This paper seeks to enhance the performance of Mel Frequency Cepstral Coefficients (MFCCs) for detecting abnormal heart sounds. Heart sounds are first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs are used for heart sound classification. For that purpose, a single classifier and an innovative ensemble classifier strategy are presented and compared. In the single classifier strategy, the MFCCs from nine consecutive beats are averaged to classify heart sounds by a single classifier (either a support vector machine (SVM), the k nearest neighbors (kNN), or a decision tree (DT)). Conversely, the ensemble classifier strategy employs nine classifiers (either nine SVMs, nine kNN classifiers, or nine DTs) to individually assess beats as normal or abnormal, with the overall classification based on the majority vote. Both methods were tested on a publicly available phonocardiogram database. The heart sound classification accuracy was 91.95% for the SVM, 91.9% for the kNN, and 87.33% for the DT in the single classifier strategy. Also, the accuracy was 93.59% for the SVM, 91.84% for the kNN, and 92.22% for the DT in the ensemble classifier strategy. Overall, the results demonstrated that MFCCs were more effective than other features, including time, time-frequency, and statistical features, evaluated in similar studies. In addition, the ensemble classifier strategy improved the accuracies of the DT and the SVM by 4.89% and 1.64%, implying that the averaging of MFCCs across multiple phonocardiogram beats in the single classifier strategy degraded the important cues that are required for detecting the abnormal heart sounds, and therefore should be avoided.

This paper presents two efficient frameworks for seizure detection and prediction that depend on statistical analysis. The common thread between them is the selection of certain attributes extracted from the electroencephalography (EEG) signals and the derivation of probability density functions (PDFs) of these attributes in two different types of activities of EEG signals. The first framework is for seizure detection based on scale-invariant feature transform (SIFT). Its idea is to choose some segments for normal and seizure activities. These segments are transformed to 2D matrices to be treated with the well-known SIFT. The feature key-points are extracted from those 2D matrices. The parameter that is used to discriminate between normal and seizure activities is the number of key-points. The PDFs of the number of key-points in cases of normal and seizure activities are estimated and a threshold value is used to classify any new segment as either a seizure or a normal segment. The other framework is for seizure prediction. The discrimination in this case is between normal and pre-ictal activities. A statistical treatment is performed on five attributes extracted from the wavelet transforms of different EEG segments for normal and pre-ictal activities. These attributes are amplitude, local mean, local variance, median and derivative. All PDFs of these attributes are estimated for normal and pre-ictal activities. Thresholds are set for all attributes. Decisions are taken based on these thresholds. Finally, a majority-voting strategy is used to merge decisions taken for all attributes.

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal Communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

Emotion dysregulation and social dysfunction are core characteristic of many psychiatric disorders such as schizophrenia and mood disorders.Medication-based treatment for patients with psychiatric disorders may alleviate acute clinical symptoms; however, it is limited in terms of its ability to improve social and emotional functions.There is evidence highlighting the central role of emotion regulation deficits in determining social functioning in individuals with psychiatric disorders.Therefore, emotion-related training intervention is needed to enhance patient's awareness and expression of emotions and for successful social functioning.Aim: the aimof this study was to evaluate the effect of an emotion regulation training intervention on social functioning of patients with psychiatric disorders.Setting:The present study was conducted at psychiatric inpatient department of TantaUniversity Hospital that is affiliated to Tanta University.Subjects: 60 patients with psychiatric disorders divided randomly into experimental and control group (30 patients in eachgroup).Tools:Difficulties in Emotion Regulation Scale and Social Functioning Scale were used to collect the study data.Design:A randomized controlled trial was conducted.The intervention was conducted through 9 training sessions, each session lasting from (60-90) minutes; 3 times per week for a period of 4 weeks.Results: there was a statistically significant improvement in social functioning and emotion regulation in the experimental groupthan the control group after the implementation of the intervention.Also, a statistically significant negative correlation was found between emotion regulation difficulties and social functioning.Conclusions :Emotion regulation training intervention has a salient effect on reducing emotion regulation difficulties and improving social functioning in patients with psychiatric disorders.Recommendations: this non-pharmacological evidence-based-nursing practice should be incorporated in the psychiatric hospital protocol for cumulative and consistent effects

In this study, a new non-iterative adaptive beamforming (ABF) algorithm for the signal-to-interference and noise ratio (SINR) enhancement is proposed. It is based on a combination between the direction of arrival (DOA) estimation and the method of moments (MoM). The proposed algorithm is denoted as DM/ABF which stands for DOA and MoM-based ABF. The DOA is used to provide accurate estimates for the directions of the desired and interfering signals. On the basis of the estimated DOAs, a dedicated shaped pattern version of the ordinary pattern is created and applied as the desired input to the MoM algorithm. The MoM is used for shaped pattern synthesis to estimate the weights vector required to provide deep nulls toward the interfering signals and directs the main beam toward the desired signal. In this case, the weights vector does not update iteratively at each received signal sample as in case of least mean square (LMS) and recursive least squares (RLSs) algorithms, but it is updated only when the estimated DOAs of the desired and interfering signals are changed. Furthermore, a large number of close nulls can be produced without the need for additional antenna elements compared with other algorithms.

The dynamic spectrum allocation is important to improve spectrum efficiency usage of available radio spectrum. Cognitive radio has emerged as a solution to dynamic spectrum access, due to its adaptability and re-configurability. The main problem of cognitive radio is how the secondary users can detect the holes in the frequency band of the primary users. Spectrum sensing is the main feature of cognitive radio technology. Finding a more accurate and efficient spectrum sensing technique is the core of cognitive radio technology to develop dynamic resource management in future wireless networks. A spectrum sensing based on the auto correlation technique provides performance improvement at very low SNR over the energy detection technique. In this paper, we show the autocorrelation spectrum sensing features over energy detection spectrum sensing technique. Analytical and Simulation results are performed in a non-fading and a fading environment as well. The results show that the autocorrelation technique has enormous superiority in performance over that of the energy technique.

Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the prediction and analysis of epileptic seizures. This paper provides a statistical analysis associated with EEG signals assuming that these signals can be categorized into inter-ictal, pre-ictal, and ictal states. We study histograms and cumulative histograms for segments of various signal states, in wavelet domain by utilizing different signal processing tools such as the differentiator and median filtering, as well as the local mean, and local variance estimators. The results show that signal states could be distinguished according to statistics in the wavelet domain.

Self cancellation schemes confirmed its effectiveness for mitigating the intercarrier interference (ICI) in orthogonal frequency multiplexing (OFDM) system.The main source of ICI is the carrier frequency offsets (CFO) in mobile radio channel which destroys the carrier's orthogonality.In this paper, we employ a self cancellation scheme known as the conjugate self cancellation scheme to reduce the impact the ICI in Multicarrier CDMA systems that use Time-Frequency domain (TF or Hybrid) spreading and are used in broadband wireless communications.We show with proof that TF-domain spreading Multicarrier CDMA system with conjugate cancellation (CC) works significantly better than conventional MC-CDMA system if the normalized frequency offset is less than 0.3 in the fading channels and in additive white Gaussian channel.The CC algorithm provides a high carrier to interference power ratio (CIR) in the small frequency offset compared with other self cancellation schemes and uses low cost receiver without increase in the system complexity.The drawback of this algorithm is the reduction in bandwidth efficiency, which can be compensated by using larger signal alphabet sizes.Simulation results show good agreement with the numerical results.

In general, Multicarrier systems are very sensitive to carrier frequency offset (CFO) which generates intercarrier interference (ICI) resulting in performance degradation. However, by using frequency diversity in multicarrier DS-CDMA system, the effect of ICI caused by CFO can be significantly reduced. This can be achieved in Time-Frequency (TF) domain spreading MC DS-CDMA system which employs both time and frequency (TF) domain spreading or hybrid spreading. In this paper, the performance of TF-domain spread MC-DS-CDMA system over Nakagami-m fading channels is evaluated and investigated with varying CFO, number of users, number of subcarriers, and Signal-To-Noise-Ratio (SNR). The probability of bit error rate (BER) of TF-domain spread MC-DS-CDMA is derived using the standard Gaussian Approximation (SGA) approach for different values of m parameter. Numerical evaluation of the analytical expression of BER reveals that a high capacity and BER performance improvement are achieved for TF-domain spread MC-DS-CDMA system over the performance of mnlticarrier CDMA (MC-CDMA) system which spreads the signal in the frequency domain only. The simulation results show excellent agreement with the analysis.

In this paper the effect of carrier frequency offset on the multicarrier direct sequence CDMA system which employs both time and frequency (TF) domain spreading is evaluated and simulated. The MC DS-CDMA system achieves high spectrum efficiency by using time-frequency spreading mechanism. The probability of bit error rate (BER) of TF- domain spread MC DS-CDMA is derived using the standard Gaussian approximation (SGA) in additive white Gaussian noise (AWGN) channel with frequency offset impairments. Numerical evaluation of the analytical expression of BER reveals that a high capacity and BER performance improvement are achieved over the performance of multicarrier CDMA (MC-CDMA) system which spreads the signal in the frequency domain. Extensive simulations were also performed to illustrate the correctness of the analysis of the TF-domain spread MC DS-CDMA system.

In this paper the effect of carrier frequency offset on the multicarrier direct sequence CDMA system which employs both time and frequency (TF) domain spreading or hybrid spreading is evaluated and simulated. The MC DS-CDMA system achieves high spectrum efficiency by using time-frequency spreading mechanism. The probability of bit error rate (BER) of TF-domain spread MC DS-CDMA is derived using the standard Gaussian approximation (SGA) in additive white Gaussian noise (AWGN) channel with frequency offset impairments. Numerical evaluation of the analytical expression of BER reveals that a high capacity and BER performance improvement are achieved over the performance of multicarrier CDMA (MC-CDMA) system which spreads the signal in the frequency domain. Extensive simulations were also performed to illustrate the correctness of the analysis of the TF-domain spread MC DS-CDMA system.

Low bit rate coding of voice is critical for accommodating more users on channels that have inherent limitations of bandwidth or power-like cellular radio or satellite links. Analysis-by-Synthesis (AbS) coders are the candidate coders for producing good quality speech in the range from 4.8 to 16 kb/s. The multi-pulse excited linear predictive coder (MPE-LPC), and the regular-pulse excited linear predictive coder (RPE-LPC) are among very large number of (AbS) speech coders. The WE and RPE can produce high quality speech at bit rate lower than 9.6 kb/s with a fairly moderate complexity. In this paper we introduce a novel approach for achieving variable-bit rates below 9.6 kb/s by using switched quantization. The issues of transmitting a 9.6 kb/s MPE and RPE analysis-by-synthesis predictive coded speech over a mobile radio-fading channel are discussed. The coder robustness and the sensitivity of speech-carrying bits to channel errors are also presented. Further, the performance of a pi/4 differential quadrature phase shift keying (pi/4 DQPSK) method over Rayleigh fading mobile radio channel is presented by the coder output. Objective and subjective tests are used to show the effect of the Rayleigh fading mobile radio channel on the performance of the coder, and also determine the amount of the minimum value of channel signal to noise power ratio (SNR dB) required to obtain a good speech quality.