Jari Nurmi

Tampere University | UTA · Department of Electrical Engineering

The popularity of mobile robots in factories, warehouses, and hospitals has raised safety concerns about human-machine collisions, particularly in non-line-of-sight (NLoS) scenarios such as corners. Developing a robot capable of locating and tracking humans behind the corners will greatly mitigate risk. However, most of them cannot work in complex...

Radio direction finding, traditionally used for localizing radio signal sources, has been adapted for Bluetooth to enable indoor localization of wireless devices. This adaptation is particularly relevant for achieving accurate indoor localization within Internet of Things (IoT) networks, especially in battery-powered and resource-limited embedded s...

Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial...

Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density re...

High-fan-in dot product computations are ubiquitous in highly relevant application domains, such as signal processing and machine learning. Particularly, the diverse set of data formats used in machine learning poses a challenge for flexible efficient design solutions. Ideally, a dot product summation is composed from a carry-free compressor tree f...

A Global Navigation Satellite System (GNSS) is widely used today for both positioning and timing purposes. Many distinct receiver chips are available as Application-Specific Integrated Circuit (ASIC)s off-the-shelf, each tailored to the requirements of various applications. These chips deliver good performance and low energy consumption but offer c...

This special section is composed of substantially extended handpicked papers from the IEEE Nordic Circuits and Systems Conference (NorCAS) 2022 that took place in October 2022 in Oslo, Norway.

This research investigates an affordable, energy-efficient Direction-of-Arrival (DOA)-based localization solution for Digital Enhanced Cordless Telecommunications (DECT) 2020 New Radio (NR), a new standard lacking a native positioning feature. This standard enables Massive Internet of Things (IoT) networks, a vast 5G network interconnecting an unpa...

Machine Learning (ML) is ubiquitous in contemporary applications. Its need for efficient acceleration has driven vast research efforts into the quantization of neural networks with low-precision numerical formats. Models quantized with minifloat formats of eight or fewer bits have proven capable of outperforming models quantized into same-size inte...

Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are he...

The state-of-the-art Android environment, available on a major market share of smart-phones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medi...

Diabetes is one such chronic disease that, if undetected, can result in several adverse symptoms or diseases. It requires continuous and active monitoring, for example, by using various smartphone sensors, wearable/smart watches, etc. These devices are minimally invasive in nature and can also track various physiological signals, which are importan...

Many popular applications show resilience to computational errors. Approximate Computing (AxC) exploits this to reduce their execution time and energy consumption by introducing approximations in software and hardware. Using AxC raises new challenges to ensure that hardware designs satisfy their demands before deployment, which hardware designers a...

The demand for indoor location-based services and the wide availability of mobile devices have triggered research into new positioning systems able to provide accurate indoor positions using smartphones. However, accurate solutions require a complex implementation and long-term maintenance of their infrastructure. Collaborative systems may help to...

The popularity of the Internet of Things and next-generation wireless networks calls for a greater distribution of small but high-performance and energy-efficient compute devices at the networks’ Edge. These devices must integrate hardware acceleration to meet the latency requirements of relevant use cases. Existing work has highlighted Coarse-Grai...

Modern Edge Computing devices execute applications that must meet strict latency requirements as per traditional standardization activities. Achieving the needed performance implies a need for efficiency in all aspects, thus, flexible solutions are needed. In this Ph.D. project, we address this issue for error-tolerant applications by using Coarse-...

The trailblazing development in mobile and wearable-based gaming dictates both the support of new technology enablers to allow for current demand and the development of modern computational offload-ing strategies to decrease the energy of handheld devices and maintain the energy emissions caused both by computation and transmission of data. Modern...

As modern 5G systems are being deployed, researchers question whether they are sufficient for the oncoming decades of technological evolution. Growing numbers of interconnected intelligent devices put these networks under tremendous pressure, demanding their development. Paving the way for beyond 5G and 6G systems, commonly denoted by B5G herein, t...

Autonomous driving services rely heavily on sensors such as cameras, LiDAR, radar, and communication modules. A common practice of processing the sensed data is using a high-performance computing unit placed inside the vehicle, which deploys AI models and algorithms to act as the brain or administrator of the vehicle. The vehicular data generated f...

With the miniaturization of electronics, Global Navigation Satellite Systems (GNSS) receivers are getting more and more embedded into devices with harsh energy constraints. This process has led to new signal processing challenges due to the limited processing power on battery-operated devices and to challenging wireless environments, such as deep u...

This paper evaluates the implementation of a low-complexity adaptive full DS-KF for robust tracking of GNSS signals. The full DS-KF includes FLL, DLL, and PLL tracking schemes. The DS-KF implementation in real-time applications requires a high computational cost. Additionally, the DS-KF performance decays in time-varying scenarios where the statist...

This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction- finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batterie...

Direction of Arrival (DOA) methods are found in many applications, and in the case of the Internet of Things (IoT), it is used for indoor localization. However, the implementation of DOA in IoT devices poses a real challenge, since they are computationally expensive complex numerical methods that could easily lead to resource starvation, unacceptab...

Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary f...

Indoor positioning based on machinelearning models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semi-supervised, and unsupervised models have thus been widely used in this field not only to estimate the user position but also to compress, clean, and denoise fingerprinting datasets. S...

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, p...

The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said...

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, while enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the...

The development of new processing algorithms has always been at the centre of satellite navigation research, aiming to minimize complexity and maximize the quality of the results. Often, these two objectives cannot be achieved simultaneously; thus, identifying the necessary trade-offs are required. Over the last decade, the development of new GNSS...

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-...

Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feeli...

Angle-of-Arrival (AoA) methods are an Internet of Things (IoT) application, which could be used, for example, in indoor localization. Anchor nodes have an array of antennas and could send the data via Ethernet cable to the cloud that calculates AoA. However, having cable connections means high installation costs, and constantly transferring big chu...

Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recent collaborative inference frameworks have adopted dynamic inference methodologies such as early-exit and run-time partitioning of...

This paper evaluates the implementation of a low-complexity adaptive direct-state Kalman filter (DSKF) for robust carrier phase tracking of global navigation satellite system (GNSS) signals. This architecture consists of a loop-bandwidth control algorithm (LBCA)-based lookup table (LUT)-DSKF in an FLL-assisted-PLL (FAP) tracking scheme. The FAP con...

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this...

Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor positioning system to ensure the data quality and provide a high Quality of Service (QoS) to the end-user. In this...

Collaborative deep learning inference between low-resource endpoint devices and edge servers has received significant research interest in the last few years. Such computation partitioning can help reducing endpoint device energy consumption and improve latency, but equally importantly also contributes to privacy-preserving of sensitive data. This...

The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms offer different complexity to the system. In this work, we propose a fingerprinting positioning method for mult...

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Anothe...

Despite the recent advances in semiconductor technology and energy-aware system design, the overall energy consumption of computing and communication systems is rapidly growing. On the one hand, the pervasiveness of these technologies everywhere in the form of mobile devices, cyber-physical embedded systems, sensor networks, wearables, social media...

Indoor localization is a growing research field and interest is expanding in many application fields, including services, measurement, mapping, security, and standardization. The quest for appropriate tracking technologies for COVID-19 pandemic control has shown us the importance of identifying the sensors data and processing that are suitable, acc...

As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated...

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limit...

This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve t...

Indoor Positioning based on wifi fingerprinting needs a reference dataset, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the dataset and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing position...

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisatio...

Nowadays, several indoor positioning solutions support Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large num...

The dynamic behavior of frequency-modulated continuous-wave (FMCW) interferences i.e., chirp interference) makes them challenging to mitigate. An adaptive notch filter (ANF) determines the instantaneous frequency and removes the interference signal. However, the adaption algorithm may either be too slow to accommodate the signal agility or too fast...

The development of small form-factor handheld electronics is pacing the personal devices market, followed by the increasing number of various applications. Some of those applications also cover computation-hungry use-cases, such as image or video processing and compression, among others. Historically, wearable and handheld devices were not designed...

This paper presents a cognitive radio architecture incorporating machine learning into its cognition engine. Both the cognition engine and the software-defined transmitter and receiver chains make use of reconfigurable technologies to enable adaptation to the radio operating environment.

The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and...

In this digitized world, data has become an integral part in any domain, including healthcare. The healthcare industry produces a huge amount of digital data, by utilizing information from all sources of healthcare, including the patients’ demographics, medications, vital signs, physician’s observations, laboratory data, billing data, data from var...

Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently,...

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety...

Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this work we introduce a lossy compression method for time-series data,...

GNSS receivers use tracking loops to lock onto GNSS signals. Fixed loop settings limit the tracking performance against noise, receiver dynamics, and the current scenario. Adaptive tracking loops adjust these settings to achieve optimal performance for a given scenario. This paper evaluates the performance and complexity of state-of-the-art adaptiv...

The propagation of light underwater is tied closely to the optical properties of water. In particular, the underwater channel imposes attenuation on the optical signal in the form of scattering, absorption, and turbulence. These attenuation factors can lead to severe spatial and temporal dispersion, which restricts communication to a limited range...

Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computati...

IoT devices and wearables may rely on Wi-Fi finger-printing to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dime...