This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users’ configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

Biological control often combines selective sensing, complementary response scales, and attenuation under weak evidence. Guided by these principles, this paper presents Spectrum-Guided Identifiable Single-Anchor Range Compensation (SGIS-RC), a deterministic physics–data framework for single-anchor scalar-range compensation. SGIS-RC removes the local anchor–bias nuisance tangent from the kinematic sensitivity, retains a numerically conditioned physical subspace, and models the remaining discrepancy through a Chebyshev–Matérn global–local residual field. The learned correction is further regulated by leverage, configuration-space support, and bounded amplitude control. On two public workbooks, ABB IRB120 and HSR-C JR680, a pre-specified outer-partition evaluation performed after the model specification was frozen yielded configuration-group MAEs of $0.2306\pm 0.0148$ mm and $0.0493\pm 0.0021$ mm, respectively, while the matched conventional hybrid yielded slightly lower mean errors of $0.2244\pm 0.0151$ mm and $0.0478\pm 0.0015$ mm. Same-fit analyses show that support placement can alter the balance between global and local residual correction, and the block protocols indicate that support-shift behavior remains dataset dependent. Overall, SGIS-RC provides a structured and auditable framework that integrates identifiability-aware physical adaptation, global–local residual modeling, and support-aware deployment control for single-anchor scalar-range compensation.