Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using various neural networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work. Then an online algorithm for movement identification and classification that utilises the trained neural networks is presented

The signal-to-noise ratio (SNR) is one of the most significant measures of performance of the sigma-delta modulators. An approximate formula for calculation of signal-to-noise ratio of an arbitrary sigma-delta modulator (SDM) has been proposed. Our approach for signal-to-noise ratio computation does not require modulator modeling and simulation. The proposed formula is compared with SNR calculations based on output bitstream obtained by simulations, and the reasons for small discrepancies are explained. The proposed approach is suitable for fast and precise signal-to-noise ratio computation. It is very useful in the modulator design stage, where multiple performance estimates are required.

Memristors are state-of-the-art electronic elements with nano sizes, about 3 nm dimensions, with very good nano-second switching and memory properties, low power usage of about 100 µW, and good compatibility with the current technology of CMOS-integrated chips and circuits. These components are potentially applicable in T-byte memory arrays, artificial neural networks, logic gates and many other digital and analog electronic schemes and devices for artificial intelligence. This paper presents the application of some simple and fast-operating modified memristor models with activation thresholds in neural networks and logic circuits. MATLAB ver. 2016a and LTSPICE ver. XVII products are used for the analysis of memristor neural nets and logical circuits for artificial intelligence. Several simple, accurate and fast-operating existing modified memristor models, together with several frequently used standard memristor models, are utilized for the associated analyses and simulations. A comparison between the used memristor models is conducted. The considered memristor models are tuned, using experimentally recorded i-v relations of tungsten-sulfide Knowm memristors. An accurate functioning of the analyzed neural nets and logic functions is confirmed by the derived results. The considered modified memristor models, neural networks and logic schemes are important in modeling and analysis of memristor-based circuits for ultra-high-density artificial intelligence-integrated chips.

The transition from internal combustion engine vehicles toward hybrid electric vehicles and battery electric vehicles has transformed not only automotive engineering but also the sensory and emotional experience of driving. While previous studies have examined environmental and performance differences between propulsion systems, limited researches investigated their direct impact on human neurophysiology and emotional perception during real-world driving. This study investigates the neurophysiological and autonomic responses of drivers exposed to three vehicle categories: electric vehicles, internal combustion engine vehicles, and hybrid electric vehicles. A standardized driving sessions were performed in urban driving environment, while electroencephalography, heart rate, heart rate variability, galvanic skin response and peripheral blood oxygen saturation were continuously recorded. Measurements were collected during three phases: a five-minute pre-driving baseline, twenty minutes of active driving, and a five-minute post-driving recovery period. Electroencephalography power in the theta (4–8 Hz), alpha (8–12 Hz), low beta (12–20 Hz), and high beta (20–30 Hz) frequency bands were analyzed as indicators of cognitive workload, cortical relaxation, and attentional engagement. Cardiovascular and electrodermal signals were interpreted as markers of autonomic arousal, sympathetic activation, and stress regulation. Peripheral oxygen saturation was included as complementary index of cardiorespiratory and metabolic demand across vehicle conditions.

Circular economy implementation is feasible through digital tracking of raw materials. The aim of this article is to analyze the possibilities for deploying blockchain or Ethereum as Distributed Ledger Technology (DLT) platforms. Various decision-selection algorithms were examined for choosing a suitable platform and network type (public, private). The article analyses the drawbacks and barriers to achieving a circular economy in accordance with sustainable development criteria. Based on the regulatory framework and operational projects, it is shown that digital technologies improve the efficiency of raw material processing and life cycle tracking. To build an effective network, a separator is proposed as a separate participant in addition to producers, processors, and consumers. Their functions are waste classification and sorting, and receiving and transmitting heterogeneous data streams while preserving the privacy of sensitive ones. Drawing on the specifics of circular economy implementation, selection criteria are proposed, including deployment cost, data immutability, energy consumption, need for new technology, and others. A superior decision support tree tailored to material recycling is proposed. Its advantage is fast decision-making and selection of an appropriate platform within the DLT landscape. The proposed conceptual framework is not a universal solution as it can be adapted to different recycling schemes, especially when the regulatory framework, technologies, materials, flows, scheme participants, stakeholders, or other factors change.