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

The shortest path routing does not balance the routing load on the links according to their capacity thus some links experience more traffic than they can accommodate, which congests the network Hence there is a need to balance the traffic load on all links according to their capacities By combining physical layer capacity with MAC layer estimated congestion value, a new delay-based load-balancing routing (DLBR) algorithm is proposed in this paper A new routing metric ie the end-to-end delay (EED) is introduced, which estimates the congestion level at the MAC and physical layers The DLBR then uses this metric to select high capacity links for data forwarding, thus providing paths with less congested nodes and high capacity links Simulations have been conducted on MATLAB to study the performance of DLBR Results show that for the same network load, DLBR outperforms the shortest path in terms of the average delay and packet loss

Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks

In Wireless Sensor Network (WSNs), routing protocol is an important aspect to utilize network resources fairly. Normally, routing mechanisms are selfish in nature and are based on a single objective criterion, e.g., hop count. This behaviour is inappropriate for a long lasting communication as it causes performance degradation in the entire network and ultimately leads to network congestion. To enhance network performance, we propose a Co-operative Selection Reactive Routing Protocol (CSRP) that cooperatively selects the End-to-End (ETE) route by considering the traffic load along with the hop count. We compared CSRP with AODV using ns-2.35 in terms of ETE throughput, ETE delay, and Packet Delivery Ratio (PDR). The results reveal that CSRP improves throughput by 20.95%, reduces delay by 18.65% and increases PDR by 56.41%.

A cooperative selection reactive routing protocol for wireless sensor networks

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.

/pdf/drowsiness-detection-using-ocular-indices-from-eeg-signal-gqqr0vb8.pdf

Drowsiness Detection Using Ocular Indices from EEG Signal

This study presents an algorithm that can detect people’s facial features being studied and then applied mainly on daily basis activities, as an example in driving which is detection of driver drowsiness. In this study, the algorithm named ‘PERCLOS’ which stands for ‘percentage of eye closure’ was tested to detect face by using two face landmark detectors, that are pre-trained model and library Dlib’s 68-points facial landmark and 468 3D face landmarks detector from MediaPipe by Google as an alternative and detects the condition of a person’s eye based on Eye Aspect Ratio (EAR). Initial assessment of the Dlib’s solution on 151,537 frames (about 84 minutes) of one of tested subjects revealed that 98.66% of eye states were properly identified, resulting in 378 blinks to be recorded. Despite having rather good accuracy, the algorithm produced 166 more blinks than the 212 blinks that were expected. As for MediaPipe, with 264 blinks and only 52 additional blinks, the MediaPipe Face Mesh solution was able to categorize the identical subject with a classification accuracy of 99.87%. Additionally, adaptive thresholds for different subjects were applied in order to investigate a way to improve the studied algorithm. Surprisingly, the adaptive threshold method being studied resulted in decreasing accuracy and precision for some of the subjects. For one of tested subject, the resulted precision of studied algorithm somehow drops from 100% to 98.60%.

Enhancements to PERCLOS Algorithm for Determining Eye Closures

Excessive growth in electricity consumption has been experienced over the past few years due to an increase in population around the world.This tends to increase the use of renewable energy and randomness of the load. So.it is important to improve the traditional methodologies and techniques applied on microgrid to make it more intelligent. In this paper, multi agent system is employed over autonomous microgrid framework to endorse its intelligence. The Multi-Agent system is simulated in Java Agent Development Environment (JADE) environment and matlab toolbox Simulink is used for the implementation of the microgrid model. Further, MACSimJX is used to communicate between the micro grid and agent system. This paper shows the communication between the agents and the microgrid model and how they process the data through MACSimJX to make intelligent decisions.

Nasreen Badruddin

Papers

Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks

A cooperative selection reactive routing protocol for wireless sensor networks

Drowsiness Detection Using Ocular Indices from EEG Signal

Enhancements to PERCLOS Algorithm for Determining Eye Closures

Multi-Agent Based Energy Management in Microgrids Using MACSimJX