Motor imagery-based brain-computer interface by implementing a frequency band selection

Les interfícies cervell-ordinador basades en imaginacions motores (MI-BCI) són una promesa per a revolucionar la manera com els humans interactuen amb les màquines o el programari, realitzant accions només amb el pensament. Els pacients que pateixen discapacitats de moviment crítiques, com l'esclerosi lateral amiotròfica (ALS) o la tetraplegia, podrien utilitzar aquesta tecnologia per controlar una cadira de rodes, pròtesis robòtiques o qualsevol altre dispositiu que els permeti interactuar de manera independent amb el seu entorn. L'objectiu d'aquest projecte és ajudar les comunitats afectades per aquests trastorns amb el desenvolupament d'un mètode que sigui capaç de detectar, amb la màxima precisió possible, la intenció d'executar moviments (sense que es produeixin) en les extremitats superiors del cos. Això es farà mitjançant senyals adquirits amb un electroencefalograma (EEG), el seu condicionament i processament, i la seva posterior classificació amb models d'intel·ligència artificial. A més, es dissenyarà un filtre de senyal digital per mantenir les bandes de freqüència més característiques de cada individu i augmentar significativament l’exactitud del sistema. Després d'extreure les característiques estadístiques, freqüencials i espacials més discriminatòries, va ser possible obtenir una exactitud del 88% en les dades de validació a l'hora de detectar si un participant estava imaginant un moviment de la mà esquerra o de la dreta. A més, es va utilitzar una xarxa neuronal convolucional (CNN) per distingir si el participant estava imaginant un moviment o no, la qual cosa va aconseguir una exactitud del 78% i una precisió del 90%. Aquests resultats es verificaran mitjançant la implementació d'una simulació en temps real amb l'ús d'un braç robòtic.Las interfaces cerebro-computadora basadas en imaginaciones motoras (MI-BCI) son una promesa para revolucionar la forma en que los humanos interactúan con las máquinas o el software, realizando acciones con tan solo pensar en ellas. Los pacientes que sufren discapacidades críticas del movimiento, como la esclerosis lateral amiotrófica (ALS) o la tetraplejia, podrían usar esta tecnología para controlar una silla de ruedas, prótesis robóticas o cualquier otro dispositivo que les permita interactuar de manera independiente con su entorno. El objetivo de este proyecto es ayudar a las comunidades afectadas por estos trastornos con el desarrollo de un método que sea capaz de detectar, con la mayor precisión posible, la intención de ejecutar movimientos (sin que se produzcan) en las extremidades superiores del cuerpo. Esto se hará mediante señales adquiridas con un electroencefalograma (EEG), su acondicionamiento y procesamiento, y su posterior clasificación con modelos de inteligencia artificial. Además, se diseñará un filtro de señal digital para mantener las bandas de frecuencia más características de cada individuo y aumentar significativamente la exactitud del sistema. Después de extraer características estadísticas, frecuenciales y espaciales discriminatorias, fue posible obtener una exactitud del 88% en los datos de validación a la hora de detectar si un participante estaba imaginando un movimiento con la mano izquierda o con la derecha. Además, se utilizó una red neural convolucional (CNN) para distinguir si el participante estaba imaginando un movimiento o no, lo que logró un 78% de exactitud y un 90% de precisión. Estos resultados se verificarán implementando una simulación en tiempo real con el uso de un brazo robótico.Motor Imagery-based Brain-Computer Interfaces (MI-BCI) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to control a wheelchair, robotic prostheses, or any other device that could let them interact independently with their surroundings. The focus of this project is to aid communities affected by these disorders with the development of a method that is capable of detecting, as accurately as possible, the intention to execute movements (without them occurring) in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a Convolutional Neural Network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved a 78% accuracy and a 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm

Optical Investigations of CdSe1-x Tex Thin Films

The alloys of CdSe1-xTex compound have been prepared from their elements successfully with high purity (99.9999%) which mixed stoichiometry ratio (x=0.0, 0.25, 0.5, 0.75 and 1.0) of (Cd, Se and Te) elements. Films of CdSe1-xTex alloys for different values of composition with thickness(0.5?m) have been prepared by thermal evaporation method at cleaned glass substrates which heated at (473K) under very low pressure (4×10-5mbar) at rate of deposition (3A?/s), after that thin films have been heat treated under low pressure (10-2mbar) at (523K) for two hours. The optical studies revealed that the absorption coefficient (?) is fairly high. It is found that the electronic transitions in the fundamental absorption edge tend to be allowed direct transition. It was also found that the optical energy gap vary non-linearly with composition (x) and have a minimum value at x=0.5 and increases after heat treatment. It is found that the optical constants vary non-linearly with composition, and the behavior inverse at x=0.5, and affected by heat treatment. The behavior of ?1 is similar to the behavior of n, while the behavior of ?2 is similar to the behavior of k

Ultra-violet spectra studies of photodegradation of PVC films in presence of Fe(III) chelate complex

A complex of Fe(III) with 4-amino-5-(pyridyl)-4H-1,2,4-triazole-3-thiol was prepared and evaluated as a photodegradation for rigid poly(vinyl chloride) (PVC). Polyvinyl chloride dissolved with Fe(III) complex in THF solvent to form PVC films of 5% (40 μm) thickness containing different concentrations (0.01, 0.02, 0.03, 0.04 and 0.05 g) of the complex by weight. These different samples were produced by the casting method from the solvent. The photodegradation of films was investigated using UV-visible spectra. The photostabilization activity of these compounds was determined by calculating the photodecomposition rate constant (Kd) for modified PVC films against a blank

Realization of Autonomous Sensor Networks with AI based Self-reconfiguration and Optimal Data Transmission Algorithms in Resource Constrained Nodes

149-158Wireless sensor networks (WSN) prove to be an enabling technology for Industry 4.0 for their ability to perform in autonomous manner even in regions of extreme conditions. Autonomy brings in independent decision making and exerting controls without manual intervention and frequent maintenance. This paper aims to inculcate intelligence to the WSN exploiting the merits of Artificial Intelligence (AI) algorithms in cheap and most preferred ESP8266 and ESP32 based nodes. Autonomy is brought in by means of optimal data transmission, compressive sensing fault detection and network reconfiguration and energy efficiency. Optimal data transmission is achieved using Q-learning based exploration exploitation algorithm. Compressive sensing performed using Autoencoders ensure reduction in transmission overhead. Fault detection is done using Binary SVM classifier and the network re-configures based on physical redundancy. This paper highlights the implementation of such autonomous WSN in real time along with their performance statistics

Enhanced Road Network to Reduce the Effect of (External – External) Freight Trips on Traffic Flow

The transportation system is often described as the lifeblood of modern society. Roads constitute a fundamental part of this system for both passenger and freight transports, a well-functioning freight transportation system is an essential element in any successful economy. Hilla is one of the most densely populated cities in Iraq. The road network in Hilla city is under additional load due to (external - external) trips, especially freight trips by trucks passing through the city's main entrances to cross into neighboring districts and provinces. This is due to the city's strategic location, which connects Baghdad with the southern provinces, making it an important transit route. The objective of this research is to study a proposal for modifying and developing the road network in the city of Hilla by adding new roads to the current network in order to reduce the negative impact of freight trucks passing through the city, especially (external - external) trips, by using Trans CAD and ArcGIS software network analysis. The result of network analysis shows that the suggested roads will reduce the total (travel time and distance) for the same origin and destination points by 9%, and 30%, compared with the current distance and time, respectively, while improving the level of service from D to C at peak hours for freight vehicles. Doi: 10.28991/CEJ-2022-08-11-015 Full Text: PD

Prevalence of Helicobacter Pylori in Type II Diabetes Mellitus

Objective: To determine the prevalence of Helicobacter pylori infection in type 2 diabetes and non-diabetes patients.Methodology: This case control study was conducted in Microbiology Department, Basic Medical Sciences Institute, Jinnah Postgraduate Medical Centre Karachi with collaboration of diabetic clinic of medical and gastroenterology OPD, from June 2019 to November 2019.  Known cases of type 2 diabetes mellitus, irrespective of gender, above 30 years of age and in equal number non-diabetic patients with history of epigastric burning, epigastric pain, belching, bloating, nausea, vomiting for >1 month were included. Three ml venous blood was taken from antecubital vein after overnight fasting of 8-12 hours for blood sugar fasting. Patients underwent H. pylori Antigen Rapid Test Cassette (Stool). All the data was recorded on self-made proforma. Data was analyzed by using SPSS version 20.Results: In diabetic group most of the cases 35.2% were more than 60 years old and in non-diabetic group majority of the cases 44.8% were <50 years. Females were most common in both diabetes non-diabetes groups. Total of 83(79%) of diabetic group and 54 (51.4%) of non-diabetic were labeled as positive for H. pylori infection (p=0.001). Diabetic patients >50 years of age, were significantly associated with h-pylori infection, (p-0.001), while there was no significant impact found of gender on H-pylori infection (p-0.330).Conclusion: H. pylori infection in diabetic patients was higher as compared to non-diabetes. Effectiveness of stool antigen method is the best diagnostic tool for the detection of H. pylori infection in diabetic subjects

Intellectual Implications of the Duality of Mother and Child in the Social Perspective

The artistic subject includes relationships derived from the social fabric as the artist is an important part of Society, as well as his ability to express and influence Society through an aesthetic discourse within a dialogue outside the traditional meaning. From this, the focus has been on the intellectual connotations of the mother-child duality, as the forms carry intellectual implications. Through which the aesthetic meaning is highlighted, the current research focuses on studying (the intellectual implications of the mother-child duality in contemporary sculpture). It included presenting the research problem, which centered on the following question: What is the nature of the intellectual messages of the mother-child duality in contemporary sculpture. The importance of the research comes from social treatments and from adopting the mother-child duality as an intellectual and critical window. At the same time, the research raises the issue of mother and child with the Iraqi sculptor in the future. The current research aims to reveal the intellectual implications of the sculptural works that carry the content of the mother-child duality. The works of European and non- European sculptors naturalized in Europe were limited to the period ranging (2000-2022) based on the descriptive-analytical method. It also included the duality of the mother and the child from a social perspective, in which the views of the researcher were reviewed with the opinions of sociologists and psychologists on the subject, highlighting the differences and agreement among them. Shedding light on the intellectual and semantic concept of the duality of mother and child, in addition to focusing on the duality of mother and child in contemporary European sculpture, with a focus on research procedures and through which the researcher presented the research community and the research sample, which is about (20) sculptural works that were deliberately tested within the limits of the research spatial and temporal

Realization of Autonomous Sensor Networks with AI based Self-reconfiguration and Optimal Data Transmission Algorithms in resource constrained nodes

Wireless sensor networks (WSN) prove to be an enabling technology for Industry 4.0 for their ability to perform in autonomous manner even in regions of extreme conditions. Autonomy brings in independent decision making and exerting controls without manual intervention and frequent maintenance. This paper aims to inculcate intelligence to the WSN exploiting the merits of Artificial Intelligence (AI) algorithms in cheap and most preferred ESP8266 and ESP32 based nodes. Autonomy is brought in by means of optimal data transmission, compressive sensing fault detection and network reconfiguration and energy efficiency. Optimal data transmission is achieved using Q-learning based exploration exploitation algorithm. Compressive sensing performed using Autoencoders ensure reduction in transmission overhead. Fault detection is done using Binary SVM classifier and the net- work re-configures based on physical redundancy. This paper high- lights the implementation of such autonomous WSN in real time along with their performance statistics

Perforin detection as specific tumor marker for UrothelialCancer inPatients

Perforin″PRF1″ is a fenestrae-framing peptide whichhas the capacity of ″toxic lymphocytes″, which slaughter changed cells as well as cells harboring intracellular pathogens(Voskoboinik and Trapani 2006).These lymphocytes traverse both the intrinsic and versatile safe compartments, and include ″toxic T lymphocytes″, regular executioner killer cells, Natural killercells.Thesecytes can release ″PRF1″continuously sending warning signals.″PRF1″ is placed in the cellular parts that are released in a cellular way to empty their contents, performing their role as target cells (Voskoboiniket.al 2010)

Does Carbon Emissions, and Economic Expansion Induce Health Expenditure in China: Evidence for Sustainability Perspective

The current paper assesses the drivers of health care expenditure such as urbanization, natural resources, economic expansion, and CO2 utilizing quarterly data from 2000Q1 to 2018Q4. The research applied the novel dual adjustment approach to identify the long run association between healthcare expenditure and urbanization, economic growth, natural resource and CO2 emissions. The main novelty of the dual adjustment approach is that the approach offers another way to cointegration analysis by relaxing the implicit assumption of the singular adjustment in cointegration analysis. The outcome of the dual adjustment approach affirmed cointegration among the variables in the long run. Furthermore, we applied fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegrating regression (CCR) estimators and their results disclosed that economic growth, urbanization, and CO2 emissions increase health care expenditure while natural resource rent mitigates healthcare expenditure in China. Moreover, the spectral causality test uncovered that urbanization, economic growth, natural resource, and CO2 emissions can predict healthcare expenditure at various frequencies. Based on these findings, China’s policymakers should establish strategic environmental management policies that improve healthy and clean air to reduce healthcare costs. In addition, policymakers in China should reevaluate their urban development strategies to avoid negative externalities associated with fast urbanization. Copyright © 2022 Xiu, Ameer, Abbas and Altuntaş