Low-Voltage Ultra-Low-Power Current Conveyor Based on Quasi-Floating Gate Transistors

The field of low-voltage low-power CMOS technology has grown rapidly in recent years; it is an essential prerequisite particularly for portable electronic equipment and implantable medical devices due to its influence on battery lifetime. Recently, significant improvements in implementing circuits working in the low-voltage low-power area have been achieved, but circuit designers face severe challenges when trying to improve or even maintain the circuit performance with reduced supply voltage. In this paper, a low-voltage ultra-low-power current conveyor second generation CCII based on quasi-floating gate transistors is presented. The proposed circuit operates at a very low supply voltage of only ±0.4 V with rail-to-rail voltage swing capability and a total quiescent power consumption of mere 9.5 µW. Further, the proposed circuit is not only able to process the AC signal as it's usual at quasi-floating gate transistors but also the DC which extends the applicability of the proposed circuit. In conclusion, an application example of the current-mode quadrature oscillator is presented. PSpice simulation results using the 0.18 µm TSMC CMOS technology are included to confirm the attractive properties of the proposed circuit

Extended Method of Digital Modulation Recognition and Its Testing

The paper describes a new method for the classification of digital modulations. ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK and 16QAM were chosen for recognition as best known digital modulations used in modern communication technologies. The maximum value of the spectral power density of the normalized-centered instantaneous amplitude of the received signal is used to discriminate between frequency modulations (2FSK, 4FSK and MSK) on one hand and amplitude and phase modulations (ASK, BPSK, QPSK, 8PSK and 16QAM) on the other hand. Then the 2FSK, 4FSK and MSK modulations are classified by means of spectrums. The histograms of the instantaneous phase are used to discriminate between ASK, BPSK, QPSK, 8PSK and 16QAM. The method designed was tested with simulated and measured signals corrupted by white Gaussian noise

Robot for plastic garbage recognition

Waste and related threats are becoming more and more severe problems in environmental security. There is growing attention in waste management globally, both in developing techniques to decrease their quantity and those correlated to their neutralization and commercial use. The basic segregation process of waste due to the type of material is insufficient, as we can reuse only some kinds of plastic. There are difficulties with the effective separation of the different kinds of plastic; therefore, we should develop modern techniques for sorting the plastic fraction. One option is to use deep learning and a convolutional neural network (CNN). The main problem that we considered in this article is creating a method for automatically segregating plastic waste into seven specific subcategories based on the camera image. The technique can be applied to the mobile robot for gathering waste. It would be helpful at the terrain and the sorting plants. The paper presents a 15-layer convolutional neural network capable of recognizing seven plastic materials with good efficiency

Reward-based decision signals in parietal cortex are partially embodied

Recordings in the lateral intraparietal area (LIP) reveal that parietal cortex encodes variables related to spatial decision-making, the selection of desirable targets in space. It has been unclear whether parietal cortex is involved in spatial decision-making in general, or whether specific parietal compartments subserve decisions made using specific actions. To test this, we engaged monkeys (Macaca mulatta) in a reward-based decision task in which they selected a target based on its desirability. The animals' choice behavior in this task followed the molar matching law, and in each trial was governed by the desirability of the choice targets. Critically, animals were instructed to make the choice using one of two actions: eye movements (saccades) and arm movements (reaches). We recorded the discharge activity of neurons in area LIP and the parietal reach region (PRR) of the parietal cortex. In line with previous studies, we found that both LIP and PRR encode a reward-based decision variable, the target desirability. Crucially, the target desirability was encoded in LIP at least twice as strongly when choices were made using saccades compared with reaches. In contrast, PRR encoded target desirability only for reaches and not for saccades. These data suggest that decisions can evolve in dedicated parietal circuits in the context of specific actions. This finding supports the hypothesis of an intentional representation of developing decisions in parietal cortex. Furthermore, the close link between the cognitive (decision-related) and bodily (action-related) processes presents a neural contribution to the theories of embodied cognition

AI-Based Enhancing of the Smart City Residents\u27 Safety

Smart Cities are urban environments that use digital technology and data-driven solutions to improve citizens\u27 efficiency, sustainability, and quality of life, especially using Artificial Intelligence to improve human lives. These methods can also help urban residents in dangerous situations by detecting dangerous situations and securing communication during emergency services notifications. In this paper, we present the security protocol that ensures secure communication during the notification of emergency services. This protocol is secure, lightweight, and scalable. We verified its security using an automated tool that did not detect a protocol attack