WJ Wenying Jia
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Research

Hardware-oriented intelligent sensing

My research interests lie in systems that integrate sensing materials, electronics, embedded software, and machine learning.

Current research

Neuromorphic sensory intelligent systems

My recent work investigates multimodal learning for object classification from visual and tactile information collected during grasping. The research includes developing a tactile glove and its acquisition architecture and evaluating uni- and multimodal learning methods.

  • 30 fabricated piezoresistive pressure sensors distributed across a tactile glove
  • Embedded master–slave acquisition architecture and real-time data logging
  • Visual and tactile preprocessing for paired learning samples
  • Multimodal learning based on halfway and late fusion of 1D/2D CNNs
Vision–tactile perception Tactile sensing Sensor fusion Soft electronics Multimodal learning Object classification
Illustration of a tactile sensing glove with distributed pressure sensors
Research directions

Future research directions

I am currently exploring these themes to connect my current experience with possible future doctoral research in robotics and intelligent physical systems.

A · MULTIMODALITY

Cross-modal perception

Learning relationships between complementary modalities such as vision, touch, motion, and sound for robust perception.

B · EMBODIMENT

Robotic and embodied intelligence

Integrating sensing into systems that physically interact with uncertain environments and adapt through contact.

C · HARDWARE

Distributed sensing platforms

Scalable embedded architectures, synchronized data acquisition, and reliable hardware–software interfaces.

D · MATERIALS

Soft and tactile sensor integration

Combining emerging sensing materials with practical electronics, packaging, calibration, and system validation.

Working method

Build, measure, understand

I enjoy research that moves repeatedly between physical prototyping and data analysis.

01

Build the system

Sensor fabrication, circuits, embedded communication, mechanical integration, and acquisition software.

02

Characterize the behavior

Calibration, controlled experiments, synchronized data collection, and reliability checks.

03

Interpret the data

Signal processing, visualization, statistical evaluation, and machine-learning-based classification.