Data Mining Practical Machine Learning Tools and Techniques

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Information Theory and Reliable Communication

Major depressive disorder (MDD) is a debilitating disease that is characterized by depressed mood, diminished interests, impaired cognitive function and vegetative symptoms, such as disturbed sleep or appetite. MDD occurs about twice as often in women than it does in men and affects one in six adults in their lifetime. The aetiology of MDD is multifactorial and its heritability is estimated to be approximately 35%. In addition, environmental factors, such as sexual, physical or emotional abuse during childhood, are strongly associated with the risk of developing MDD. No established mechanism can explain all aspects of the disease. However, MDD is associated with alterations in regional brain volumes, particularly the hippocampus, and with functional changes in brain circuits, such as the cognitive control network and the affective-salience network. Furthermore, disturbances in the main neurobiological stress-responsive systems, including the hypothalamic-pituitary-adrenal axis and the immune system, occur in MDD. Management primarily comprises psychotherapy and pharmacological treatment. For treatment-resistant patients who have not responded to several augmentation or combination treatment attempts, electroconvulsive therapy is the treatment with the best empirical evidence. In this Primer, we provide an overview of the current evidence of MDD, including its epidemiology, aetiology, pathophysiology, diagnosis and treatment.

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Major depressive disorder

Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.

http://ieeexplore.ieee.org/iel5/3/23094/01073023.pdf

Statistical optics

Distributed video coding (DVC) is a novel coding paradigm that offers low computational encoding relative to conventional video-coding framework at the expense of high-decoding computational complexity. The challenging part of this video-coding framework is achieving better rate-distortion (RD) compared with conventional codec performance. A suitable and accurate correlation noise model (CNM) is crucial in improving the RD performance by achieving high coding efficiency and making decoding less computationally demanding. Since the correlation is nonstationary and time-variant and can vary from frame to frame, offline CNM estimation is not feasible for practical applications and real-time decoding. An online CNM may be the solution to this problem. In DVC, neither Wyner–Ziv frame (WZF) nor estimated side information (SI) of the corresponding WZF is available at the encoder. Therefore, online estimation of the CNM and its parameters can be quite challenging. The contribution of this research work is a novel online CNM which is computed by taking the mean of each transformed coefficient band and deployed for two different codecs. Our proposed codec, DIVCOM, which stands for “Distributed Video Coding with Online Band Mean Correlation Noise Model”, outperforms the existing baseline codec, DISCOVER (DIS), in both coding efficiency and peak signal-to-noise ratio (PSNR). The DIVCOM codec achieves coding efficiency of up to 8.05 kbps, and PSNR ranges from 0.0245 dB to 0.18 dB. An extended version of DIVCOM incorporating phase-based side information called PDIVCOM achieves coding efficiency up to 10.9 kbps, and PSNR ranges from 0.019 to 0.17 dB compared to DIS.

/pdf/a-novel-online-correlation-noise-model-based-on-band-36vo0tl9.pdf

A Novel Online Correlation Noise Model Based on Band Coefficients Mean to Achieve Low Computational and Coding-Efficient Distributed Video Codec

Driver drowsiness is a well known problem that depreciates road safety that could cause road accidents, worldwide. Researchers are increasingly using the eye/eyelid images or the electroencephalogram’s (EEG) spectral information to detect drowsiness in drivers. However, no attempt has been made to detect drowsiness using the eye blink artifact features that contaminates EEG signals, which are typically regarded noise and undesired. Therefore, in this study, we have investigated whether the eye blink artifacts that were originally intended to be eliminated from EEG signals could be used to detect drowsiness among drivers. The eye blink artifacts and their features are extracted from EEG signals via the BLINKER algorithm. The deep learning classifiers, multilayer perceptron (MLP) and Recurrent Neural Network with Long-Short-Term-Memory (RNN-LSTM) are trained, validated, and tested to confirm if the eye blink artifacts can be used as an indicator of drowsiness. The investigation has demonstrated that using eye blink artifacts as an indicator of drowsiness is viable, with a classification accuracy of 94.91% achieved through RNN-LSTM.

An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models

A study on drowsiness detection is being conducted based on the video dataset of 22 subjects who are wearing EEG sensors while driving on a simulator. The video dataset is not labelled, and it is in the process of being labelled by using Percentage of Eye Closure (PERCLOS) which is an indicator of drowsiness level. One of the preliminary steps in obtaining PERCLOS values, is to determine the eye state (open or closed) of subjects. A common method used by researchers to determine the eye state of the subject is using Eye Aspect Ratio (EAR) values that are obtained from face landmark detectors such as DLib's 68 face landmarks predictor. Based on the evaluation of the DLib's solution on 151,537 frames (approximately 84 minutes) of one subject, it was found that 98.66% of eyes state were classified correctly which resulted in 378 blinks to be detected (expected 212 blinks). The 468 3D face landmarks detector from MediaPipe (Google) was proposed as an alternative and it managed to classify the same subject with a classification accuracy of 99.87% with 264 blinks (only 52 extra blinks). Plenty of researchers are not aware of this solution and are uncertain of how it compares to DLib's solution. Thus, this paper compares the performance of these two solutions in terms of processing time and eye state classification metrics based on 10 diverse subjects.

Comparative Study of State-of-the-art Face Landmark Detectors for Eye State Classification in Subjects with Face Occlusion

The H.264/AVC video compression standard incorporates new features to enhance video coding. It supports a variety of block sizes and shapes for motion estimation (ME), and gives more flexibility than previous standards. In this paper, we propose a method of determining the block mode to be used for a particular video sequence to give the optimum performance for the encoder. To reduce the number of computations involved, the block mode decision phase is done on the filrst few frames only and filxed for subsequent frames. The decision is based on motion vectors (MVs) found from previous frames. From our results, the block computation time can be reduced as much as 47.72% for a video sequence with low motion activity. On average, most of the test video sequences suffer degradation in PSNR value of less than 0.5dB.

Block Mode Decision Based on Motion Vectors for H.264/AVC

Electroencephalography (EEG) signal is one of the popular approaches for analyzing the relationship between motor movement and the brain activity. This is mainly driven by the rapid development of Brain-Computer-Interface BCI devices for applications like prosthetic devices, using EEG as its input signal. The EEG is known to be highly affected by artefact and with more motor events, this may result in low classification accuracy. In this paper, classification of 3-class hand motor EEG signals, performing grasping, lifting and holding using Common Spatial Pattern (CSP) and pre-trained CNN is investigated. Thirteen electrodes capturing signals related to motor movement, C3, Cz, C4, T3, T4, F7, F3, Fz, F4, F8, P3, Pz and P4 are utilized and signal from \(\alpha \), \(\beta \), \(\varDelta \) and \(\theta \) bands are selected in the pre-processing stage. CSP filters utilizing the scheme of pair-wise are used to increase the discriminative power between two classes whereby the signals extracted by the CSP filter are converted into scalograms by utilizing Continuous Wavelet Transform (CWT). The accuracy of the proposed multi-band and CSP based classification algorithm tested using DenseNet giving average accuracy values of 97.3%, 93.8% and 100%, for GS, LT and HD movements, respectively. These results indicate that the classification framework using CSP filters and pre-trained CNN can provide a good solution in decoding hand motor movement from EEG signals.

Nasreen Badruddin

Papers

A Novel Online Correlation Noise Model Based on Band Coefficients Mean to Achieve Low Computational and Coding-Efficient Distributed Video Codec

An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models

Comparative Study of State-of-the-art Face Landmark Detectors for Eye State Classification in Subjects with Face Occlusion

Block Mode Decision Based on Motion Vectors for H.264/AVC

EEG Motor Classification Using Multi-band Signal and Common Spatial Filter