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Rihards Novickis

Dr. sc. ing.

Senior Researcher

Dr. Rihards Novickis is a leading researcher at the Institute of Electronics and Computer Science (EDI), head of the Integrated Circuits and Systems (ICS) Laboratory, and chair of the Scientific Council. Rihards joined EDI in 2016 as part of the Biometrics Research Group. He established the EDI Single-Chip System Research Group, which became a separate laboratory in 2024. Rihards has graduated from Riga Technical University (Riga, Latvia), where he obtained a PhD in electronics in 2022. In 2018, he received a letter of recognition from the Latvian Academy of Sciences for one of the most notable achievements in Latvian science – an original approach to transforming artificial neural network architectures into programmable logic array structures. Rihards has been involved in many international European Union projects as an executor, technical development manager, and coordinator.

Rihards considers himself a systems architect, whose work relies on intuition as much as technical knowledge. His research laboratory is investigating how to improve and incorporate into digital circuits various algorithms that have previously only existed in software format. The main goal of Rihards' research is to create new computing concepts and approaches, thereby promoting the sustainable development of our society. The research group's skills and experience have enabled it to identify three main areas of research: (1) development of next-generation sensors; (2) artificial intelligence in heterogeneous single-chip systems; and (3) computing accelerators and their integration.

In his free time, Rihards watches old movies, enjoys music of various genres, and studies its history. He is seriously involved in squash, where he still dreams of beating Latvia's top player.

Projects

2 results

Publications

  • 2025

    An Approach to Hardware-Accelerated Quantum Sensing Based on NV Centers in Diamonds(opens in a new window)

    Andrejs Cvetkovs. Gatis Supols. Rihards Novickis.

    2025 International Conference on Networking, Sensing and Control (ICNSC)

    Abstract

    Magnetometers based on the nitrogen-vacancy defect centers in diamonds are currently an active research topic due to their high sensitivity and relative ease of use compared to other similar solutions. Despite already being used by numerous research institutions, their widespread adoption is still hindered by high cost and complexity of the required associated equipment. This paper presents a hardware-accelerated measurement system implemented in FPGA chip, as well as the supporting analog electronics and software application. The developed system is capable of ODMR spectrum measurement, lock-in detection and multipoint resonance frequency tracking.

    Indexed in Scopus / WoS

  • 2025

    Real-time accelerator design for lossless compression of 1D data(opens in a new window)

    Marko Melnyk, Rihards Novickis

    2025 International Conference on Networking, Sensing and Control (ICNSC), p. 8, IEEE

    Abstract

    The increasing number of Internet of Things devices puts a strain on the available wireless communication bandwidth and consequent power consumption. However, the modular System-on-Chip architecture is well-suited for on-the-fly data processing. Subsequently, this technical work proposes an accelerator-based solution for real-time compression and decompression of one-dimensional data, which consists of an encoder, decoder, predictor, variable-to-fixed and fixed-to-variable converter digital circuits. The solution is tested, validated and characterised with multiple real and synthetic datasets. The experiments demonstrate that using even the simplest predictor type can improve communication bandwidth by up to 49% in a serialised communication scenario.

    Indexed in Scopus / WoS

    DOI(opens in a new window)
  • 2025

    A Distributed Time-of-Flight Sensor System for Autonomous Vehicles: Architecture, Sensor Fusion, and Spiking Neural Network Perception(opens in a new window)

    Edgars Lielamurs, Ibrahim Sayed, Andrejs Cvetkovs, Rihards Novickis, Anatolijs Zencovs, Maksis Celitans, Andis Bizuns, George Dimitrakopoulos, Jochen Koszescha, Kaspars Ozols

    Electronics, Vol. 14 (7), MDPI

    Abstract

    Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind zones, especially in elongated vehicles. To mitigate these challenges, we propose a distributed Time-of-Flight (ToF) sensor system with a flexible hardware–software architecture designed for multi-sensor synchronous triggering and fusion. We formalize the sensor triggering, interference mitigation scheme, data aggregation and fusion procedures and highlight challenges in achieving accurate global registration with current state-of-the-art methods. The resulting surround view visual information is then applied to Spiking Neural Network (SNN)-based object detection and probabilistic occupancy grid mapping (OGM) for enhanced environmental awareness. The proposed system is demonstrated on a test vehicle, achieving coverage of blind zones in a range of 0.5–6 m with a scalable and reconfigurable sensor mounting setup. Using seven ToF sensors, we can achieve a 10 Hz synchronized frame rate, with a 360° point cloud registration and fusion latency below 40 ms. We collected real-world driving data to evaluate the system, achieving 65% mean Average Precision (mAP) in object detection with our SNN. Overall, this work presents a replacement or addition to LiDAR in future high-level automation tasks, offering improved coverage and system integration.

    Indexed in Scopus / WoS · Scopus percentile: 60

  • 2024

    Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation(opens in a new window)

    Valērija Movčana, Arnis Strods, Karīna Narbute, Fēlikss Rūmnieks, Roberts Rimša, Gatis Mozoļevskis, Maksims Ivanovs, Roberts Kadiķis, Kārlis Gustavs Zviedris, Laura Leja, Anastasija Zujeva, Tamāra Laimiņa, Arturs Abols

    Data, Vol. 9 (2), p. 28, MDPI

    Abstract

    Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future.

    Indexed in Scopus / WoS · Scopus percentile: 75

    DOI(opens in a new window)
  • 2024

    Deviations Detection in Registered 3D Point Clouds

    Ibrahim Sayed, Andrejs Cvetkovs, Kaspars Ozols and Rihards Novickis

    2024 19th Biennial Baltic Electronics Conference (BEC, p. 1-4, IEEE

    Abstract

    This paper explores the validity of three distance metrics: a Point-to-Point distance, a Point-to-Plane distance and a Modified Hausdorff Distance, in detecting the misplacement of a Time-of-Flight camera in a registered 360-degree surround view perception system installed on a vehicle. The vehicle's undamped mechanical vibrations can unexpectedly pivot one of the cameras to an unforeseen orientation, leading to inaccurate processes that rely on correct registered point clouds. Parameterizing the camera exterior 3D orientation and synthesizing point clouds of a city model inside Blender shows that a pair of registered point clouds has a unique distance value in the camera configuration space for each metric method. This allows continuous monitoring over time and notifies the user of any sudden changes in the camera configuration.

    Indexed in Scopus / WoS