise contaminated environment. Damage of small magnitude has been tested under Gaussian pulse noise ranging from 0 to 100% for damage analysis. FDD, a signal processing algorithm, has system identification capability in the presence of noise but requires the output acceleration data from all the sensors installed in the nodes of a structure to identify damage. To reduce the number of sensor data needed to identify damage, wavelet-based algorithms have been used to obtain intrinsic mode functions (IMFs) from single sensor output. These IMFs are then fed to FDD algorithm to obtain the natural frequencies of the structure. For comparison purpose, the algorithms (empirical mode decomposition (EMD) + FDD, and VMD + FDD) have been applied to ASCE benchmark building, which has been set as a common platform, using sensor data of first storey. It was observed that the VMD + FDD gives satisfactory damage identification results for 100% noise contamination whereas EMD + FDD was unable to identify damage accurately for noise above 20%. The robustness of VMD + FDD has been established for a different type of noise, random-valued impulse noise, applied on the benchmark structure for detecting the structural parameters. The hybrid algorithm was also checked for system identification using the sensor data of fourth storey to establish its robustness against the sensitivity of the sensor location.

ElectroCardioGram(ECG) bio-sensors are used to detect and transmit the electrical activity of the heart over a period of time. The ECGs are treated by a signal processing unit inside the bio-sensor to detect and identify heart related medical conditions. Testing and verification of bio-sensor is of paramount importance as faults in these critical systems may lead to loss of life. However, due to the complexity of these systems traditional testing methods might not be adequate to provide high level of assurance about the correctness of their operation. In this paper, we propose a verification framework where we utilize simulation and formal methods to validate the correctness of the ECG bio-sensor. The framework is used to verify ECG bio-sensor at several levels of abstractions, including the signal processing level, where the ECG signal attributes, signal processing algorithm including its filters, detection and analysis algorithms, and triggering the alarm are modeled.

The research outlined in this paper was conducted to allow real-time processing, transmission and presentation of data to swimming coaches and subsequently their swimmers in a training environment, focused on providing information relevant to strokes in free swimming. This was achieved using a wearable wireless sensor and embedded programming techniques, using accelerations involved in the swimming stroke to provide relevant features for coaches. Current methods used do not offer real-time response to coaches, which results in the lack of real-time feedback and significantly increased post-session analysis time. Filtering and signal processing algorithms are described here, which allow real-time data analysis to be embedded within a wireless sensor node. The system significantly reduces the time for processing acquired data and has delivered a novel monitoring device suitable for operation within the harsh environment of the pool.

We are developing position-sensitive transition-edge sensors (PoSTs) for future X-ray astronomy missions such as NASAs Constellation-X. The PoST consists of 1 or more transitions-edge sensors (TESs) thermally connected to a large X-ray absorber, which through heat diffusion, gives rise to position dependence. The development of PoSTs is motivated by the desire to achieve the largest focal-plane coverage with the fewest number of readout channels. In this paper, we investigate optimized signal processing algorithms for single channel PoSTs or ‘Hydras’ (consisting of 4-absorbers connected to 1 TES). Using simulated data, we investigate the impact different parameters such as the thermal conductances and the inductance of the bias circuit have on device performance.

The objective of this paper is to elaborate on a mathematical method for selecting the most informative pairs of stochastically correlated radiometric signals. The classification and measurement algorithms are applied to informative pairs of signals in different measuring systems. A signal processing algorithm is developed which is suitable for various forms of functional dependences and is independent of signal nature. Application of the signal processing algorithm in different measuring systems shows that it is possible to significantly decrease the root mean square deviation of the error in determining the unknown controllable parameter. 3 refs., 6 figs., 1 tab.

Most image and signal processing algorithms are local algorithms, regardless of the specific physical nature of the images and signals. Because of the strong correlation of operations required to determine two neighboring elements, image processing can be substantially accelerated by using fast algorithms. Some considerations regarding the construction of such algorithms on the basis of recursive computation have been examined in this paper. The development of specific algorithms for fast local image processing requires further work, because we still do not have the explicit form of the algorithm for the determination of a general local function.

The paper reports the results of investigations comparing the relative in vitro responses of different signal processing algorithms commonly employed in laser Doppler flowmetry (LDF). A versatile laser Doppler system is described which enabled complex signal processing to be implemented relatively simply using digital analysis. The flexibility of the system allowed a variety of processing algorithms to be studied by simply characterising the algorithm of interest under software control using a personal computer. An in vitro physical model is also presented which was used to maintain reproducible fluid flows. Flows of particles were studied in a physical model using both a near-infra-red (NIR) diode and an He/Ne laser source. The results show that frequency-weighted algorithms are responsive to both particle velocity and concentration, whereas non-weighted algorithms respond to concentration only. The linearity of the velocity response is critically dependent on both the dimensions of the in vitro model and the frequency bandwidth of the signal-processing algorithm.