We have known that in our eyes and ears, to the external stimulations there exist many cells that are respectively most sensitive to some specific center frequencies, and this paper has  discussed that the neuron system utilizes the transient response zone to represent different stimulation intensity levels, therefore we can generally use the Gaussian radial basis function model to explain that the neuron system is how to analyze the sound and color in the associated primary layer, and also can better explain some phenomena in the color cognitive behaviors. In addition, we have known that in the cerebral cortex, many small neuron groups are respectively sensitive to the different arrangement directions of line segments, and we find that the neuron system can further  extract the high-order information of a curve-line segment at the corresponding position, then as introduced later, the vison neuron system can conveniently represent a curve-line segment with an attribute-set constructed by using the polynomial radial basis function model related to the Taylor series expansion approach.

Time-division-multiplexing clocked-analog low dropout regulator (TDM CLDO) is a new multi-channel LDO architecture that is able to reduce the total chip area by analog hardware multiplexing. It is particularly suitable for thermo-optic tuning in silicon photonics that has a low tuning power and requires a small power supply chip area. However, previous designs require a dedicated compensation capacitor for each channel. This is because, during controller switching, one node of the compensation capacitor experiences large voltage changes that prevent reliable multiplexing of the capacitor. Multiplexing the compensation capacitor is required to further reduce chip area. This paper presents a four-channel TDM CLDO using a novel Current Miller compensation with a Degenerated Resistor (CMDR) block. This CDMR block is disconnected from voltage nodes with large changes, therefore enabling reliable capacitor multiplexing. In addition, Gray-code addressing technique is adopted to reduce the crosstalk between four channels caused by clock timing errors. Implemented in a 65 nm CMOS process, this design has a total chip area of 0.0047 mm2, which is 55% smaller than the conventional solution with four identical LDOs and consumes a quiescent current of 3.1 uA per channel. Measured results show that the presented four-channel TDM CLDO can simultaneously track four 1 Vpp sinusoidal signals up to 200 kHz.

Creating machines that are conscious is a long term objective of research in artificial intelligence. This paper look at this idea with new arguments from physics and logic.  Observers have no place in classical physics, and although they play a role in measurement in quantum physics there is no explanation for their emergence within the framework. There is suggestion that consciousness, which is implicitly a property of the observer, is a consequence of the complexity of specific brain structures, but this is problematic because one associates free will with consciousness, which goes counter to causal closure of physics. Considering a nested physical system, we argue that even if the system were assumed to have agency, observers cannot exist within it. Since complex systems can be viewed in nested hierarchies, this constitutes a proof against consciousness as a product of complexity, for then we will have nested system of conscious agents. As the existence of consciousness in cognitive agents cannot be denied, the implication is that consciousness belongs to a dimension that is not physical and machine consciousness is unattainable. These ideas are used to take a fresh look at two well-known paradoxes of quantum theory that are important in quantum information theory.

This study presents a state non-parametric identifier based on neural networks with continuous dynamics, also known as differential (namely DNNs) whose laws for adjusting their parameters are developed using a control Barrier Lyapunov Functions (BLFs). The motivation for using the BLF comes from the preliminary information of the system states, which remain in a predefined time-depending set characterized by state or purely time-dependent functions. In this study, time-dependent state constraints are supposed to be known in advance of continuous-time functions. The obtained learning laws require solving differential continuous-time Riccati equations and nonlinear differentialequations for the learning laws that depend on the identification error and the state restrictions. The developed identifier was evaluated concerning the identifier that does not consider the state restrictions. This comparison included the numerical evaluation of the identifier considering its application on a robotic arm that is intended to reproduce a non-standard flight simulator. This evaluation confirmed that the identification results were improved using the proposed learning laws considering that the state limits were not transgressed, and the quality indicators based on the mean square error were more minor by 4.2 times.

Recent studies have focused on developing advanced assistive devices to help blind or visually impaired people. Navigation is challenging for this community; however, developing a simple yet reliable navigation system is still an unmet need. This study targets the navigation problem and proposes a wearable assistive system. We developed a smart glove and shoe set based on radio-frequency identification technology to assist visually impaired people with navigation and orientation in indoor environments. The system enables the user to find the directions through audio feedback. To evaluate the device’s performance, we designed a simple experimental setup. The proposed system has a simple structure and can be personalized according to the user’s requirements. The results identified that the platform is reliable, power efficient, and accurate enough for indoor navigation.

Driver Drowsiness is one of the leading causes of road accidents. Electroencephalography (EEG) is highly affected by drowsiness; hence, EEG-based methods detect drowsiness with the highest accuracy. Developments in manufacturing dry electrodes and headsets have made recording EEG more convenient. Vehicle-based features used for detecting drowsiness are easy to capture but do not have the best performance. In this paper, we investigated the performance of EEG signals recorded in 4 channels with commercial headsets against the vehicle-based technique in drowsiness detection. We recorded EEG signals of 50 volunteers driving a simulator in drowsy and alert states by commercial devices. The observer rating of drowsiness method was used to determine the drowsiness level of the subjects. The meaningful separation of vehicle-based features, recorded by the simulator, and EEG-based features of the two states of drowsiness and alertness have been investigated. The comparison results indicated that the EEG-based features are separated with lower p-values than the vehicle-based ones in the two states. It is concluded that EEG headsets can be feasible alternatives with better performance compared to vehicle-based methods for detecting drowsiness.

We investigate transceiver design and digital signal processing for spatially multiplexed transmission over multimode fibers. In conventional architectures, the full spatial domain of the transmission fiber has to be detected and processed such that the modal walk-off and mixture can be estimated and equalized. These architectures scale poorly with the number of modes supported, besides the sparsity of the fiber transfer matrix is not fully exploited. Instead, here we aim to employ selective mode vector launch and detection in order to minimize the number of optical front-ends required. In this case, an ideal basis for multiplexing is offered by principal modes, that to first order are frequency independent. We show that such mode vector basis can be used for full baud rate transmission over inter-data center distances despite limited coherence bandwidth and vulnerability to environmental-induced drift of the optical channel. It is shown that crosstalk at the receiver front-end can be significantly suppressed, critically reducing the number of coherent receiver front-ends to that of spatial tributaries aimed for data transmission -- as opposed to the total number of fiber modes. Residual crosstalk can still be experienced due to environmental-induced channel drift and loss of orthogonality in presence of mode dependent loss. Multiple-input single-output digital signal processing is shown to be effective in this case, with the required equalizer array size scaling sub-linearly with the number of tributaries. A multimode fiber with 156 spatial and polarization modes and optimized for low modal dispersion is considered.

Optimizing the emission properties of materials in ultraviolet and deep blue (UV-DB) is interesting in development of new scintillator devices for the detection of X-ray, γ-ray and radiation particles as those materials can be strong candidates for high light yield and fast scintillators. While their intrinsic material properties are already well studied, photonic enhancement generated through optical confinement could significantly improve their emission characteristics, however one needs to overcome the problem of relatively low refractive indices contrast resulting in poor confinement of UV-DB light. This motivates the search for resonator structures built from readily accessible materials that can boast strong confinement in this spectral regime. Here, we present such a structure, leveraging bound states in the continuum (BICs) to realize large-area confinement of UV-DB light with ultra-high quality factors up to Q ∼107. These ultra-high Q-factors in turn result in strong enhancements in light emission via the Purcell effect. We demonstrate the operation of such a design by simulating the mode shape, Q factor and emission behaviour in organic, hybrid perovskite and III-V scintillating materials. By tailoring the structure geometry, it can be robustly tuned to match the emission characteristics of chosen materials. We start with considering ideal infinite structure supporting perfect BIC and we extend our model on finite sized structures and we discuss the limitations associated with the self-absorption and thickness of the structure. Our findings pave the way to cost-effective and efficient designs for scintillators in the UV-DB regime (submitted to IEEE Transactions on Nuclear Sciences).

We present several calculations for the DC inductance of planar polygonal nonuniform spiral inductors, including common layout shapes of square, hexagon, octagon, and circle. The term “nonuniform” is a novel classification proposed in this paper to characterize the turn-to-turn variable metal width inductors. We take three extreme cases into consideration, according to the spiral coil from inside to outside: type 1 with the increasing width, type 2 with the fixed width and space (conventional inductors), and type 3 with the decreasing width. Five unified calculations are first proposed for the conventional inductors (type 2 inductors), which are only layout dependent with different coefficients, and then we successfully further three of the five calculations, with a simple modification on some calculation parameters, for all nonuniform types. Fractional inductors are treated as “integer plus fraction” parts, both of which can be easily determined by derived calculations. Automatic scripts are employed to generate over a million inductor layouts for electromagnetic (EM) simulation. Our calculations match the EM inductance values typically within around 3%. Comparison with both measured and published experimental results also shows good agreement.

Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data–obtained through a Supervisory Control And Data Acquisition (SCADA) system–are used to train an Artificial Neural Network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility’s Extra-High Voltage transmission network and over 91% for all forty-two substations. The trained model is tested in a manner in which it would be used through the use of using simulated data generated by state estimation software. Using this simulated data validates the model’s capacity to predict the substation buses that would experience voltage unbalance.

In this paper, we exemplify a new mathematical theory and method for data prediction, interpolation and acquisition, called “high efficiency sensing.” Compressed sensing theory is the most sensational topic of scientific research in the past century. The original paper was unprecedentedly cited over 30,000 times in only 15 years. Compressed sensing purports to exert data compression at the phase of data acquisition, but turns out to massively lose data quality. Based on insight into flaws of compressed sensing theory and crucial discovery of a series of new logical phenomena, our high efficiency sensing method radically rectifies mathematical rationale and immensely improves technical performance in vast dominance. The core method is simple yet powerful. It runs hundreds of thousand times faster in computation. Even better, it duplicates data information in various domains altogether while boosting data dimension. In consequence, prediction and interpolation conform to the waveform of samples without loss or distortion. The deep copy ensures the high quality of resulting data set. Demo software and test data are downloadable at website www.lucidsee.ca.

Hairpin winding technology, combined with direct oil jet impingement cooling, is a viable solution known to increase volumetric power density and efficiency in the next generation of traction motors. However, the coolant fluid interaction with the complex winding geometry has not yet been fully examined; specifically, with the use of high-fidelity CFD simulations. Thus, the present work investigates the radial and axial oil impinging jets in a hairpin winding motor using multi-phase simulation. The study first analyses power losses and temperature distribution of the motor under the Worldwide Harmonised Light Vehicle Test Procedure (WLTP). Next, the performance of axial and radial jet impingement is numerically analysed by considering the fluid flow. Finally, the two configurations are compared in terms of their oil film formation rate. The results indicate that the maximum power losses observed in typical driving conditions are considerably lower than the maximum losses predicted for the complete operational region of the motor. Moreover, the axial impinging jet shows a higher oil film formation rate compared to a radial jet impingement configuration within the examined conditions. Manuscript accepted to publication in WEMDCD 2023 IEEE conference. © 20XX IEEE.  Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Modern medical device solutions involving hardware, algorithms, and data storage are typically vertically integrated and maintained by single entities. These monolithic systems force patients to consider trade-offs between hardware reliability, software quality, and storage costs. However, recent blockchain technology has created an opportunity for decentralization and horizontal integration of these solution components. We have designed and simulated a medical blockchain system that encourages decentralization, facilitates immutable medical transactions, and provides fast and reliable identity verification. Immutable medical transactions create a trusted history, viewable by insurance providers, and promote integrity from all acting parties. By leveraging reliable identity verification, patients, physicians, and service providers can confidently initiate and accept transactions. We developed our system with a private blockchain based on Ethereum.

It is shown that fractal dimensions may be reasonably estimated in an evolving system when further information is not available by considering the growth in the number of cells in two steps. This may be of use in the estimation of the dimensionality of metamaterials as well as in other natural phenomena. In contrast to algorithm-generated patterns for which a time step is well defined, natural systems evolve in an active space that leads one to the optimal information dimension of e, and this provides a novel perspective on self-similar behavior in natural systems.

In recent times, digital locks have become increasingly popular for securing homes and valuables. However, these locks can still be vulnerable to picking or disabling by those with the necessary skills or tools. To address this issue, this project proposes the use of blockchain technology and biometrics or smartphones to create a more secure system for home security. By leveraging the decentralized and tamper-proof nature of blockchain, combined with biometric identification or smartphone authentication and using the power of IOT technology, homeowners can ensure that their property remains secure and protected from unauthorized access. This project offers a novel solution to the problem of digital lock vulnerabilities, with potential applications in the fields of home security and asset protection.

ChatGPT, a language model developed by OpenAI, has triggered a new wave of AI development. This article reviews the principles of ChatGPT, analyzes five types of robot perception related to human perception (visual, auditory, tactile, olfactory, and gustatory), and seven types of robot intelligence (logical-mathematical, linguistic, spatial, musical, bodily-kinesthetic, interpersonal, and naturalistic), but excludes intrapersonal intelligence as a type of robot intelligence. Human intrapersonal intelligence is related to human self-awareness, personality, biases, and ethics, all of which should be carefully considered in robotic systems. The author proposes a multimodal approach that takes advantage of GPT-3 to implement the seven types of robot intelligence and provides a framework for robot GPT. No data is applied in this paper, except the references.

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi- nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 μA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x; ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (V TH); iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution; iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enable

This paper presents a design and an implementation of an isolated grid connected micro inverter system that is based on an interleaved flyback topology. The developed system gets input from a 250 W PV panel, converts the dc energy into ac and connects it to the grid. This work covers the simulation aspect of developing the above mentioned system where extensive simulations are performed in order to find and optimize values of desired parameters and simulate the overall system. The CCM (Continues Conduction Mode) of operation for the flyback converter is preferred as it generates considerably less amount of the current ripple at converter output when compared to DCM operation and less amount of power loss. A feedback loop with a PI controller is incorporated into the design in order to properly stabilize the system. Simulation results demonstrated that the developed system generates sinusoidal voltage and current signals of 50 Hz in frequency with desired amplitudes at the output of the system. Connection of generated ac waveforms to the grid is accomplished by means of a PLL block. THD of the grid current is found as 11.57% due to spikes at zero crossings.