In This Paper, two different feature extraction methods were studied and their performances in pattern recognition based- P300 detection were compared. These two methods were Common Spatial Pattern (CSP) and intelligent segmentation. Data set II (P300 speller) from the BCI competition 2005 was used. After pre-processing and feature extraction, these features were compared. For this purpose, first, a statistical analysis had been applied for evaluating the fitness of each feature in discriminating between target and non-target signals. Then, each of these two groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Finally in this research, the best result for P300 detection was 95.25% for intelligent segmentation as a feature extraction method. This result shows that intelligent segmentation is better than CSP method for P300 detection.

Multi-channels Electroencephaloram (EEG) needs a long preparation time for electrode installation.Furthermore, using a large number of EEG channels may contain redundant and noisy signals which may deteriorate the performance of the system. Therefore, channels reduction is a necessary step to save preparation time, enhance the user convenience and retain high performance for an EEG-based system. In this study, we present a simple and practical EEG-based emotion recognition system by optimizing the channels number based on two different Common Spatial Pattern (CSP) channel reduction methods. We applied feature extraction based on the Fast Fourier Transform (FFT) algorithm and classification method based on the Support Vector Machine (SVM) and K-nearest neighbor (KNN) which make our proposed system an efficient and easy-to-setup emotion recognition system. According to experimental results, the proposed system using small number of channels not only does not increase the error of the system, but also improves the performance of the system compared to the use of total number of channels.

The classification of emotions using electroencephalography (EEG) signals is inherently challenging due to the intricate nature of brain activity. Overcoming inconsistencies in EEG signals and establishing a universally applicable sentiment analysis model are essential objectives. This study introduces an innovative approach to cross-subject emotion recognition, employing a genetic algorithm (GA) to eliminate non-informative frames. Then, the optimal frames identified by the GA undergo spatial feature extraction using common spatial patterns (CSP) and the logarithm of variance. Subsequently, these features are input into a Transformer network to capture spatial-temporal features, and the emotion classification is executed using a fully connected (FC) layer with a Softmax activation function. Therefore, the innovations of this paper include using a limited number of channels for emotion classification without sacrificing accuracy, selecting optimal signal segments using the GA, and employing the Transformer network for high-accuracy and high-speed classification. The proposed method undergoes evaluation on two publicly accessible datasets, SEED and SEED-V, across two distinct scenarios. Notably, it attains mean accuracy rates of 99.96% and 99.51% in the cross-subject scenario, and 99.93% and 99.43% in the multi-subject scenario for the SEED and SEED-V datasets, respectively. Noteworthy is the outperformance of the proposed method over the state-of-the-art (SOTA) in both scenarios for both datasets, thus underscoring its superior efficacy. Additionally, comparing the accuracy of individual subjects with previous works in cross subject scenario further confirms the superiority of the proposed method for both datasets.

An alarm system has become essential to prevent someone from drowsiness while driving, considering the high incidence due to fatigue or drowsiness. This study offered an alternative to overcome all the limitations provided by the conventional system to detect sleepiness based on the driver’ s brain electrical activity using wearable electroencephalogram (EEG), which is lighter and easy to use. The EEG signals were collected using EMOTIV Epoc + and then were decomposed into narrowband frequency, such as delta, theta, alpha, and beta using DWT. The relative power, as the result of feature extraction, then were processed further by calculating its variance using the common spatial pattern (CSP) method to optimize the accuracy of extreme learning machine (ELM). Comparison of relative power between awake and drowsy state showed that during the drowsy state, theta‑ wave, alpha‑ wave, and beta‑ wave were tend to be higher than in the awake state. However, despite with the help of ELM, the accuracy was not too high (below 87%). The feature extraction which continued by calculating its variance using CSP algorithm before classified by ELM obtained a high accuracy, even with small amount of data training. This showed that CSP combining with ELM could be useful to shorten the time in training/calibration session, yet still, obtained high accuracy in classifying the awake state and drowsy state. The overall average accuracy of testing ranged from 91. 67% to 93. 75%. This study could increase the ability of EEG in detecting drowsiness that is important to prevent the risk caused by driving in a drowsy state.

Finger vein is one of the most fitting biometric for identifying individuals. In this paper a new method for finger vein recognition is proposed. First the veins are extracted from finger vein images by using entropy based thresholding. In finger vein images the veins are appeared as dark lines. The method extracts veins as well, but the images are noisy, that means in addition to the veins they have some short and long lines. Then radon transformation are applied to segmented images. The Radon transform is not sensitive to the noise in the images due to its integral nature, so in comparison with other methods is more resistant to noise. For extracting dominant features from finger vein images, common spatial patterns (CSP) is applied to the blocks of radon transformation. Finally the data classified by using nearest neighbor (1-NN) and multilayer perceptron (MLP) neural network. The research was performed on the Peking University finger vein dataset. Experimental results show that 1-NN using CSP, with detecting rate 99.6753%, against MLP is most appropriate for finger vein recognition.

Today, the communities' security has linked with the military, livelihood, environmental, social and cultural aspects and sustainable security cannot be achieved without providing valid and incomegenerating employment. The societies that look at sustainable security and economic sustainability as a strategic mission, they follow the identifying, strengthening and developing the potential of human, organizational, spatial and location capacities to realize sustainable employment. CSP implementation in rural areas is one of the international mechanisms for the realization of sustainable economy and environment with partnership, spatial planning and regional approach which is implementing in recent years in rural areas of Kerman in southern Iran, including Jazmurian. The aim of this study is to analyze the performance of CSP Jazmurianin about employment and presenting a desirable model for the success of such programs. The researchmethodology is descriptive – analytical one and the data were gathered through library and field methods and by observation and questionnaires. The sample society is 8 villages under the implementation of the CSP and 60 people of development heads are the member of CSP. The method for data analysis is the use of Anova test and Tone example in SPSS environment. The results of the research showed that in most of the villages, the rate of successful employment was less than 50 percent. Results of Anova test showed that there is a significant difference between the quad policies in the field of employment and the T test results showed that the success of education policy-based jobs are higher than the other ones. Based on the research findings, the education based policy and then space-based have been more effective in creating jobs in comparing with organization based and experience based policies. Presenting an optimal pattern requires that the strength points to be focused and its weak points to be removed. The optimal model of employment and the rural economy in addition to observe the process, integrity, and partnership should be based on the reality of the human being and infrastructure and local circumstances, standards, spatial location and pattern of the residents of each village.

Background: Mild cognitive impairment (MCI) is identified as the initial stage of Alzheimer's disease. This condition presents less severe symptoms compared to Alzheimer's Disease (AD) to the extent that it does not significantly impact daily activities. Due to its subtle symptoms, diagnosing MCI is considerably more challenging than diagnosing Alzheimer's. However, early detection of MCI enhances the chances of treatment and prevention of its progression to Alzheimer's and dementia. Methods: This study introduced a novel method for diagnosing MCI using an automated signal processing approach for electroencephalogram (EEG) signals. The method employs advanced signal processing techniques, including discrete wavelet transform in preprocessing and wavelet packet decomposition alongside spatial-spectral filters for feature extraction from EEG signals. EEG signals from 29 patients and 32 healthy individuals were utilized in this study. Findings: The proposed method achieved a classification accuracy of 100% using a random subsampling validation approach. Wavelet packet decomposition effectively isolated frequency sub-bands within the EEG signals, enabling precise extraction. Furthermore, feature extraction using features extracted by the filter bank common spatial pattern (FBCSP) contributed to the increased classification accuracy of the two groups. Conclusion: This study introduces a novel approach for MCI diagnosis by extracting spatial-spectral features from frequency sub-bands of EEG signals obtained through wavelet packet decomposition. The findings underscore the significance of wavelet packet decomposition in separating frequency sub-bands and applying a common spatial pattern filter on these sub-bands for effective feature extraction in distinguishing healthy individuals from those with MCI.

One of the most fitting biometric for identifying individuals is finger veins. In this paper, we study the human recognition via finger vein images that recognize persons at a high level of accuracy. First we use entropy based thresholding for segmentation and extraction veins from finger vein images. The method extract veins as well, but the images are very noisy. That means in addition to the veins that appeared as dark lines, they have some Intersecting lines. Then we applied radon transformation to segmented images. The radon transform is not sensitive to the noise in the images due to its integral nature, so in comparison with other methods is more resistant to noise. This transform does not require the extraction of vein lines accurately, that can help to increase accuracy and speed. Then for extracting features from finger vein images, common spatial patterns are appliedto the blocks of Radon Transform. In identification step two methods are used: Nearest Neighbor (1-NN) and Artificial Neural Network (MLP). Experiments conducted on sets of finger vein image database of Peking University show 99.6753 percent success rate in identifying individuals.

In this paper, a Dish-Stirling Concentrating Solar Power (CSP) system was examined in which the engine works no longer as a receiver but is displaced away from it. In this arrangement it is necessary to adopt a heat transfer fluid capable of transmitting the useful power from the receiver to the hot spring in the Stirling engine head.Various components of the system need designing and especially the heat exchanger in charge of transferring power to the engine head thanks to the cooling of the fluid.The Dish-Stirling system under study includes a linear piston Stirling engine of 4 kW total rated power (3 kW thermal and 1 kW electric). The minimum temperature for starting of the engine is 190oC, while the maximum is 565oC. There are many innovative aspects of dish Concentrating Solar Power systems with Stirling engine dislocated, for example, the possibility of using an increased number of engines powered by a single greater dish and energy savings in the solar tracking system. The work covers the search for the most suitable fluids for the purpose, risks and benefit evaluation of fluids never usedpreviously in the field of solar concentration. The heat exchanger sizing was carried out examining different geometric configurations. The study was conducted using a computer and setting up thermo-fluid dynamics simulations in the ANSYS 14.5 environment. Finally, the results were tested and validated through a comparison study with empirical correlations found in the literature.