Search Results (115)

For data-intensive applications like edge AI and image processing, we present a new reconfigurable 8T SRAM-based in-memory computing (IMC) macro designed for high-performance and energy-efficient operation. This architecture mitigates von Neumann limitations through numerous major breakthroughs. We built a new architecture with an adjustable capacitance array to substantially increase the multiply-and-accumulate (MAC) engine’s accuracy. It achieves 10–20 TOPS/W and >95% accuracy for 4–10-bit operations and is robust across PVT changes. By supporting binary and ternary neural networks (BNN/TNN) with XNOR-and-accumulate logic, a dual-mode inference engine further expands capabilities. With sub-5 ns mode switching, it can achieve up to 30 TOPS/W efficiency and >97% accuracy. In-memory Hamming error correction is implemented directly using integrated XOR circuitry. This technique eliminates off-chip ECC with >99% error correction and >98% MAC accuracy. Machine learning-aided co-optimization ensures sense amplifier dependability. To ensure CMOS compatibility, the macro may perform Boolean logic operations using normal 8T SRAM cells. Comparative circuit-level simulations show a 31.54% energy efficiency boost and a 74.81% delay reduction over other SRAM-based IMC solutions. These improvements make our macro ideal for real-time AI acceleration, cryptography, and next-generation edge computing, enabling advanced compute-in-memory systems. Full article

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. In general, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. It is also difficult to manually create and implement the quantum algorithm for the swap circuit as the size of the system increases. Here, we propose a different approach: to use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB 2022a. Training is conducted for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that the scale-up is very effective and relatively easy, and the effects of noise and decoherence are $reduced$ as the size of the system increases. Full article

This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has many advantages over other decomposition methods, notably for non-stationary signals and noise. VMD’s robustness stems from its ability to decompose a signal into intrinsic mode functions (IMFs) with well-defined centre frequencies and bandwidths. The proposed methodology integrates VMD with a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture to efficiently extract and learn distinctive temporal and spectral properties from three-phase current sources. Ten operational scenarios with a wind speed range of 5–16 m/s were simulated using a comprehensive MATLAB/Simulink version R2022b model, including one healthy condition and nine unique IGBT failure conditions. The obtained current signals were decomposed via VMD to extract essential frequency components, which were normalised and utilised as input sequences for deep learning models. A comparative comparison of CNN-LSTM and CNN-only classifiers revealed that the CNN-LSTM model attained the greatest classification accuracy of 88.00%, exhibiting enhanced precision and resilience in noisy and dynamic environments. These findings emphasise the efficiency of integrating advanced signal decomposition with deep sequential learning for real-time, high-precision fault identification in wind turbine power electronic converters. Full article

Synaptic plasticity is the key mechanism underlying learning and memory. Neurexins are pre-synaptic molecules that play a pivotal role in synaptic plasticity, interacting with many different post-synaptic molecules in the formation of neural circuits. Neurexins are alternatively spliced at different splice sites, yielding thousands of isoforms with different properties of interaction with post-synaptic molecules for a quick adaptation to internal and external inputs. The endocannabinoid system also plays a central role in synaptic plasticity, regulating key retrograde signaling at both excitatory and inhibitory synapses. This study aims at elucidating the crosstalk between alternative splicing of neurexin and the endocannabinoid system in the hippocampus. By employing an ex vivo hippocampal system, we found that pharmacological activation of cannabinoid receptor 1 (CB1) with the specific agonist ACEA led to reduced neurotransmission, associated with increased expression of the Nrxn1–3 spliced isoforms excluding the exon at splice site 4 (SS4−). In contrast, treatment with the CB1 antagonist AM251 increased glutamatergic activity and promoted the expression of the Nrxn variants including the exon (SS4+) Knockout of the involved splicing factor SLM2 determined the suppression of the exon splicing at SS4 and the expression only of the SS4+ variants of Nrxns1–3 transcripts. Interestingly, in SLM2 ko hippocampus, modulation of neurotransmission by AM251 or ACEA was abolished. These findings suggest a direct crosstalk between CB1-dependent signaling, neurotransmission and expression of specific Nrxns splice variants in the hippocampus. We propose that the fine-tuned regulation of Nrxn1–3 genes alternative splicing may play an important role in the feedback control of neurotransmission by the endocannabinoid system. Full article

Gas bubbles are one of the main disturbances encountered when dispensing drugs of microliter volumes using portable miniaturized systems based on piezoelectric diaphragm micropumps. The presence of a gas bubble in the pump chamber leads to the inaccurate administration of the required dose due to its impact on the flowrate. This is particularly important for highly concentrated drugs such as insulin. Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. These solutions increase the complexity, size, and cost of the microdosing system. To address these problems, a radically new approach is taken by utilizing the sensing capability of the piezoelectric diaphragm during micropump actuation. This work demonstrates the workflow to build a self-sensing micropump based on artificial intelligence methods on an embedded system. This is completed by the implementation of an electronic circuit that amplifies and samples the loading current of the piezoelectric ceramic with a microcontroller STM32G491RE. Training datasets of 11 micropumps are generated at an automated testbench for gas bubble injections. The training and hyper-parameter optimization of artificial intelligence algorithms from the TensorFlow and scikit-learn libraries are conducted using a grid search approach. The classification accuracy is determined by a cross-training routine, and model deployment on STM32G491RE is conducted utilizing the STM32Cube.AI framework. The finally deployed model on the embedded system has a memory footprint of 15.23 kB, a runtime of 182 µs, and detects gas bubbles with an accuracy of 99.41%. Full article

This study proposes a deep learning (DL)-based method for identifying the parameters of equivalent circuit models (ECMs) for lithium-ion batteries using time-series voltage response data from current pulse charge–discharge experiments. The application of DL techniques to this task is presented for the first time. The best-performing baseline model among the recurrent neural network, long short-term memory, and gated recurrent unit achieved a mean absolute percentage error (MAPE) of 0.52073 across the five parameters. Furthermore, more advanced models, including a one-dimensional convolutional neural network (1DCNN) and temporal convolutional networks, were developed using full factorial design (FFD), resulting in substantial MAPE improvements of 37.8% and 30.4%, respectively. The effectiveness of Latin hypercube sampling (LHS) for training data generation was also investigated, showing that it achieved comparable or better performance than FFD with only two-thirds of the training samples. Specifically, the 1DCNN model with LHS sampling achieved the best overall performance, with an average MAPE of 0.237409. These results highlight the potential of DL models combined with efficient sampling strategies. Full article

Conventional approaches to analyzing power losses in electrical transmission networks have largely emphasized generic power loss minimization through the integration of loss-reducing devices such as shunt capacitors. However, achieving optimal power loss minimization requires a more data-driven and intelligent approach that transcends traditional methods. This study presents a novel classification-based methodology for detecting and analyzing transmission line losses using real-world data from the Ikorodu–Sagamu 132 kV double-circuit line in Nigeria, selected for its dense concentration of high-voltage consumers. Twelve (12) transmission lines were examined, and the collected data were subjected to comprehensive preprocessing, feature engineering, and modeling. The classification capabilities of advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)—were explored through six experimental scenarios: LSTM, LSTM with Attention Mechanism (LSTM-AM), BiLSTM, GRU, LSTM-BiLSTM, and LSTM-GRU. These models were implemented using the Python programming environment and evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, support, and confusion matrices. Statistical analysis revealed significant variability in transmission losses, particularly in lines such as I1, Ps, Ogy, and ED, which exhibited high standard deviations. The LSTM-AM model achieved the highest classification accuracy of 83.84%, outperforming both standalone and hybrid models. In contrast, BiLSTM yielded the lowest performance. The findings demonstrate that while standalone models like GRU and LSTM are effective, the incorporation of attention mechanisms into LSTM architecture enhances classification accuracy. This study provides a compelling case for employing deep learning-based classification techniques in intelligent power loss classification across transmission networks. It also supports the realization of SDG 7 by aiming to provide access to reliable, affordable, and sustainable energy for all. Full article

Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics of CNN algorithms, it is difficult to implement them at edge terminals with limited memory and computing resources. This paper uses a FPGA (Field-Programmable Gate Array) to accelerate CNNs to realize forest fire recognition in the field environment and solves the problem of the difficulty in giving consideration to the accuracy and speed of a forest fire recognition network in the implementation of edge terminal equipment. First, a simple seven-layer lightweight network, LightFireNet, is designed. The network is compressed using a knowledge distillation method and the classical network ResNet50 is used as the teacher network to supervise the learning of LightFireNet so that its accuracy rate reaches 97.60%. Compared with ResNet50, the scale of LightFireNet is significantly reduced. Its model parameter amount is 24 K and its calculation amount is 9.11 M, which are 0.1% and 1.2% of ResNet50, respectively. Secondly, the hardware acceleration circuit of LightFireNet is designed and implemented based on the FPGA development board ZYNQ Z7-Lite 7020. In order to further compress the network and speed up the forest fire recognition circuit, the following three methods are used to optimize the circuit: (1) the network convolution layer adopts a depthwise separable convolution structure; (2) the BN (batch normalization) layer is fused with the upper layer (or full connection layer); (3) half float or ap_fixed<16,6>-type data is used to express feature data and model parameters. After the circuit function is realized, the LightFireNet terminal circuit is obtained through the circuit parallel optimization method of loop tiling, ping-pong operation, and multi-channel data transmission. Finally, it is verified on the test dataset that the accuracy of the forest fire recognition of the FPGA edge terminal of the LightFireNet model is 96.70%, the recognition speed is 64 ms per frame, and the power consumption is 2.23 W. The results show that this paper has realized a low-power-consumption, high-accuracy, and fast forest fire recognition terminal, which can thus be better applied to forest fire monitoring. Full article

Edge-computing applications demand ultra-low-power architectures for both feature extraction and classification tasks. In this manuscript, a Keyword Spotting (KWS) system tailored for energy-constrained portable environments is proposed. A 16-channel analog filter bank is employed for audio feature extraction, followed by a digital Gated Recurrent Unit (GRU) classifier. The filter bank is behaviorally modeled, making use of second-order band-pass transfer functions, simulating the analog front-end (AFE) processing. To enable efficient deployment, the GRU classifier is trained using a Learned Step Size (LSQ) and Look-Up Table (LUT)-aware quantization method. The resulting quantized model, with 4-bit weights and 8-bit activation functions (W4A8), achieves 91.35% accuracy across 12 classes, including 10 keywords from the Google Speech Command Dataset v2 (GSCDv2), with less than 1% degradation compared to its full-precision counterpart. The model is estimated to require only 34.8 kB of memory and 62,400 multiply–accumulate (MAC) operations per inference in real-time settings. Furthermore, the robustness of the AFE against noise and analog impairments is evaluated by injecting Gaussian noise and perturbing the filter parameters (center frequency and quality factor) in the test data, respectively. The obtained results confirm a strong classification performance even under degraded circuit-level conditions, supporting the suitability of the proposed system for ultra-low-power, noise-resilient edge applications. Full article

The temperature variations in wind turbine motors and gearboxes are closely related to their health status, making accurate temperature prediction essential for operational monitoring and early fault detection. However, conventional deep learning-based temperature prediction methods, such as recurrent neural networks (RNN) and convolutional neural networks (CNN) and their hybrid models, often face challenges in capturing both local feature dependencies and long-term temporal patterns in complex, nonlinear temperature fluctuations. To address these limitations, this paper proposes a hybrid model based on depthwise separable convolutional neural networks (DSCNNs) and bidirectional long short-term memory (BiLSTM) networks. The DSCNN module enhances feature extraction from temperature signals, while the BiLSTM module captures long-term dependencies, improving prediction accuracy and robustness. Experimental validation using temperature data from a wind farm in Shaanxi, China, demonstrates that the proposed model outperforms existing deep learning approaches, achieving superior prediction accuracy, better adaptability to temperature fluctuations, and greater robustness in handling complex nonlinear dynamics. Furthermore, the proposed model provides an effective solution for early fault detection in wind turbines, including both mechanical faults (e.g., gearbox wear, bearing overheating) and electrical faults (e.g., winding short circuits, overload conditions), contributing to more reliable condition monitoring in industrial applications. Full article

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. Full article

Hydrogen sulfide (H₂S) has emerged as a pivotal gaseous transmitter in the central nervous system, influencing synaptic plasticity, learning, and memory by modulating various molecular pathways. This review examines recent evidence regarding how H₂S regulates NMDA receptor function and neurotransmitter release in neuronal circuits. By synthesizing findings from animal and cellular models, we investigate the impacts of enzymatic H₂S production and exogenous H₂S on excitatory synaptic currents, long-term potentiation, and intracellular calcium signaling. Data suggest that H₂S interacts directly with NMDA receptor subunits, altering receptor function and modulating neuronal excitability. Simultaneously, H₂S promotes the release of neurotransmitters such as glutamate and GABA, shaping synaptic dynamics and plasticity. Furthermore, reports indicate that disruptions in H₂S metabolism contribute to cognitive impairments and neurodegenerative disorders, underscoring the potential therapeutic value of targeting H₂S-mediated pathways. Although the precise mechanisms of H₂S-induced changes in synaptic strength remain elusive, a growing body of evidence positions H₂S as a significant regulator of memory formation processes. This review calls for more rigorous exploration into the molecular underpinnings of H₂S in synaptic plasticity, paving the way for novel pharmacological interventions in cognitive dysfunction. Full article

To diagnose open-circuit faults (OCFs) in sub-module switching devices within input-independent output-series (IIOS) photovoltaic DC collection systems, this paper presents a hybrid diagnostic method combining convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). The method utilizes a sliding window technique to segment sub-module capacitor voltage signals into time-series samples. Initially, CNN automatically extracts local features from the samples, followed by LSTM for capturing temporal dependencies and extracting global time series features, enabling effective fault detection under complex conditions. This approach eliminates the need for manual feature extraction and complex system modeling. By leveraging the model’s learning capabilities, it mitigates the impact of solar irradiance fluctuations on diagnostic accuracy. After training, the model performs real-time fault diagnosis with high precision using voltage data, offering fast, efficient, and reliable performance. The effectiveness of the method was validated through both simulation and experimental results. Full article

An ultralow-power, high-performance online-learning and anomaly-detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks—one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. The system demonstrates 87% anomaly-detection accuracy; achieves a performance of 16.1 GOPS—774× faster than the ASUS Tinker Board edge processor; and delivers an energy efficiency of 783 GOPS/W, consuming only 20.5 mW during anomaly detection. Full article

Background: The advent of digital technology has significantly altered ways of writing. While typing has become the dominant mode of written communication, handwriting remains a fundamental human skill, and its profound impact on cognitive processes continues to be a topic of intense scientific scrutiny. Methods: This paper investigates the neural mechanisms underlying handwriting and typing, exploring the distinct cognitive and neurological benefits associated with each. By synthesizing findings from neuroimaging studies, we explore how handwriting and typing differentially activate brain regions associated with motor control, sensory perception, and higher-order cognitive functions. Results: Handwriting activates a broader network of brain regions involved in motor, sensory, and cognitive processing. Typing engages fewer neural circuits, resulting in more passive cognitive engagement. Despite the advantages of typing in terms of speed and convenience, handwriting remains an important tool for learning and memory retention, particularly in educational contexts. Conclusions: This review contributes to the ongoing debate about the role of technology in education and cognitive development. By understanding the neural differences between handwriting and typing, we can gain insights into optimal learning strategies and potential cognitive advantages, in order to optimize educational, cognitive, and psychological methodologies. Full article