The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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Wavelet transforms and the ECG: a review.

Recommendations for the Standardization and Interpretation of the Electrocardiogram

In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

https://www.heartrhythmjournal.com/article/S1547-5271(07)00105-1/pdf

Recommendations for the Standardization and Interpretation of the Electrocardiogram: Part I: The Electrocardiogram and Its Technology A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology

Machine learning algorithms for wireless sensor networks: A survey

In this chapter, the feasibility of using eye blinking Electro-Oculo-Gram (EOG) signal as a new biometric trait for human identity recognition is tested. For this purpose, raw Electro-Encephalo-Gram (EEG) signals were recorded from 40 volunteers while performing the task of eye blinking. These signals were recorded using portable EEG headset, known as Neurosky Mindwave, which has wireless and dry electrodes at Fp1 position above the left eye. This makes it practical for biometric applications and for measuring EOG signals. For pre-processing, Discrete Wavelet Transform (DWT) is adopted to isolate EOG signals from brainwaves. Then, the onset and the offset of the eye blinking waveforms in the EOG signals are detected. After that, features are extracted using time delineation of the eye blinking waveform where important marks like the amplitude, position, and duration of the positive and negative pulses of the eye blinking waveform are employed as features. Finally, Discriminant Analysis (DA) classifier is used for classification. Moreover, a feature selection technique based on differential evolution is added for the proposed system. The best Correct Recognition Rate (CRR) achieved is 93.75 %. In verification mode, the lowest Equal Error Rate (EER) achieved is 7.45 %. Also, the permanence issue is evaluated using training and testing samples with different time separation between them. The optimistic results achieved in this chapter direct the scientific research to study different approaches for human identification using eye blinking to increase system’s performance. Moreover, eye blinking EOG biometric trait can be fused with other traits like EEG signals to build a multi-modal system to improve the performance of the EEG-based biometric authentication systems.

Eye Blinking EOG Signals as Biometrics

Energy saving is one of the most important issues of wireless sensor networks that are gaining a lot of attention. In other words, energy modeling plays a greater role in energy optimization that helps designers to produce an economical and practical design of sensor nodes. In this paper, an energy consumption model is proposed considering most of the parameters of both MAC and physical layers, unlike other related works that concern with either MAC or physical layer parameters. The proposed energy consumption model is validated with real measurements and NS-2 simulator. Results show good agreement between proposed model, experimental measurements and NS-2 simulator with mean absolute percentage error less than 6 %. The validated model is used to optimize transmitted power to achieve minimum energy consumption. Finally, a closed-form expression for optimum transmitted power is derived for different modulation schemes.

Modeling of Wireless Sensor Networks with Minimum Energy Consumption

In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.

PCG biometric identification system based on feature level fusion using canonical correlation analysis

A novel approach is presented to the design and synthesis of switched-capacitor (SC) filters. An efficient iterative algorithm is described for the construction of a class of selective linear-phase bandpass filter with equiripple passband amplitude response. The solution is obtained in terms of digital linear phase bandpass polynomials. A novel SC implementation is performed using an approach based on the reflection filter concept. The synthesis of the SC filter follows the steps used in microwave filter synthesis. The resulting SC circuits are completely insensitive to parasitic capacitances. Design tables and an illustrative example are also given. >

Design of selective linear phase bandpass switched-capacitor filters with equiripple passband amplitude response

Automatic detection and tracking of multiple vehicles in airborne videos is still a challenging problem due to camera movement, vehicle occlusion and the need for computational resources. This paper presents a robust and efficient real-time method for automatic detection and tracking of vehicles in airborne videos. The detection process is based on a combination of Top-hat and Bot-hat transformation aided by the morphological operation. Background objects are removed through analyzing feature points motion of the obtained object regions using K-means clustering and KLT tracker. The obtained vehicles features are grouped and clustered into separate vehicles based on their motion properties. Finally, a connecting scheme is presented to determine the connectivity of vehicle cluster with the corresponding cluster in the vehicles trajectories. Experiments conducted on videos representing airborne cameras verify the excellent performance compared to other existing approaches.

Mohammed Abo-Zahhad

Papers

Eye Blinking EOG Signals as Biometrics

Modeling of Wireless Sensor Networks with Minimum Energy Consumption

PCG biometric identification system based on feature level fusion using canonical correlation analysis

Design of selective linear phase bandpass switched-capacitor filters with equiripple passband amplitude response

Real-Time Algorithm for Simultaneous Vehicle Detection and Tracking in Aerial View Videos