Field Programmable Gate Array
Field Programmable Gate Array - Related Videos
10:36
Electric-field Control of Electronic States in WS2 Nanodevices by Electrolyte Gating
0 Views •
Cited by 2 •
2018
Here, we present a protocol to control the carrier number in solids by using the electrolyte.
05:30
Making Patch-pipettes and Sharp Electrodes with a Programmable Puller
0 Views •
Cited by 22 •
2008
This video shows how to use a programmable puller to make patch pipettes and sharp electrodes for electrophysiology. The same procedure can be used to make a variety of glass tools, including injection needles.
07:51
Development and Functionalization of Electrolyte-Gated Graphene Field-Effect Transistor for Biomarker Detection
0 Views •
Cited by 2 •
2022
The present protocol demonstrates the development of electrolyte-gated graphene field-effect transistor (EGGFET) biosensor and its application in biomarker immunoglobulin G (IgG) detection.
12:55
P50 Sensory Gating in Infants
0 Views •
Cited by 5 •
2013
Methods to record auditory P50 sensory gating, a physiological marker of cerebral inhibition which reflects early stages of attention, are described.
01:30
Ligand-Gated Ion Channel Receptor: Gating Mechanism
0 Views •
2023
Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...
FAQs
Parallel execution allows multiple operations to proceed at the same time rather than waiting for a processor to complete one sequence before starting another. This architecture can reduce processing delay when biomedical systems must handle incoming signals continuously. The resulting low-latency computation is especially relevant to real-time signal processing, medical imaging, and responsive neural or wearable interfaces.
These three resource types provide the device’s functional structure. Configurable logic blocks implement digital operations, programmable interconnects determine how those operations communicate, and input/output resources connect the design with external systems. Together, they let a hardware design match the computational and communication requirements of a bioengineering instrument or interface instead of relying on a fixed circuit arrangement.
A hardware-description language specifies the behavior that the device will implement, allowing designers to translate a computational requirement into configurable digital hardware. This supports specialized processing rather than merely running a general software routine. In bioengineering prototypes, the design can therefore be adjusted as signal-processing, imaging, control, or interface requirements evolve.
Software on a conventional processor generally executes operations sequentially, whereas an FPGA design can organize operations to execute in parallel through configurable hardware resources. That distinction can provide faster data handling for workloads requiring continuous or specialized computation. The hardware approach also preserves reprogrammability, so researchers can modify the implemented design without changing the manufactured integrated circuit.
A typical workflow begins by identifying the system’s computational requirement, such as signal processing, imaging, or instrument control. Designers then describe the intended hardware behavior with a hardware-description language and configure the device’s logic, interconnects, and input/output resources accordingly. The resulting implementation can be revised when research requirements change, supporting iterative prototype development.
They are useful when a system must handle data rapidly and with limited processing delay. Biomedical signal processing can require continuous handling of incoming measurements, while medical imaging can involve specialized computation on acquired data. FPGA-based hardware acceleration addresses these demands by assigning suitable operations to configurable digital resources, supporting real-time or near-immediate system responses.
In neural and wearable interfaces, the device can provide rapid processing close to the system’s data source while remaining adaptable during research. For bioengineering instruments, it can implement specialized control and computation within the same reconfigurable platform. This combination of data-handling speed and design flexibility helps bridge experimental prototypes and deployable devices as requirements become better defined.