A pulse sequence shaper for the pursuance of the research using a wide spectrum of radiospectroscopy and relaxation methods in NQR is proposed. The distinctive feature of this product is its implementation with the application of a multi-functional programmable frequency synthesizer suitable for high-speed amplitude and phase manipulations.

The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distributed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.

The paper presents a novel implementation of a time-to-digital converter (TDC) in field-programmable gate array (FPGA) devices. The design employs FPGA digital signal processing (DSP) blocks and gives more than two-fold improvement in mean resolution in comparison with the common conversion method (carry chain-based time coding line). Two TDCs are presented and tested depending on DSP configuration. The converters were implemented in a Kintex-7 FPGA device manufactured by Xilinx in 28 nm CMOS process. The tests performed show possibilities to obtain mean resolution of 4.2 ps but measurement precision is limited to at most 15 ps mainly due to high conversion nonlinearities. The presented solution saves FPGA programmable logic blocks and has an advantage of a wider operation range when compared with a carry chain-based time coding line.

The main goal of the article is to present the concept of using a simulation environment when designing an advanced fibre-optic seismometer (FOS) using a field-programmable gate array (FPGA) computing system. The first part of the article presents the advanced requirements regarding the FOS principle of operation, as well as the measurement method using a closed-loop operation. The closed-loop control algorithm is developed using the high-level language C++ and then it is synthesised into an FPGA. The following part of the article describes the simulation environment developed to test the operation of the control algorithm. The environment includes a model of components of the measurement system, delays, and distortions in the signal processing path, and some of the measurement system surroundings. The article ends with a comparison of simulation data with measurements. The obtained results are consistent and prove correctness of the methodology adopted by the authors.

This work shows a time-domain method for the discrimination and digitization of parameters of voltage pulses coming from optical detectors, taking into account the presence of electronic noise and afterpulsing. Our scheme is based on an FPGA-based time-to-digital converter as well as an adjustable-threshold comparator complemented with commercial elements. Here, the design, implementation and optimization of a multiphase TDC using delay lines shorter than a single clock period is also described. The performance of this signal processing system is discussed through the results from the statistical code density test, statistical distributions of measurements and information gathered from an optical detector. Unlike dual voltage threshold discriminators or constant-fraction discriminators, the proposed method uses amplitude and time information to define an adjustable discrimination window that enables the acquisition of spectra.

An embedded time interval data acquisition system (DAS) is developed for zero power reactor (ZPR) noise experiments. The system is capable of measuring the correlation or probability distribution of a random process. The design is totally implemented on a single Field Programmable Gate Array (FPGA). The architecture is tested on different FPGA platforms with different speed grades and hardware resources. Generic experimental values for time resolution and inter-event dead time of the system are 2.22 ns and 6.67 ns respectively. The DAS can record around 48-bit x 790 kS/s utilizing its built-in fast memory. The system can measure very long time intervals due to its 48-bit timing structure design. As the architecture can work on a typical FPGA, this is a low cost experimental tool and needs little time to be established. In addition, revisions are easily possible through its reprogramming capability. The performance of the system is checked and verified experimentally.

This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 μs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL) and Central Processing Unit (CPU) implementation.