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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
The Ideal Transformer01:26

The Ideal Transformer

1.4K
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
1.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

389
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
389
Energy Losses in Transformers01:21

Energy Losses in Transformers

1.3K
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
1.3K
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

1.4K
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
1.4K
Transformers in Distribution System01:27

Transformers in Distribution System

497
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Related Experiment Video

Updated: Jan 15, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

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IML-Spikeformer: Input-Aware Multilevel Spiking Transformer for Speech Processing.

Zeyang Song, Shimin Zhang, Yuhong Chou

    IEEE Transactions on Neural Networks and Learning Systems
    |October 8, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Input-Aware Multilevel Spikeformer (IML-Spikeformer), a novel Spiking Neural Network (SNN) architecture for efficient speech processing. IML-Spikeformer achieves competitive performance with reduced energy consumption, advancing neuromorphic computing for large-scale applications.

    Related Experiment Videos

    Last Updated: Jan 15, 2026

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

    Published on: March 25, 2014

    10.3K

    Area of Science:

    • Neuromorphic Computing
    • Artificial Intelligence
    • Speech Processing

    Background:

    • Spiking Neural Networks (SNNs) offer energy efficiency but face challenges in large-scale speech tasks.
    • Existing SNNs struggle with high computational overhead and lack specialized architectures for speech.
    • Traditional Artificial Neural Networks (ANNs) are effective but less energy-efficient.

    Purpose of the Study:

    • To develop a scalable Spiking Neural Network architecture for high-performance, energy-efficient speech processing.
    • To address the limitations of current SNNs in handling large-scale speech data and computational demands.
    • To introduce the Input-Aware Multilevel Spikeformer (IML-Spikeformer) as a solution.

    Main Methods:

    • Introduced the Input-Aware Multilevel Spike (IMLS) mechanism to simulate multitimestep spiking within a single timestep.
    • Developed the Hierarchical Decay Mask-Reparameterized Spiking Self-Attention (HD-RepSSA) module for precise attention and multiscale temporal modeling.
    • Designed the IML-Spikeformer, a novel spiking transformer architecture tailored for speech processing.

    Main Results:

    • Achieved competitive Word Error Rates (WERs) of 6.0% on AiShell-1 and 3.4% on Librispeech-960.
    • Demonstrated significant reductions in theoretical inference energy consumption ($4.64\times$ on AiShell-1, $4.32\times$ on Librispeech-960) compared to ANN transformers.
    • Showcased IML-Spikeformer's scalability and effectiveness in large-scale speech processing tasks.

    Conclusions:

    • IML-Spikeformer represents a significant advancement in scalable SNN architectures for speech processing.
    • The proposed architecture achieves both high task performance and superior energy efficiency.
    • Publicly available code and model checkpoints facilitate further research and development in neuromorphic speech processing.