Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.

Ahstract-In recent years, alternate energy sources has become essential as the demand for power increases. In the last energy decades energy obtained from external sources such as thermal energy, solar power, wind energy, and RF energy has been in use for various purposes. To provide unlimited energy for the lifespan of electronic devices, energy harvesting uses inexhaustible sources with no adverse environmental effect. This paper focuses on RF energy harvesting. The receiving antenna captures the RF energy from surrounding sources, such as nearby mobile phones, wireless LANs (WLANs), FMlAM radio signals, broadcast television signals and rectified into a usable DC voltage. One possibility to overcome their power limitations is to extract (harvest) energy from the environment to either recharge a battery, or even to directly power the electronic device. To meet various objectives in last few years, several antenna designs of rectenna have been proposed for use in RF energy harvesting,. Among the other antennas, microstrip patch antennas are widely used because of their low profile, light weight, and planar structure.This paper presents several methods to design and energy harvesting device depending on the type of energy available and also presents microstrip path structural rectenna are explains RF signal harvester for powering low consumption electrical devices. IndexTerms-Low power circuits, Microstrip patch structured rectenna, RF Energy harvesting

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A series of substituted phenyl methyl piperazine triazolyl benzotriazoles 4a, 4b, 4c, 4d, 4e, 4f, 4g have been synthesized through the Mannich reaction of substituted phenyl triazolyl benzotriazoles 3a, 3b, 3c, 3d, 3e, 3f, 3g. The substituted phenyl triazolyl benzotriazoles were prepared from benzotriazolyl acetohydrazide, where the cyclization was facilitated through ammonium acetate and aryl aldehydes. The IR, 1H NMR, mass spectral data and elemental analysis were performed to assign the structure. All the newly synthesized compounds were screened for their antimicrobial and antioxidant activity.