Parallel-Pipeline Fast Walsh-Hadamard Transform using FPGA

Walsh Hadamard Transform (WHT) is an orthogonal, symmetric, involutional, and linear operation used in data encryption, data compression, and quantum computing. The WHT belongs to a generalized class of Fourier transforms, which allows many algorithms developed for the fast Fourier transform (FFT) to work for fast WHT implementations (FWHT). This paper employs this property and uses a well-known parallel-pipeline FFT strategy for VLSI implementation to build parallel-pipeline architectures for FWHT. We apply the FFT parallel-pipeline approach on a Fast WHT and use the High-Level Synthesis (HLS) tool from Xilinx Vitis to generate an FPGA solution.

We also provide an open-source code with the basic blocks to build any model with any parallelization level. The parallel-pipeline proposed solutions achieve a latency reduction of up to 3.57% compared to a pipeline approach on a 256-long signal using 32-bit floating-point numbers.

Fig.1. Fast Walsh-Hadamard Transform dived on a vector of 8 samples.

The black dots perform the sum between the two input arrows. The

dashed lines invert the data sign, and the solid lines keep the data sign

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https://ieeexplore.ieee.org/document/9609874

Single-Pixel Near-Infrared 3D Image Reconstruction in Outdoor Conditions

In the last decade, vision systems have improved their capabilities to capture 3D images in bad weather scenarios. Several techniques exist for image acquisition in foggy or rainy scenarios that use infrared (IR) sensors. Due to the reduced light scattering at the IR spectra, it is possible to discriminate the objects in a scene compared with the images obtained in the visible spectrum. Therefore, in this work, we proposed 3D image generation in foggy conditions using the single-pixel imaging (SPI) active illumination approach in combination with the Time-of-Flight technique (ToF) at 1550 nm wavelength. For the generation of 3D images, we use space-filling projection with compressed sensing (CS-SRCNN) and depth information based on ToF. To evaluate the performance, the vision system included a designed test chamber to simulate different fog and background illumination environments and calculate the parameters related to image quality.

NIR-SPI System Test~Architecture

Fig.1. Sequence algorithm used to generate 2D/3D images.

In this work, we propose an NIR-SPI vision system based on the structured illumination scheme depicted in Figure 1. Still, instead of using an SLM or a DMD to generate the structured illumination patterns, an array of 8 × 8 NIR LEDs is used, emitting radiation with the wavelength λ = 1550 nm. The NIR-SPI system architecture is divided into two stages: the first one controls the elements used to generate images by applying the already explained Fig.2. 3D NIR-SPI camera system developed

single-pixel imaging principle: an InGaAs photodetector (diode FGA015 @ 1550 nm), accompanied by an array of 8 × 8 NIR LEDs. Nevertheless, the spatial resolution of the objects in the scene is achieved by applying the Shape-From-Shading (SFS) method and the unified reflectance model, additionally applying mesh enhancement algorithms, is still very much away from the aimed resolution goal of below 10 mm at a distance of 3 m. Thus, four control spots were incorporated into the system illumination array, consisting of NIR lasers with controlled variable light intensity emulating an illumination sinusoidal signal modulated in time and four additional InGaAs photodiode pairs to measure the distance to the objects in the depicted scene with much higher precision, using the indirect Time-of-Flight (iTOF) ranging method (see Figure 3a). The second stage of the system is responsible for processing the captured signals by the photodiode module through the use of an analog-to-digital converter (ADC), which is controlled by a Graphics Processing Unit (GPU) (see Figure 2). The GPU unit (Jetson–Nano) generates the Hadamard patterns and processes the converted data by the ADC. The 2D/3D image reconstruction is performed using the OMP-GPU algorithm.

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https://www.mdpi.com/2072-666X/13/5/795/htm

Araguaney Solar vehicle telemetry system

In an automotive prototype, having a telemetry system is of great importance since it allows measuring and recording its behavior under real operating conditions. For this reason, a telemetry system was developed, composed of hardware and software stages. For the system's development process, first, the variables to be measured were defined, and the data transmission networks and communication stages were chosen. Both the RF communication cards and the 915 MHz band were chosen.

For this application, we use data packet formats, communication protocols, encryption algorithms, and Red Solomon coding to increase the transmitted data's reliability and efficiency. The radio system developed comprises a transmitter chipset (CC1101), coupling networks, amplifiers, duplexers, and an antenna (see Fig. 1). The transfer of information to the transmitter and receiver card is done through the CAN 2.0 protocol and the telemetry card, as shown in Fig.1.Transmitter and receiver card Figure 2.

using chip CC1101.

Communication protocol CC1101

The information packets coming from the CAN bus are transferred to the RF module via SPI protocol. The captured information is stored in registers depending on the packet type. These packets are assigned an identification command,

Fig.2. Control card of transmitter and length, and CRC16 polymode, encrypted using AES. After

the receiver based on chip CC1101 passing through this process, the packets are separated into

128-byte blocks. Each block is encoded using Reed Solomon, forming three blocks of 160 bytes to which an identifier is assigned. The new block encoding is transferred via SPI to the chipset in 256-byte blocks. The maximum data buffer size of the CC1101 is 256 bytes.

At the hardware level, the telemetry system has the following characteristics:

- Modulation type: 2-FSK.

Communication technique: FHSS 510 hops/s.

- ∆f = 80 KHz.

- Fc = 915 MHz.

- Rb = 125 kbps.

- Modulation index: 0.64.

- Channel frequency: 200 kHz.

- Number of channels 16.

- Minimum output power: +0dBm.

- Maximum output power: +27dBm.

- Sensitivity: -86.5 dBm.

- Network losses: 2dB.

- Free space losses 97.748dB.

- Maximum range: 1Km

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