Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLABbased tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/− 0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value <0.001, and RR interval 1 +/− 0.001 p-value <0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease. INDEX TERMS Electrocardiogram, digitization, Matlab tool, image processing.

A cognitive transceiver is required to opportunistically use vacant spectrum resources licensed to primary users. Thus, it relies on a complete adaptive behavior composed of: reconfigurable radio frequency (RF) parts, enhanced spectrum sensing algorithms, and sophisticated machine learning techniques. In this paper, we present a review of the recent advances in CR transceivers hardware design and algorithms. For the RF part, three types of antennas are presented: UWB antennas, frequency-reconfigurable/tunable antennas, and UWB antennas with reconfigurable band notches. The main challenges faced by the design of the other RF blocks are also discussed. Sophisticated spectrum sensing algorithms that overcome main sensing challenges such as model uncertainty, hardware impairments, and wideband sensing are highlighted. The cognitive engine features are discussed. Moreover, we study unsupervised classification algorithms and a reinforcement learning (RL) algorithm that has been proposed to perform decision-making in CR networks.

Abstract-The design of filter antennas with reconfigurable band stops is proposed. They are meant for employment in ultrawideband cognitive radio (UWB-CR) systems, where unlicensed users communicate using adaptive pulses that have nulls in the bands used by licensed users. Neural networks or circuits implementing the Parks-McClellan algorithm can generate such pulses. With filter antennas, reconfigurable bandstop filters are first designed, to induce adaptive nulls in UWB pulses, and are then integrated in the feed line of a UWB antenna. The advantages of this combination are discussed. The filters are based on split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs). The relationship between the SRR and CSRR parameters and the stop band is also studied.

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Spectrum sensing has been identified as the key step of the cognition cycle and the most important function for the establishment of cognitive radio. In this paper, a blind cyclostationary feature detector, which is based on the symmetry property of cyclic autocorrelation function (SP-CAF), is implemented and tested using universal software radio peripheral platform and GNU Radio open-source software development toolkit. Performance of the SP-CAF is compared to the classical energy detector via various tests conducted in real scenarios where both detection algorithms are employed to blindly sense the spectrum for opportunistic access. This study shows that the blind cyclostationary feature detector outperforms the classical energy detector while guaranteeing acceptable complexity and low sensing time. Moreover, different experimental results indicate that the blind sensing detector can achieve high detection probability at a low false alarm probability under real channel conditions and low signal-to-noise ratio.

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Spectrum scarcity has emerged as a primary problem in the communications technology. The combination of cognitive radio (CR) and ultra-wideband impulse radio (UWB-IR) has been proposed to solve the shortage problem by allowing smart and adaptive spectrum management, leading to UWB-CR. In a UWB-CR scheme, secondary users are supposed to ensure interference avoidance by adaptively selecting the portions of the spectrum not being used by primary users. In this paper, three different techniques for the generation of adaptive UWB pulses are studied. The Parks-McClellan algorithm is employed, a neural network is trained, and a reconfigurable band stop filter is designed to generate an adaptive waveform with nulls at specific frequencies. Simulations, measurements, and analysis show that each generated UWB pulse has remarkable advantages in the frequency utilization, spectrum avoidance, and hardware implementation.

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