Calyo Pulse: Advanced Pulsed Sensing

• Calyo Pulse is a family of sensor and instrumentation technologies characterized by ultra-stable, programmable pulses for precise calibration, neurostimulation, flow sensing, and 3D perception.
• It employs advanced methodologies—such as low-noise pulsing, programmable signal control, and modular architectures—to achieve sub-ppm thermal stability and efficient energy recovery.
• The systems leverage closed-form analytic modeling and real-time control to enable calibration-free, high-precision measurements across biomedical, cryogenic, and autonomous applications.

Calyo Pulse refers collectively to a family of sensor and instrumentation technologies employing advanced pulsed signal generation, detection, and processing architectures across diverse applications, including ultra-precise calibration for bolometric detectors, modular high-power pulse synthesis for neuroscience and medical stimulation, calorimetric flow sensing for biomedical catheters, and solid-state 3D ultrasound perception for autonomous systems. These heterogeneous systems are unified by their reliance on programmable, low-noise, and high-stability pulsing mechanisms to enable accurate measurement, actuation, or perception in demanding operating regimes.

1. Multi-Channel Ultra-Stable Pulse Generation for Bolometric Calibration

The Calyo Pulse system for bolometric calibration is designed to provide ultra-stable, multi-channel, programmable electrical pulses suitable for in situ energy calibration and stabilization of large cryogenic bolometer arrays operating in extremely low-background environments (Carniti et al., 2017). Each board features four fully independent differential channels, each capable of delivering a DC level or a precisely timed square pulse, with amplitude programmable up to ±5 V (pre-attenuation) and a pulse width adjustable in 1 μs increments over a 1 μs–2 s range.

A 1 MHz reference clock underlies pulse timing, with control and housekeeping managed by an NXP LPC1768 Cortex-M3 microcontroller. Outputs are gated by fast bistable relays and synchronized via a low-skew daisy-chained optical trigger. The system supports remote configuration and monitoring over a 100 kbit/s CAN-bus, providing full pulse parameterization and board status telemetry. On-board temperature monitoring (silicon diodes) and compensation (PID loop with AD7732 ADC) underpin sub-ppm/°C amplitude and width stability.

Energy injected to a heater is

$$E\,[\mathrm{eV}] \;=\;\eta\;\frac{V_0^2\,T}{e\,R\,\varepsilon^2}$$

where $V_0$ is post-divider pulse amplitude, $T$ pulse width, $R$ heater resistance, $\epsilon$ attenuation factor, and $\eta$ the thermal coupling efficiency. White noise in the pulser output limits single-pulse energy resolution, with

$$\Delta E_\mathrm{FWHM}\approx 20\,\mathrm{eV\,at\,1\,MeV};\,2\,\mathrm{eV\,at\,10\,keV}$$

after conversion from the calculated $\sigma_E$. Thermal drift is actively compensated to achieve residual energy drift $<0.1\,\mathrm{ppm/}^\circ\mathrm{C}$ over the range 20–60 °C.

Pulse width jitter (<300 ps RMS) is two orders of magnitude below the voltage-noise limited resolution. Each channel may also be configured for static DC output to drive cryostat heaters under PID temperature control, as deployed in CUORE and CUPID-0, achieving sub-μK baseline stabilization.

Performance Metric	Value	Conditions
Amplitude range	0 … 5 V (single-ended), 10 Vpp (differential)	pre-divider
Pulse width range	1 μs – 2 s, 1 μs steps	–
Pulse width jitter	≤ 300 ps RMS	T ≥ 100 μs
Amplitude thermal drift	≤ 8 ppm/°C (uncorr.), ≤ 0.1 ppm/°C (compensated)	–
Energy resolution (FWHM)	20 eV @ 1 MeV, 2 eV @ 10 keV	–
Control	100 kbps CAN, optical trigger (<10 ns skew)	–

These capabilities enable continuous, on-line calibration and thermal control in rare-event experiments where radioactive sources are untenable due to background constraints (Carniti et al., 2017).

2. Modular Pulse Synthesis Architecture for Neurostimulation and High-Power Pulsing

The Calyo Pulse modular pulse synthesizer system is a scalable, digitally controlled platform capable of generating arbitrary high-power voltage and current pulses with rapid reconfiguration and flexible waveform shaping, primarily demonstrated in the context of transcranial magnetic stimulation (TMS) (Li et al., 2022). The design uses $N$ cascaded, nearly identical modules (prototype: $N=6$), each providing a high-speed H-bridge cell and local DC-link capacitor bank (supporting up to three capacitance stages for commutation and bulk storage). Each H-bridge employs parallel, press-fit SiC MOSFETs (e.g., Infineon FF11MR12W1M1) for >10 kA pulsed current and >4 kV voltage handling.

Modules interconnect via series and bypass states, supporting programmable insertion, subtraction, or parallelization of their voltage steps into the output chain. During pulse emission, real-time control logic (FPGA/CPLD for sub-μs switching, ARM MCU for supervision) sequences each module’s output state according to the desired waveform, achieving voltage quantization in $2N+1$ discrete steps (e.g., for $N=6$, steps of ~667 V from –4 kV to +4 kV).

Phase-Shifted Carrier (PSC) modulation distributes switching events across modules, enabling a coil output bandwidth of DC to >60 kHz with <1% amplitude error. A “passive” mode routes residual coil current into freewheeling diodes for energy recovery, with overall system pulse efficiency

$$\eta_{\mathrm{pulse}} = \frac{E_{\mathrm{out}}}{E_{\mathrm{stored}}} \approx 0.90\text{–}0.98$$

for typical biphasic pulses.

The system achieves <50 μs minimal inter-pulse interval and fully software-defined pulse sequence programmability, supporting arbitrary changes in pulse shape, amplitude, polarity, and timing.

Key hardware performance includes maximum output voltage ±4 kV, peak coil currents up to ±8 kA, and effective per-module switching rates of 100 kHz. Stress tests confirm safe device temperatures (≤85 °C) under continuous operation at 10⁶ pulses and >100 kHz switching. Thermal protection is ensured with on-board temperature sensors and forced-air rack ventilation.

This architecture enables rapid prototyping and exploration of complex pulse sequences for neuroscience, offering advanced features compared to conventional single-oscillator or IGBT-based TMS devices, which lack both rapid pulse-to-pulse reconfiguration and high-efficiency energy recovery (Li et al., 2022).

3. Calorimetric Flow Sensing in Biomedical Applications

A miniature Calyo Pulse calorimetric sensor is employed for catheter-based assessment of coronary artery flow (Gelderblom et al., 2011). The device comprises a constant-power heater and spatially distributed thermopiles positioned upstream and downstream of the heater on the sensor substrate. The thermal response of the overlying fluid is governed by a two-dimensional advection-diffusion equation, with a quasi-steady assumption justified for Strouhal numbers $Sr = \omega/S_0 \ll 1$.

The sensor domain is modeled as a strip (0 ≤ y ≤ h) above a heated wall with linearly time-dependent velocity $u(y,t) = S(t)y = S_0 [1 + \beta\sin(\omega t)]y$. The governing equation is

$$S(t)\,y\,\frac{\partial T}{\partial x} = \alpha \left( \frac{\partial^2 T}{\partial x^2} + \frac{\partial^2 T}{\partial y^2} \right)$$

with a Gaussian heat-flux boundary condition on the wall,

$$q(x) = -k\,\left.\frac{\partial T}{\partial y}\right|_{y=0} = \frac{Q}{l\,\sigma\sqrt{2\pi}}\exp\left[ -\frac{(x-x_h)^2}{2\sigma^2} \right]$$

where $\sigma = b/2$ is half the heater width, $l$ its spanwise length.

An analytical solution is constructed by spectral decomposition in $y$, leading to a coupled ODE system for the modal amplitudes $C_k(x)$, which is solved in Fourier space. Key dimensionless numbers include the Péclet number

$Pe = \frac{b\,S}{\alpha}$

and the Strouhal number

$Sr = \frac{\omega}{S_0}$

Experimental validation in a 5 mm tube demonstrates agreement with model predictions to within 5% for heater overheat (h–T_f) signals and ~27% for downstream–upstream thermopile (d–u) signals, with the latter deviation attributable to unmodeled substrate–fluid coupling effects.

Optimal sensitivity is achieved for coronary-scale shear rates ($S \sim 10^3\,\mathrm{s}^{-1}$) by appropriate selection of heater width and thermopile geometry. The ratio

$R = \frac{d-u}{h-T_f}$

is a monotonic, calibration-free function of shear rate $S$, as the Seebeck coefficient cancels. Device deployment in vivo must further consider blood rheology, mounting-induced curvature, and substrate heat loss, with the model extendable through small conjugate conduction corrections (Gelderblom et al., 2011).

4. Solid-State 3D Ultrasound Sensing for Volumetric Perception

Calyo Pulse also encompasses a modular, solid-state 3D ultrasound sensor platform for real-time volumetric scene understanding in adverse environments (Liu et al., 19 Jan 2026). The architecture integrates a 40 kHz Tx transducer and a broadband 32-channel receive array (100 Hz–80 kHz) in a ruggedized, fanless enclosure with <1 W power draw. The sensor supports direct USB-C connectivity, providing raw RF, 3D point cloud, and 2D projection outputs via the Calyo Sensus SDK.

The sensor operates over a range interval $r \in [0, 12\,\mathrm{m}]$ (96 bins at 0.125 m), azimuth $\phi \in [-90^\circ, 90^\circ]$ (64 bins), and elevation $\theta \in [-10^\circ, 30^\circ]$ (64 bins), yielding a 3D grid of $64 \times 96 \times 64$ voxels. Real-time on-device signal conditioning includes delay-and-sum (DAS) and minimum variance distortionless response (MVDR) beamforming, followed by constant false alarm rate (CFAR) filtering, ego-vehicle masking, ground-plane removal, and object filtering (size: L ∈ [0.2, 8 m]; W,H ∈ [0.5, 3 m]). Range compensation applies to mitigate signal roll-off.

Labeled data are constructed using LiDAR-based semi-automatic bounding box annotation and are stored as aligned occupancy masks on the 3D volume grid. Neural semantic segmentation employs a two-stage 3D U-Net architecture with Dice-based loss to address class imbalance. The network achieves background segmentation F₁ (Dice) of 99.52% and object F₁ of 67.30% on test data. Typical qualitative errors include missed detections for occluded or low-reflectivity objects and apparent false positives due to acoustic returns of fences or poles not labeled in LiDAR ground truth.

The sensor demonstrates robustness in conditions challenging for optical or LiDAR modalities (e.g., rain, fog, dust) and provides a volumetric input stream suitable for integration into multi-modal perception frameworks (Liu et al., 19 Jan 2026).

5. Technological and Methodological Unification

Despite application diversity, Calyo Pulse systems are characterized by:

• Ultra-stable, programmable pulse and waveform generation, backed by detailed characterization of timing, amplitude, and noise sources.
• Modular, scalable hardware platforms supporting software-defined operation and real-time control.
• Integrated calibration, compensation, or signal processing workflows tailored to the physical constraints of each use case (e.g., thermal PID for bolometers, PSC modulation for TMS, spectral-Fourier analysis for flow, CFAR for ultrasound).
• Emphasis on closed-form analytic modeling and experimental validation, enabling device optimization and calibration-free or monotonic mapping between measured signals and physical quantities.

Each system serves as a reference in its field for high-precision, programmable pulsing and sensing, supported by comprehensive theoretical treatment and benchmarked against conventional approaches in both laboratory and application-specific scenarios.

6. Prospective Directions and Challenges

Emerging challenges in the deployment and extension of Calyo Pulse technologies include:

• For calorimetric flow sensors: modeling of non-planar geometries, biofluid-specific conductance, and in vivo substrate effects.
• For modular pulse synthesizers: further integration of energy recovery mechanisms, scaling beyond 6 modules, and exploration of alternative semiconductor topologies for higher frequency output.
• For 3D ultrasound: annotation and generalization to multi-class labeling, larger and more diverse datasets, and sensor fusion with optical/radar systems.
• For all platforms: evolution toward more adaptive, self-calibrating architectures and the expansion of application domains via integration with machine learning methods for real-time control and interpretation.

The technological framework established by Calyo Pulse systems thus provides a foundation for continued advances in precision measurement, actuation, and perception across scientific and engineering disciplines (Gelderblom et al., 2011, Carniti et al., 2017, Li et al., 2022, Liu et al., 19 Jan 2026).