New Publications are available for Digital signal processing
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New Publications are available now online for this publication.
Please follow the links to view the publication.Decentralised road-map assisted ground target tracking using a team of UAVS
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0407
This paper presents a ground moving target tracking filter and guidance using a team of UAVs. To improve the estimation accuracy, approximated road-map information using constant curvature segments is utilised with a constrained filtering. Furthermore, the decentralised extended information filter and the Kalman consensus algorithm are applied to a standoff orbit tracking problem along with coordinated vector field guidance, and their performances are analysed depending on the communication noises and network structures using a realistic car trajectory data. (6 pages)Array shape calibration using a single multi-carrier pilot
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0173
In this paper, a novel single pilot array shape calibration algorithm is proposed for an arbitrary planar array. The method requires a single multi-carrier pilot operating at a known location with respect to the arbitrary array reference point. Typically two or more sources are required to calibrate the shape of a planar array. However, by exploiting the difference in the array response model when the source operates in the "near-far" and "far" field of the array, it is shown how this can be reduced to just one. Simulation results exhibiting the performance of the proposed method are also presented. (6 pages)Multispeaker direction of arrival tracking for multimodal source separation of moving sources
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0143
An improvement is proposed in the audio-visual approach to solve the problem of source separation of physically moving speakers by exploiting multiple video cameras, a circular microphone array and robust spatial beamforming. The challenge of separating moving sources is that the mixing filters are time varying; as such the unmixing filters should also be time varying but these are difficult to determine from only audio measurements. Therefore the visual modality is utilized to track the direction of each speaker to the microphone array by using a Markov chain Monte Carlo particle filter (MCMC-PF). The proposed direction of arrival (DOA) tracker improves the computational complexity with respect to a previously employed 3-D multi-speaker position tracker. The DOA information is used in a robust least squares frequency invariant data independent (RLSFIDI) beamformer to separate the audio sources. Experimental results show that the proposed technique efficiently tracks the DOA with improved computational complexity and enhanced source separation. (5 pages)Robust PSD features for ion-channel signals
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0154
Ion-channel sensors which mimic naturally occurring pore-forming proteins can be used to detect small metal ions and organic molecules. A chamber with a lipid bilayer hosting ion-channels produced by protein insertion constitutes such a sensor. Each analyte produces a characteristic signal pattern during its migration from one section of the chamber to another through the ion-channels. A four chamber ion-channel sensor array is built for accurate analyte detection. The power distribution information in the transform domain has been successfully used as discriminatory features for each chamber signal. However, these features are not robust to noise and hence result in a reduced classification performance. In this paper, we pose the stabilization of PSD features extracted from noisy segments as a matrix completion problem. Matrix completion with a low rank assumption provides the stabilized features. We demonstrate using a synthetic experiment that the proposed setup achieves improved classification performance in comparison to using the features directly. Furthermore, performing analyte detection in real ion-channel data, using the proposed robust features, provides reduction in false alarm rates. (5 pages)Towards assisted living via probabilistic vital-sign monitoring in the home
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0022
This paper describes the development of a reliable multi-sensor data fusion system for monitoring patient vital-signs in the home. Initial investigatory work has taken place using ambulatory hospital patients, in the Oxford Cancer Hospital. Our monitoring approach is based on a probabilistic model of normality learned from a data-set of vital signs acquired from a representative group of high-risk patients. Alerts are provided to carers whenever patient vital signs are deemed "abnormal" with respect to the model of normality. We show examples of how this approach correctly detects physiological deterioration in the target patient group, and describe future work in further validation of the technology in home monitoring applications. (6 pages)A review of the state of the art in artifact removal technologies as used in an assisted living domain
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0033
There has been significant growth in the area of ubiquitous, pervasive, distributed healthcare technologies due to the increasing burden on the healthcare system and the impending demographic shift towards an aging population. The move from a hospital-centric healthcare system towards in-home health assessment is aimed to alleviate the burden on healthcare professionals, the health care system and caregivers. Advances in signal acquisition, data storage and communication channels provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and reduce the utility of the affected part of the signal. The degrading effect of the artifacts significantly increases when data collection is moved from the clinic into the home. Advances in signal processing have brought about significant improvement in artifact removal over the last number of years. This paper reviews the most common physiological and location-indicative signals recorded in the home and documents the artifacts which occur most often. A discussion of some of the most common artifact removal techniques is then provided. An evaluation of the advantages and disadvantages of each is given with reference to the assisted living environment. (6 pages)A recurrent quantum neural network model enhances the EEG signal for an improved brain-computer interface
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0028
The brain-computer interface (BCI) technology is a means of communication that allows individuals with severe movement disability to communicate with external assistive devices using the electroencephalogram (EEG) or other brain signals. The human mind and mental processes are inherently quantum in nature. It is therefore logical to investigate the possibility of designing new approaches to Brain-computer interface (BCI) with the amalgamation of quantum and classical approaches. This paper presents an intelligent information processing paradigm to enhance the raw electroencephalogram (EEG) data. A Recurrent Quantum Neural Network (RQNN) model using a non linear Schrodinger Wave Equation (SWE) is proposed here to filter the Motor Imagery (MI) based EEG signal of the BCI user. It is shown that if the potential field of the SWE is excited by the raw EEG data using a self-organized learning scheme, then the probability density function (pdf) associated with the EEG signal is transferred to the probability amplitude function which is the response of the SWE. In this scheme, the EEG data is encoded in terms of a particle like wave packet which helps to recover the EEG signal by denoising the raw data. Thus the filtered EEG signal is a wave packet which glides along and moves like a particle. This denoised EEG signal is then fed as an input to the feature extractor to obtain the Hjorth features. These features are then used to train a Linear Discriminant Analysis (LDA) classifier. It is shown that the accuracy of the classifier output over the training and the evaluation datasets using the filtered EEG is enhanced compared to that using the raw EEG signal for six of the nine subjects with a fixed set of parameters for all the subjects. (6 pages)DE-noising of EEG signals using Bayes shrink based on Coiflet transform
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0048
The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. However, the presence of artifacts like Electro-oculogram (EOG), Electrocardiogram (ECG), Electromyogram (EMG) and power-line noise in the EEG signal is a major problem in the study of brain potentials. Hence, these superfluous signals are needed to be removed. There are various methods for removal of artifacts. This paper discusses a wavelet-based approach for correcting the artifacts generated by eye blinks, eyeball movements and facial muscle movements in EEG using threshold called Bayes Shrink based on Coiflet Transform.Brain Music System: standardized brain music therapy
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0029
The paper discusses a standardized therapeutic treatment using the Brain Music System, a system that uses Sonified Neurofeedback accurately and cost effectively to convert brainwaves into musical sound using Digital Signal Processing algorithms. A standard course of sonified neurofeedback therapy (for example 15 sessions), tailored specifically to individual patients, is a realistic possibility due to the inexpensive and portable nature of the system, and could be used both inside or even outside of a traditional clinical setting for subjects suffering from a wide array of mental and neurological conditions. In a pilot study to test the algorithms and output of the Brain Music System, the distribution of the Alpha, Beta and Theta waves in normal subjects corresponds closely to that in published studies using standard high-end equipment (confined to expensive clinical setups). These results allows the Brain Music System to align its protocol to practice standards, and to better associate standard algorithmic tasks to each of the three mentioned brainwave types. (4 pages)Design and implementation of efficient multiplier using Vedic mathematics
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0071
Multiplication is an important fundamental function in arithmetic operations. Multiplication-based operations such as Multiply and Accumulate(MAC) and inner product are among some of the frequently used computation Intensive Arithmetic Functions(CIAF) currently implemented in many Digital Signal Processing (DSP) applications such as convolution, Fast Fourier Transform(FFT), filtering and in microprocessors m its arithmetic and logic unit. Since multiplication dominates the execution tune of most DSP algorithms, so there is a need of high speed multiplier. Currently, multiplication time is still the dominant factor in determining the instruction cycle time of a DSP chip. The demand for high speed processing has been increasing as a result of expanding computer and signal processing applications. Higher throughput arithmetic operations are important to achieve the desired performance in many real-time signal and image processing applications . One of the key arithmetic operations in such applications is multiplication and the development of fast multiplier circuit has been a subject of interest over decades. Multiplier based on Vedic Mathematics is one of the fast and low power multiplier. Employing this technique in the computation algorithms will reduce the complexity, execution time, power etc. This vedic based multiplier is compared with binary multiplier(partial products method).Audio fingerprinting based on local energy centroid
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0907
Audio fingerprint is an effective representation of an audio signal using low-level features and can be used to identify unlabeled audio based on its content. In this paper, we introduce a robust audio feature, local energy centroid (LEC), which can represent the energy conglomeration degree of the relative small region in the spectrum. Our audio fingerprint is generated based on the LEC feature which is conducive to enhance the robustness of system. In audio retrieval processing, an improved scoring strategy is proposed to resist the linear speed change. Experimental results show that the new fingerprinting system is quite robust in the present of noise and the proposed method can achieve satisfying recognition accuracy.Sensing, processing and application of EMG signals for HAL (Hybrid Assistive Limb)
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0463
Hybrid Assistive Limb (HAL) is an assistive technology device for supporting physically disabled persons by understanding the percentage of their disability. This work aims to design and develop a HAL based on Electromyogram (EMG) signals. The EMG signal is a biomedical signal that measures electrical currents generated by muscles. These signals can be used for clinical/biomedical applications if advanced methods for detection, decomposition, processing, and classification are available. The pattern of the EMG signal produced may differ depending on the activity of the muscle movement. Four types of biceps muscle activities are identified using the signal pattern generated from raw surface EMG data. Threshold detection method and pattern recognition method were carried out and it is found that pattern recognition method is more generalized method for classification as threshold method is user dependent. The overall classification rate of about (80-83) % obtained using LDA and a classification rate of more than 90% obtained using ANN. Control commands for a stepper motor used for driving artificial limb are developed from the classified EMG signal and stepper motor control is achieved through computer parallel port.Comparison of various mutation schemes of differential evolution algorithm for the design of low pass FIR filter
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0477
Differential Evolution (DE) has established itself as a very useful tool in the domain of optimization technique in recent times. It has found its paramount importance in various signal processing applications. Amongst them, design of Finite duration Impulse Response (FIR) filter using DE algorithm has drawn special interest in recent times. In this paper, we have analyzed the impact of various mutation strategies of DE algorithm for the design of linear-phase low pass FIR filter. The performance of this evolutionary optimization technique has been evaluated in terms of its convergence behaviour for different mutation schemes. Additionally, the behaviour of the designed filter under different mutation strategies has been analyzed by studying its magnitude and impulse response. Finally, the most suitable mutation scheme has been suggested for this specific design problem.Implementation of extended Kalman filter on FPGA
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0803
The purpose of this paper is to explore the concepts and consequences of implementing the Extended Kalman Filter (EKF) on the FPGA. The methods of Runge-Kutta and Taylor-Heun are applied to approximate the continuous time update. The methods of Rugge-Kutta and Gauss-Legendre are used to solve the Riccati equation in order to update the discrete time measurement. The simulation on Matlab Simulink and implementation on the hardware in-loop are completed. Tradeoff between clock frequency, hardware resources and design accuracy are analyzed in the design. Recommended works describe the limitation of the design and give the suggestion on how to save the hardware resources and increase the accuracy.A de-noising method for heart sound signal using Otsu's threshold selection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0848
In this research, Otsu's method methods is quoted and applied to de-noise heart sounds so that unwanted noises can be separated from a combined set of noises extracted through an electronic stethoscope. The noise was analyzed by applying a random but suitable threshold section method, the unwanted noise was reduced and the useful sound was reconstructed. The initial result shows that the method applied is effective and the reconstructed signal appears to be providing a better quality of heart sound.Practical design of multi-channel oversampled warped cosine-modulated filter banks
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0844
A practical approach to optimal design of multichannel oversampled warped cosine-modulated filter banks (CMFB) is proposed. Warped CMFB is obtained by all- pass transformation of uniform CMFB. The paper addresses the problems of minimization amplitude distortion and suppression of aliasing components emerged due to oversampling of filter bank channel signals. Pro- posed optimization-based design considerably reduces distortions of overall filter bank transfer function taking into account channel subsampling ratios.PQ events classification and detection - a survey
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0412
This paper carries out a comprehensive review of various techniques used in the recent years in PQ event classification. Within this context, artificial intelligence and optimization techniques as well as their fusion have been reviewed in the field of PQ. The important techniques used in past are also provided in tabular form. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of PQ and wish to explore the opportunities offered by these techniques for further improvement in the same field. It is also analysed that still more research is to be done for online analysis of PQ events.SpaceAnnotator: a high precision location based asset management system in indoor environment
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0748
Asset management is urgently needed in supply chain which requires to solve two basic problems 1) what assets do we have; and 2) where they are? Existing methods exploit barcode and RFID technologies to retrieve the information and quantity of assets. However, the location of asset is still hard to obtain for the lack of suitable location technologies. In this paper, a high precision location based asset management system named SpaceAnnotator is proposed. SpaceAnnotator is implemented based on TOA positioning method using Ultrasound and RF signals. Leveraging the centimeter level positioning accuracy provided by the positioning system, SpaceAnnotator maps the IDs of objects to their locations. Based on the location information, location based service (LBS) in provided for asset management. Compared with conventional location based asset management system, SpaceAnnotator works well even in managing small volume objects for its high accuracy.A high speed turbo decoder implementation for CPU-based SDR system
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0622
More and more CPU-based SDR systems appear in recent two years. Such system requires high speed real-time signal processing. In this paper, we present our effort on the speed optimization of Turbo decoder, the most computation-demanding module in all baseband modules. We jointly consider the algorithm parallelism and the processor architecture. Single Instruction Multiple Data (SIMD) instruction is used for software implementation. The evaluation results show that this proposed design can achieve a maximum of 124 Mbps throughput for single Soft Input Soft Output (SISO) module with Max-Log-MAP algorithm.Extracting singing melody in music with accompaniment based on harmonic peak and subharmonic summation
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0989
In content-based music applications, music melody is one of the most basic features. In this paper, we proposed a melody extraction method for music with accompaniment based on spectral peak peaking and subharmonic summation. We conducted the performance evaluation of the proposed method against the Cepstrum method, the harmonic overtone detection (HOD) based method and the YIN algorithm using the MIREX data set. Our evaluation results showed that the proposed method is accurate, fast and interference-resistent. The overall pitch accuracy of the proposed method is as high as 92.3% for clean music, and 51.6% for music with accompaniment of equal strength. We also implemented this method in a prototype online Karaoke system and demonstrated its real-time performance in a practical system.An audio fingerprinting system based on spectral energy structure
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0301
Audio fingerprint is a compact unique content-based digital signature of an audio signal. It's an interesting technique that can be used to identify unknown audio clips. Generally, it mainly consists of two parts, i.e. fingerprint extracting from audio signals and fingerprint matching against those stored in a fingerprint database that has been set up beforehand. With the rapid growth in the quantity of audio files, the probability of collision of different audio signals become relatively high and it has become very challenging to retrieve an audio recording in real-time from the ever-growing huge database. In this letter, we introduce a reliable audio fingerprinting system, which extracts audio fingerprints from an audio signal based on its spectral energy structure. Preliminary experimental results suggest that this fingerprinting system can work well in the application of broadcast monitoring. (4 pages)A kind of adaptive filter based on a new sparsity measure function
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.1480
We propose a new sparsity measure function which effectively reflects the vector sparsity. Using the correspondingly developed Iterative Shrinkage/Thresholding Algorithms (ISTA) in filter's adaptation process, our algorithm reduces the impact of measurement noise on the filter performance and converges accurately to the sparse solution. We also apply the Barzilai-Borwein (BB) method, which is developed for determinate environments, to calculate the step-size of adaptive filters with random input. The validity of BB method in adaptive filters is verified by its fast convergence rate in our tests. The idea of our method can actually be applied to general adaptive filters in sparse environments with performance improvements. Numerical simulations prove the effectiveness of the method: sparsity based adaptive algorithms achieve lower mean square error than original algorithms without sacrificing convergence rate.Fault isolation using self-organizing map (SOM) ANNS
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0923
This paper presents a Self-Organizing Map (SOM) Artificial Neural Networks method for fault isolation based on the condition of manufacturing components, equipments and processes. The signals reflecting the conditions of equipment is collected from a set of sensors and processed by signal processing methods, such as filter and de-noising. The features that are extracted in time domain, wavelet domain and wavelet domain are used to train SOM ANNs. After training, the faults are able to be isolated according to the features extracted from the real time information. This approach is very helpful to the maintenance decision- making. A case study shows that SOM ANN can isolate faults correctively and clearly.Parallel reconfigurable computing and its application to hidden Markov model
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0542
Parallel processing techniques are increasingly found in reconfigurable computing, especially in digital signal processing (DSP) applications. In this paper, we design a parallel reconfigurable computing (PRC) architecture which consists of multiple dynamically reconfigurable computing units. The hidden Markov model (HMM) algorithm is mapped onto the PRC architecture. First, we construct a directed acyclic graph (DAG) to represent the HMM algorithms. A novel parallel partition approach is then proposed to map the HMM DAG onto the multiple DRC units in a PRC system. This partitioning algorithm is capable of design optimization of parallel processing reconfigurable systems for a given number of processing elements in different HHM states.Virtual production line based on lightning
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.1271
The technologies of VR have been adopted in simulation fields for its interactive ability and realistic senses. In our contribution we present a novel VR system for production line simulation which based on Lightning .This VR system facilitates the virtual equipment modeling, virtual process modeling as well as virtual control modeling of the production line. In this system, Multi-layer simulation method is also used to manage the signals from different levels. The system receives signals from online equipments and with the real-time signals, the online equipments as well as the virtual process will be quickly diagnosed. Rapid responds for production line will be made by VR system, and with this responds design engineer has the chance to optimize or make forward-looking decision of the line by using the advantage of this VR system.Capacitive instrumentation amplifier for low-power bio potential signal detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0487
This paper presents the design of a low-power capacitively-coupled CMOS instrumentation amplifier (INA) for long term bio potential measurement and recording applications. The increased demand for low cost, portable and wearable medical monitoring equipment for EEG (electroencephalogram) and ECG (electrocardiogram) is in turn giving rise to a need for very low power, but high precision, analogue front ends. EEG and ECG signals are differential signals characterized by very low amplitude (up to 100μV for EEG and up to 5mV for ECG), relatively low bandwidth (0.5Hz-150/300Hz) and a quite noisy environment. We present an instrumentation amplifier that achieves the specifications recommended by the application while lowering as much as possible the power consumption through appropriate design choices. The front end has been designed and simulated using a CMOS 0.35μm AMS process.The modern DSP implementation of α-β flight track filter algorithm
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0669
This paper is using the basic theory of IIR digital filter and FPGA as hardware implementation to design α-β flight track filter algorithm to implement and realize a modern DSP model. And provide respectful data analysis and simulation results. The advantage of this model is simple and straight forward, pipeline to control easy, computation effective high, fast speed and realtime. This model can be used widely in high data rate radar's real-time flight track filter application.A low-power wireless ECG processing node and remote monitoring system
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0486
The Electrocardiogram (ECG) is an important physiological measurement that is critical in the diagnosis of many cardiac disorders. In this paper, the design of a wireless system for ambulatory ECG acquisition and analysis is discussed. The wireless ECG node can operate in one of two configurations, where (i) raw ECG data is continuously transmitted to a base station, or (ii) the Heart Rate (HR) is calculated on the node, and only HR data is transmitted. The power requirements of the wireless node are also analysed. Furthermore, the system design includes a complementary base station node, which can be attached via USB to the patient's PC. The system back-end is also presented, whereby patient data is uploaded to a remote server, and can be accessed by the patient's healthcare team via a secure website.A/D conversion based on signal decomposition for DSP applications
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0614
Digital Signal Processing (DSP) systems operating on analog inputs are often limited by sampling rates of Analog to Digital Converters (ADC). ADCs are critical components since they tend to determine the overall system performance. Hence it is important that ADCs possess characteristics like high sampling rate, low conversion delay and high resolution that enhance system performance. In this paper, we propose a novel ADC to be used in signal processing based on signal decomposition. The proposed scheme folds the input signal symmetrically prior to quantization by high speed comparators. However, unlike folding ADCs that divide the input analog signal into two components, corresponding to coarse and fine quantization, the proposed converter minimizes delay and circuit complexity by introducing a Folding Bit. It does not use explicit circuits for coarse and fine quantization but uses only folding circuits to generate both most significant and least significant bits directly eliminating synchronizing problems normally encountered in the recombination of coarse and fine quantized bits.An experimental comparison on KEMAR and BHead210 dummy heads for HRTF-based virtual auditory on Chinese subjects
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0692
Head related transfer functions (HRTFs) vary with anatomical structures in head, pinna and torso. As we know, Chinese are highly different from their Western counterparts in anatomical characteristics. In this paper, we perform an experimental study on HRTF-based virtual auditory, in which the HRTF data are collected from two dummy heads: KEMAR and BHead210. KEMAR resembles the head of a worldwide average human and BHead210 replicates the head of an average Chinese. Our preliminary results show significant differences in sound localization performance between the two dummy heads. The localization performance, especially on the frontal plane, of BHead210-collected HRTF is better than KEMAR for Chinese subjects. We believe BHead210 is more suitable for Chinese in HRTF-based virtual auditory applications.Rolling bearing fault analysis and wavelet thresholding research
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.1285
The causes of bearing fault and frequency changes are analyzed. The actual bearing fault signals are analyzed by wavelet and signal noise are reduced by three wavelet thresholding model. Bearing noise reduction effective way is obtained with three wavelet thresholding model comparison, and frequency analysis of signal is made in order to get bearing characteristic value.Audio thumbnail generation of Irish traditional music
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0504
An approach is presented which generates an audio thumbnail of Irish Traditional music. An audio thumbnail is considered to be the most representative segment of the music. For popular music, the chorus is considered to be an ideal audio thumbnail, however in Irish Traditional music there is no chorus. An Irish Traditional tune consists of two or more short structural segments called parts. Parts are repeated to extend the tune, and the tune itself is also repeated once or more in its entirety. To further extend a performance, tunes are concatenated to form a set of tunes. As a result, there is plenty of repetition within this music type. The presented approach utilises an existing approach which calculates the structure of Irish Traditional Music. The structural information is used to extract a single rendition of each distinctive part. The resulting parts are concatenated to form the audio thumbnail.A new context awareness scheme for multi-mode mobile terminals in mobile Internet
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0627
Mobile Internet should provide pervasive services anywhere, anytime, and in numerous ways. Therefore, it is demanded that users could visit Internet services through any kind of radio access technology. Context awareness for multimode mobile terminals (MMT) in Mobile Internet is very important in next generation wireless mobile communications. It is the foundation of network co-operation and seamless access for user services. In this paper, a new context awareness scheme for MMT is proposed. And monitoring models for context awareness is also raised. The scheme is based on mobile agent technology, which is beneficial for saving monitoring cost of MMT. Furthermore, a mechanism of radio access network (RAN) monitoring is presented. This mechanism is on the basis of cognitive radio and reconfiguration technologies. It not only improves the intelligence of MMT but also can be widely applied in heterogeneous radio environment. Signal detecting method is designed, and the process of this mechanism is also given. Finally, in the context composed of 4 kinds of overlapped RANs, the availability of this scheme is proved by simulation results.Simple digital control of a two-stage PFC converter using DsPIC30F microprocessor
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0058
The use of dsPIC digital signal controllers (DSC) in Switch Mode Power Supply (SMPS) applications opens new perspectives for cheap and flexible digital control solutions. This paper presents the digital control of a two stage power factor corrector (PFC) converter. The PFC circuit is designed and built for 70W rated output power. Average current mode control for boost converter and current programmed control for forward converter are implemented on a dsPIC30F1010. Pulse Width Modulation (PWM) technique is used to drive the switching MOSFETs. Results show that digital solutions with dsPIC processor can be competitive with analogue controllers in cost and efficiency. (4 pages)HetMoC: heterogeneous modelling in SystemC
http://dl-live.theiet.org/content/conferences/10.1049/ic.2010.0139
We propose a novel heterogeneous model-of computation (HetMoC) framework in SystemC for embedded computing systems. As the main contribution, we formally define the computation and communication in multiple domains (continuous-time, discrete-event, synchronous/reactive, and un tuned) as polymorphic processes and signals, and present domain interfaces to integrate different domains together for heterogeneous process networks. Especially, the continuous-time signals are defined with time continuum, which are distinguished from existing approaches. For implementation, a functional modelling style has been adopted to construct HetMoC. A solver with error estimation has been exploited in numerical approximation, and the time-varying functionalities in adaptive systems have been captured in HetMoC as well. In experiments, based on an adaptive transceiver system case study, HetMoC shows promising capabilities compared with a reference model in SystemC-AMS.Kalman filter based sensorless control of a tubular permanent magnet machine for active vehicle suspension
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0072
This paper describes Kalman filter based sensorless control of a linear tubular permanent magnet (PM) machine for active vehicle suspension. A compact strut design is realised by integrating a coil spring into a high force density tubular PM machine, and sensorless control of the actuation force is achieved by employing an extended Kalman filtering technique. The issues pertinent to the design of the Kalman filter are discussed, and utility and effectiveness of the sensorless operation are demonstrated by extensive simulations of the vehicle suspension system with measured real road data. (6 pages)An investigation of the impact of artifact detection on heart rate determination from unsupervised electrocardiogram recordings
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1678
A variation of an existing technique for movement artifact detection in single lead ECG signals acquired in the unsupervised telehealth environment is examined. The impact on heart rate (HR) estimation is investigated using this artifact detection technique to remove noisy sections of signal. The estimated artifact masking and HR values are compared to a gold standard scoring, performed by consensus of an expert panel. The employment of the proposed artifact detection scheme shows an improvement in the estimated values of HR; the error in the estimated HR, from 126 of 192 signals, was less than ±0.5 BPM; compared to only 67 of 212 signals using no artifact detection; the estimation bias was reduced from an underestimation of -1.33 BPM to -0.63 BPM; the standard deviation of the error was reduced from 4.81 BPM to 3.58 BPM. The results indicate that the automated interpretation of inherently noisy ECG recordings, from the telehealth environment, becomes a feasible proposition when ECG signal quality indicators are leveraged. (6 pages)A numerically robust method for DOA estimation
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0235
In this paper, a numerically robust method for direction of arrival (DOA) estimation based on the multiple signal classification (MUSIC) algorithm is proposed. The new signal subspace is obtained by the column space of the transform matrix in the Householder-based Multistage Wiener Filter (HMSWF). The Householder transform matrix in HMSWF ensures the unitary blocking operation and significantly strengthens the orthogonal property between the basis vectors in finite-precision implementation. Compared with the traditional MSWF, simulation results demonstrate the numerically robust performance of the proposed method. (4 pages)Research on knowledge-based STAP technology
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0348
Great progress has been made in the STAP technique with the decades development, but some problems are still faced in the real application. Firstly a knowledge-based (KB) STAP technique was introduced in this paper. And then several applications were investigated in detail, and the key problems of developing KB STAP are analyzed. Finally, some advices were given. (4 pages)The performance of power transform in high resolution radar target identification
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0315
Power transform is a nonlinear pretreatment method, which can improve the recognition rate of high resolution range profiles obviously. This paper mainly analyses how we should decide the power transform coefficient in order to get good recognition results. We process the range profiles in different noise condition using different power transform coefficients, and then we classify them with radial basis function classifiers. According to the real data processing results, we know that we should decide power transform coefficients in accordance with the signal-to-noise ratio. (4 pages)Study of high performance and efficient decimator filters
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.2061
In this paper, a novel version of decimation filters for power-of-two decimation factors, which is a useful alternative to the CIC decimation filter, is proposed for high performance and efficient applications. The proposed filter, which is a cascade of the cascaded Cosine filters with the interpolated second-order polynomial (ISOP) filter, provides better passband droop performance and more alias suppression than CIC filters. Furthermore, the proposed filter is also multiplier-free and can provide much more attention of the alias suppression by cascading only some single-stage cosine filter, rather than by using multiple copies of original filters like CIC filter, thereby achieving a significant hardware reduction over CIC filter.Array calibration with sensor position errors using particle swarm optimization algorithm
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0240
The performance of high-resolution algorithm will degrade badly in the presence of sensor position errors. To deal with this problem, a mew method for array calibration using particle swarm optimization (PSO) algorithm is proposed. This new method has no requirement for calibration sources while the sensor position errors as well as the direction-of-arrivals (DOAs) of the incident signals can be estimated simultaneously. Computer simulations are conducted to show the validity and feasibility of the proposed method. (3 pages)Multi-classification of audio signal based on modified SVM
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1958
As one of the important multimedia information carrier, audio signal effectively enriches and satisfies people's apperception and acquirement of the information; in order to improve the accuracy of audio classification, we adopt the modified SVM that is based on hierarchical clustering analysis and binary decision tree to classify the seven types of audio signal in this paper, a number of the samples are used for training of each audio signal so as to obtain the excellent training templates, and then to test the audio signal. Experimental results show that the method has a good classification performance, compared with the traditional one-to-one and other algorithms, our algorithm not only reduces the training and testing time, but also further improves the accuracy rate, up to over 90%.Blind estimation of source number based on SVM in wireless communication systems
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1902
In this paper, a novel method to estimate the number of source signals based on support vector machine (SVM) is proposed. The estimation problem of number of source signals is transformed into the problem of multi-class classification, and the SVM is applied to solve the problem of multi-class classification. The proposed method can be used when the modulation type chooses from the QPSK and BPSK at random. The application of the novel method can expand present well-determined situation and over-determined situation into the under-determined situation. Simulation results confirmed these conclusions.An investigation of the impact of gait segmentation on accelerometry-based inclined terrain classification
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1679
Traditional methods of energy expenditure estimation, in the free-living environment, attempted using accelerometry operate without knowledge of the slope of the terrain which is being traversed. The ability to recognise the gradient of the walking surface will most likely improve upon these simplistic energy estimates. This paper expands upon previous work in this area, and investigates the benefit of step-by-step segmentation of the accelerometry signal in classifying the various gradients. Tri-axial accelerometry signals from 12 subjects, performing 30 s of walking on 4 different gradients (up and down paved ramps with gradients of 4.8% and 17.2%), were collected. A feature subset selection search procedure was applied to find the optimal subset of 65 extracted features which maximise the classification accuracy, performed with a Gaussian Mixture Model (GMM) classifier, as estimated using six-fold cross-validation. An overall classification accuracy of 94.83% was achieved using 13 features, for the four-class problem. There was an improvement of 4.1% upon the same classification task, without knowledge of the start/end times of individual steps, indicating that segmentation of the accelerometry signals at a step-by-step resolution is important for the automated classification of terrain gradient during walking. (6 pages)Robust adaptive beamforming algorithm in the presence of mismatches
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0212
Adaptive beamforming is known to have resolution and interference rejection capability when the array steering vector is precisely known. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the assumed array response and the true array response. Similar types of degradation can occur when the signal array response is known exactly, but the training sample size is small. In this paper, we propose a novel robust adaptive beamforming algorithm, which is based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix. The proposed algorithm belongs to the class of diagonal loading approaches, but the diagonal loading term can be precisely calculated, which is incorporated at each step. The proposed algorithm has nearly optimal performance under good conditions, provides the robustness against the signal steering vector mismatches and the small training sample size, and makes the mean output array SINR consistently close to the optimal one. Simulation results validate substantial performance improvements relative to other adaptive beamforming methods. (4 pages)A comparison of wideband beamformers in coherent situations
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0298
In this paper, we compare the performance of two classes of adaptive beamformers for wideband and coherent signals. It is well known that when the desired signal and interferences are partially or completely correlated, performance of conventional beamformer degrades severely. Therefore, spatial smoothing method and frequency focusing method have been proposed to tackle coherent sources. In this paper, we first discuss a frequency focusing method which is based on time-domain and can be applied to real time systems. And then, a new wideband beamformer was proposed by introducing spatial smoothing method into Frost beamformer. Result of simulations shows that the new beamformer has better frequency response than the time-domain based frequency focusing beamformer. (4 pages)CIOR analysis approach for SAR real-time signal processing cluster based on shared bus
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.0475
In the paper, SAR processing model is analyzed firstly. After the analysis of real-time processing cluster based on shared bus, the CIOR(Compute-IO Rate) conception is introduced, and the CIOR of some SAR algorithms and processors are assessed. Finally, several suggestions on how to improve the processing cluster performances are given. (4 pages)Automatic hexaphonic guitar transcription using non-negative constraints
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1699
Automatic music transcription is a widely studied problem, Typically, recordings that are used for transcription are taken from standard instruments, in the case of electric stringed instruments-such as the electric guitar-the recordings are captured from a standard pick-up, which unwantedly mixes the signals from each string and complicates subsequent analysis. We propose an approach to electric guitar transcription where the signal generated by each string at the guitar pickup is captured and analysed separately; thus providing six separate signals as opposed to one mixed signal, which enables finger positions to be identified. Such an instrument is known as a hexaphonic guitar and is a popular instrument for spatial music performances. We build the equipment necessary to modify a standard electric guitar into a hexaphonic guitar, and present an application of non-negative matrix factorisation to the task of transcription-where a basis for each note on the fretboard is learned and fitted to a magnitude spectrogram of the hexaphonic recording, which then undergoes a nonlinearity generating a piano roll representation of the music performance. (6 pages)Optimising recognition rates for subject independent gait pattern classification
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1680
This paper describes a study which was carried out to determine an optimally performing classification algorithm for the problem of subject independent gait pattern classification. The study utilised a frequency domain based feature vector based on the concept of cepstral coefficients whose generation methodology was optimised in terms of overall system recognition rates. The performance of a number of both linear and nonlinear classification algorithms including neural network and Support Vector Machines was examined. An optimal recognition rate of 78.4±3.2% was achieved using a "one-versus-all" MLP classier applied to a previously unseen test database of 12 subjects completing ten repetitions of five different human gait patterns including walking on level surfaces, walking up and down stairs and walking up and down ramps. (6 pages)