Recognition Circuit Research Articles

Asymmetry of neural circuits for word and face recognition in readers of Roman and Arabic script

Asymmetry of neural circuits for word and face recognition in readers of Roman and Arabic script

Field-free multistate spin–orbit torque devices for programmable image edge recognition circuit

The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications.

Prosopagnosie et syndrome de Capgras : deux syndromes en miroir ?

First described in the 19th century in patients with brain lesions, prosopagnosia was named in 1947 by Joachim Bodamer (1910–1985), a German neuropsychiatrist. The term refers to the inability to recognise a familiar face, a rarely isolated symptom. Prosopagnosia is thought to result from brain lesions, predominantly at the ventral right temporo-occipital junction. There are more common cases of developmental prosopagnosia, where no lesion has been identified. Capgras syndrome (CS), described by Joseph Capgras (1873–1950), a psychiatrist, consists of the persistent belief that a close relative, identified, has been replaced by a look-alike. SC, considered a delusional disorder of identification or familiarity, has been described in chronic psychoses, mainly schizophrenia. It is also reported in the course of neurodegenerative diseases (mainly Alzheimer's disease, Lewy body dementia, Parkinson's dementia). Lesion-based forms have recently been described. Prosopagnosia and SC were initially thought to result from mirror-image lesions involving a single factor, one affecting an overt ventral circuit for face recognition (in the case of prosopagnosia), the other a covert dorsal circuit involved in emotional and vegetative responses (in the case of CS). Lesion network mapping has been used in both lesion-related conditions and suggests a more complex figure. Lesions causing prosopagnosia were correlated with one of the main regions associated with face recognition, the right fusiform face area, and anticorrelated with left frontal regions possibly involved in visual attentional control. In lesional forms of CS or other delusional misidentification syndromes, there was a correlation with left retrosplenial cortex, an area associated with familiarity processing, and a correlation with right frontal regions associated with belief evaluation. This finding supports the intervention of two factors in CS, one generating the impaired familiarity associated with perception and the other the genesis and persistence of a delusional idea. The debate on the mechanisms of CS is not settled, especially as it is limited to its lesional forms, but studies using lesion network mapping already mark a more general paradigm shift, with attention shifting from lesions (clinico-anatomical method) to circuit dysfunction.

An enzymatically activated AND-gate DNA logic circuit for tumor cells recognition via multi-microRNAs detection

The DNA-based logic circuit, constructed to mimic biochemical reaction networks, is highly significant in detecting biomarkers at the molecular level. The differences in the expression levels of microRNAs (miRNAs) within different types of cells provide hope for distinguishing cell subtypes. However, reliance on a single miRNA often leads to unreliable results. Herein, we constructed an enzyme-triggered cascade logic circuit based on the AND gate, which is capable of generating corresponding fluorescence signals in the presence of target miRNAs. The introduction of apurinic/apyrimidinic (AP) sites effectively reduces the likelihood of false signal generation. Amplification of the fluorescence signal relies on the catalytic hairpin assembly and the repetitive reuse of the multicomponent nucleic acid enzyme (MNAzyme). We demonstrated that the logic circuit can not only distinguish cancer cells from normal cells but also identify different types of cancer cells. The programmability of the logic circuits and the simplicity of the assay system allow us to modify the functional sequences to recognize different types of biomarkers, thus providing a reference for the identification of various cell subtypes.

Research on the key technology of high-precision waveform synchronization identification and measurement.

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.

Correlated optical convolutional neural network with “quantum speedup”

Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs have been implemented. However, the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here, we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit “quantum speedup” in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNN’s capability to perform the QCNN phase recognition circuit, validating the connection between the COCNN and the QCNN. Furthermore, we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup, which will benefit the information processing in the era of big data.

Scalable DNA recognition circuits based on DNA strand displacement

A scalable recognition circuit was designed and implemented using DNA and eventually a molecular comparator was constructed.

Dynamics of Dual Memristors-based Neuron Circuit for Pattern Recognition

Dynamics of Dual Memristors-based Neuron Circuit for Pattern Recognition

DNA hairpin self-assembly on cell membrane triggered by Dual-aptamer logic circuit for cancer cell recognition and photodynamic therapy

Integrated strategy of cancer diagnosis and therapy have found wide applications. The discovery of cancer biomarkers and corresponding aptamers makes it feasible for accurate identification of cancer cells and precise therapy. Herein, we report a dual aptamer based DNA logic circuit facilitating excellent signal amplification via hybridization chain reaction (HCR) for precise identification of cancer cells and photodynamic therapy. Aptamers Sgc4f and Sgc8c perform logic calculations with cell membrane biomarkers. HCR can only be triggered in the presence of both biomarkers to generate fluorescence amplification. A fluorophore (FAM) was modified at the 3′ end of hairpin H2 to indicate the logical calculation result, and G-quadruplex was extended at the 5′ end of H3 for loading the photosensitizer 5,10,15,20-tetrakis-(N-methyl-4-pyridyl) porphyrin (TMPyP4). Thanks to the intelligent autonomous computing capability of DNA logic circuit and signal amplification, the dual aptamer logic circuit enables accurate identification of target cancer cells and fluorescence imaging. TMPyP4-loaded hairpin endows the circuit with capability of targeting photodynamic therapy. Thus, the DNA logic circuit exhibits promising potential for accurate disease diagnosis and targeted therapy.

Memory association circuit based on memristor for neural networks

Memristors have been favored in artificial intelligence, neural networks, and chaotic circuits, especially in neural synapses because of their unique advantages such as resistance variability, nonvolatile nature, and nanometer size. Benefits such as integration scale and low power consumption contribute toward simulating the biological synaptic function. Compared with memory association circuits using traditional CMOS transistors, memristors will reduce the complexity of the circuit and the power consumption. Therefore, it is greatly promising to use memristors as synapses to construct neural networks to mimic human brain functions. This paper successfully establishes a recognition circuit based on memristors to recognize some characteristics (size, color, shape, and smooth) of fruits, which is a learning function. After a few seconds, the output signal voltage drops, and this is a forgetting function. Through the establishment of a recognition circuit, the neural network and human complex behavior were simulated. This work lays the foundation for further research of human neural networks.

Adaptive image recognition circuit based on CMOS-based memristor

Adaptive image recognition circuit based on CMOS-based memristor

3D CNN Hardware Circuit for Motion Recognition Based on FPGA

In recent years, three-dimensional convolutional neural network (3D CNN) has been widely used in the fields of action recognition and video analysis. The general purpose processors are difficult to achieve efficient and intensive computing, and the deployment of 3D CNN based on FPGA has the advantages of low power consumption, high energy efficiency, and customizability, and has gradually become a hot choice for deploying convolutional neural networks in many embedded scenarios. This paper designs a small 3D convolutional neural network based on the classic 3D convolutional neural network C3D, and uses the general matrix multiplication (GEMM) to map the 3D convolution calculation to the 2D matrix multiplication calculation. The matrix is divided into blocks and transmitted to the FPGA through the AXI bus, and the multiplication operation of the block matrix is realized through a two-dimensional multiply-accumulate array. A System on Chip (SoC) architecture is built on the PYNQ platform, using ARM Cortex-A9 as the process control core, and the calculation of the entire matrix is completed under reasonable block and scheduling on the ARM. The IP core of matrix calculation is designed using High Level Synthesis (HLS), and the corresponding parallel optimization scheme is given. Experiments have verified that the prototype design of the hardware acceleration circuit achieves low power consumption, high energy efficiency, and high precision motion recognition while using less hardware resources.

Multiple target recognition and position identification circuit based on memristor

Traditional target recognition has low precision and the data processing is complicated when recognizing multiple targets. A multiple target recognition and position identification circuit based on memristor is designed in this paper, which can simultaneously realize the recognition for multiple targets with different shapes. The dual recognition is used in the designed recognition circuit, including position identification and size recognition, which will improve the recognition accuracy. Integral holding module and double threshold detection module with variable threshold based on memristor can flexibly construct the circuit according to different requirements, and the application range of the circuit is expanded. The signal processing module is designed in this paper to effectively filter out redundant targets and improve the reliability of the circuit. Different from the existing target recognition technologys, the multiple target recognition circuit based on memristor adopts the method of analog circuit to provide reference for the further development of target recognition.

Multi-class quantum classifiers with tensor network circuits for quantum phase recognition

Hybrid quantum-classical algorithms based on variational circuits are a promising approach to quantum machine learning problems for near-term devices, but the selection of the variational ansatz is an open issue. Recently, tensor network-inspired circuits have been proposed as a natural choice for such ansatz. Their employment on binary classification tasks provided encouraging results. However, their effectiveness on more difficult tasks is still unknown. In particular, the best approach to extend them to multi-class classification problems remains unclear. Here, we present numerical experiments on multi-class classifiers based on tree tensor network and multiscale entanglement renormalization ansatz circuits. We conducted experiments on image classification with the MNIST dataset and on quantum phase recognition with the XXZ model by Cirq and TensorFlow Quantum. In the former case, we reduced the number of classes to four to match the aimed output based on 2 qubits. The quantum data of the XXZ model consist of three classes of ground states prepared by a checkerboard circuit used for the ansatz of the variational quantum eigensolver, corresponding to three distinct quantum phases. Test accuracy turned out to be 59%-93% and 82%-96% respectively, depending on the model architecture and on the type of preprocessing.

Translating Hungarian Drama for the British and the American Stage

Reflecting on my experience of translating contemporary Hungarian theater into English, this paper examines the fluidity of dramatic texts in their original and in translation, and charts collaborations between playwrights, translators and theater-makers. Mindful of the responsibility when working from a “minor” to a “major” language, the paper signals the discrepancy between the indigenous and foreign ‘recognition circuit’ and observes that translations from lesser-known languages are predominantly marked by a supply-driven agenda. Through case studies from the work of Transylvanian-Hungarian playwright András Visky, the paper argues that considerations regarding such key tenets of live theater as “speakability” and “performability” have to be addressed in parallel with correspondences in meaning, rhythm and spirit. The paper also points out that register and the status of certain lexical choices differ in various languages. Nuancing the trajectory of Visky’s plays in English translation, this paper makes a case for translations created with and for their originals, in full knowledge of the source and receiving cultures, and with a view to their potential in performance. The paper posits the need for multiple options encoded in the translation journey, including hypothetical concepts for future mise-en-scène, and situates the translator as a key participant in the performance making process.

Memristor-based Hopfield network circuit for recognition and sequencing application

Hopfield neural network has been widely used in image recognition because of its associative memory behavior. In this paper, a memristor neural network circuit is designed, which can recognize and sequence four characters simultaneously. It mainly includes three modules, namely a character recognition module, a signal processing module and a sequence module. The character recognition module consists of four individual character recognition units, corresponding to the recognition of four character images (W, H, A, T). The character recognition module includes calculation submodule and iteration submodule. After the operation of the calculation submodule and the iterative submodule, the four-character images distributed by noise can be identified simultaneously. The signal processing module is used to simplify the output signals of the character recognition module by four adder units. The sequence module ensures that stable state is eventually converged to the word (WHAT). The synapse weight circuit given in this paper can obtain different weights, so as to realize the function of associative memory. The iterative process circuit of Hopfield neural network is also designed to further demonstrate the iterative process. The neural network circuit composed of memristors maybe smaller, which may provide a reference for the development of new brain-like system.

Identification circuit based on memristor

Aiming at the characteristics of the memristor with memory function in the circuit, the establishment of the memristor model and the simulation of the recognition circuit based on the memristor were carried out through the PSPICE simulation software. The NiO memristor was successfully fabricated on Si substrate by magnetron sputtering method, and the typical V-I hysteresis characteristic curve was obtained. Connecting the NiO memristor to the identification hardware circuit, the physical test results show that it is the same as the simulation diagram of the recognition circuit based on the memristor, which further confirms that the identification circuit based on the memristor has the learning function.

A quantum deep convolutional neural network for image recognition

Deep learning achieves unprecedented success involves many fields, whereas the high requirement of memory and time efficiency tolerance have been the intractable challenges for a long time. On the other hand, quantum computing shows its superiorities in some computation problems owing to its intrinsic properties of superposition and entanglement, which may provide a new path to settle these issues. In this paper, a quantum deep convolutional neural network (QDCNN) model based on the quantum parameterized circuit for image recognition is investigated. In analogy to the classical deep convolutional neural network (DCNN), the architecture that a sequence of quantum convolutional layers followed by a quantum classified layer is illustrated. Inspired by the variational quantum algorithms, a quantum–classical hybrid training scheme is demonstrated for the parameter updating in the QDCNN. The network complexity analysis indicates the proposed model provides the exponential acceleration comparing with the classical counterpart. Furthermore, the MNIST and GTSRB datasets are employed to numerical simulation and the quantitative experimental results verify the feasibility and validity.

A2Event: A Micro-Watt Programmable Frequency-Time Detector for Always-On Energy-Neutral Sensing

Internet of Things (IoT) sensors are becoming more and more intelligence and unobtrusive, surrounding us in everyday life. A key requirement for those devices is to be “always-on” performing “smart” detection to catch only the events of interest when they occur. A recent and trend is to design low power circuit to extract features close to the sensors without an analog-digital conversion. The goal is to achieve place-and-forget deployment with perpetual operation: this poses the challenge of achieving micro-watt power consumption of the always-on analog event detector and of harvesting energy from environmental sources, to supply the smart sensor. This paper presents A2Event (Analog to Event), a microwatt programmable pattern recognition circuit for analog sensors, with up to 128 simultaneous time-frequency features exploiting mixed-signal low power design, which is able to generate an event when a targeted pattern is recognized. Moreover, the paper proposes a self-sustaining always-on smart audio detector that combines A2Event with energy harvesting to detect events of interests perpetually. Experimental results show that A2Event consumes only 26.89 μW in always-on mode, during the time-frequency feature-extraction, while the whole system consumes only 63 μW during pattern recognition including the power for a commercial MEMS microphone and the energy harvesting subsystem. We demonstrate that the whole smart sensor operates perpetually when powered with a small form factor flexible photovoltaic panel in indoor lighting conditions, achieving a detection accuracy of 100% in the detection of two different audio streams.

Design of Microcontroller Based Cost Effective Portable Detection Circuit for Pesticide Recognition

Excessive use of pesticides for better agricultural productivity poses serious threats to human health. Depending upon the duration and dose of exposure, deleterious effects may be intense or moderate, which may be one of the root causes of several diseases such as diabetes, asthma, cancer, cardiac attack, fertility defects etc. Inhibition of an acetylcholinesterase enzyme activity is commonly associated with pesticide exposure and as a result acetylcholine levels are elevated in blood of an exposed person. Common tools for detection of pesticide exposure measure an enzyme activity for acetylcholinesterase (ACHE), an important enzyme that regulates the neuro transmission functions in the human body. Proposed work includes an electronic design and simulation of microcontroller based portable detection circuit using electrochemical amperometric instrumentation for measurement of an acetylcholinesterase concentration in human blood. Handheld detection circuit consists of AT89C51 microcontroller, Analog to digital converter ADC0804 and Liquid Crystal Display Module LM016L.To program the microcontroller, Embedded C is used, due to their inherent characteristics of adaptability and portability across a wide assortment of hardware platforms. The developed detection circuit for biomonitoring of pesticides is cheap, flexible and covers wide range as compared to commercially available ones.