A graph signal filtering-based approach for detection of different edge types on airborne lidar data

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

Architecture of a Real-Time Platform Independant GPS L1 Software Receiver

Personal digital assistants or mobile phones applications are not anymore restricted to multimedia or wireless communications, but have been extended to handle Global Positioning System (GPS) functionalities. Consequently, the growing market of GPS capable mobile devices is driving the interest of software receiver solutions as they provide several advantages with respect to traditional hardware implementations. First, they share the same system resources such as the processor, embedded memory and power with other system units, reducing both the size and the costs of their integration. Second, they can be easily reprogrammed – via a firmware update – for incorporating the latest developments, such as the exploitation of the future satellites signals or some improved multipath mitigation techniques. Finally, they offer a more flexible solution for rapid research and development as compared to conventional hardware receivers where the chip design is fixed and obtained after a long integration process. With the increasing performance of modern processors, it becomes now feasible to implement in software a multi-channel GPS receiver operating in real-time. However, a major problem with the software architecture is the large computing resources required for the digital signal processing. Former studies have demonstrated that a straightforward transposition of traditional hardware based architectures into software would lead to an amount of integer operations which is not suitable for today's fastest computers. From this observation, several strategies have been proposed in the literature in order to reduce the complexity of the receiver operations. The first one relies on the utilization of advanced microprocessor instructions set which provides the capability of processing vectors of data by operating on multiple integer values at the same time. This results in significant gains in execution speed, but also severely limits the portability of the code, since the operations are tied to specific processors architectures. Another alternative consists in exploiting the native bitwise representation of the signal. The data bits are stored in separate vectors on which logical parallel operations can be performed. The objective is to take advantage of the universality, high parallelism, and speed of the bitwise operations for which a single integer operation translates into a few simple parallel logical relations. However, the inherent drawback of the bitwise processing is the lack of flexibility as the complexity becomes bit-depth dependent. This thesis has been carried out in the framework of a two-year industrial project (2007-2009) in collaboration with U-blox AG in Thalwil. It aimed to the realization of a multi-channel, platform-independent real-time GPS L1 software receiver. The main challenge of this project consisted in providing real-time performances while keeping the portability of the code to make the receiver suitable for any type of software implementation. In that sense, new techniques and algorithms have been developed for optimizing the processing chain in order to lower the processor load. The main idea consists in regrouping data which share the same characteristics, and process them in batches instead of sequentially. This way, it becomes possible to progressively reduce the data throughput and consequently the amount of operations to perform. A completely new receiver architecture has been proposed and validated through the realization of a functional prototype, thus demonstrating the feasibility of the concept.

EPFL2010

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature values in different geographical locations, information diffusion in social networks, functional activities in the brain, or 3D meshes in computer graphics. The representation, analysis, and compression of such data is a challenging task and requires the development of new tools that can identify and properly exploit the data structure. In this thesis, we formulate the processing and analysis of structured data using the emerging framework of graph signal processing. Graphs are generic data representation forms, suitable for modeling the geometric structure of signals that live on topologically complicated domains. The vertices of the graph represent the discrete data domain, and the edge weights capture the pairwise relationships between the vertices. A graph signal is then defined as a function that assigns a real value to each vertex. Graph signal processing is a useful framework for handling efficiently such data as it takes into consideration both the signal and the graph structure. In this work, we develop new methods and study several important problems related to the representation and structure-aware processing of graph signals in both centralized and distributed settings. We focus in particular in the theory of sparse graph signal representation and its applications and we bring some insights towards better understanding the interplay between graphs and signals on graphs. First, we study a novel yet natural application of the graph signal processing framework for the representation of 3D point cloud sequences. We exploit graph-based transform signal representations for addressing the challenging problem of compression of data that is characterized by dynamic 3D positions and color attributes. Next, we depart from graph-based transform signal representations to design new overcomplete representations, or dictionaries, which are adapted to specific classes of graph signals. In particular, we address the problem of sparse representation of graph signals residing on weighted graphs by learning graph structured dictionaries that incorporate the intrinsic geometric structure of the irregular data domain and are adapted to the characteristics of the signals. Then, we move to the efficient processing of graph signals in distributed scenarios, such as sensor or camera networks, which brings important constraints in terms of communication and computation in realistic settings. In particular, we study the effect of quantization in the distributed processing of graph signals that are represented by graph spectral dictionaries and we show that the impact of the quantization depends on the graph geometry and on the structure of the spectral dictionaries. Finally, we focus on a widely used graph process, the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that depends on the graph topology and exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm.

EPFL2016

System-level Design of Adaptive Wearable Sensors for Health and Wellness Monitoring

Elisabetta De Giovanni

In recent years, population aging and the consequent higher incidence of noncommunicable diseases, have increased the need for long-term health monitoring. Moreover, as healthcare cost is projected to grow substantially by 2030 in OECD countries, the demand for portable, easy-to-use and low cost ultra-low power means of monitoring, diagnosis and prevention rises. Wearable sensors technology for remote health and wellness monitoring is an optimal candidate to tackle this problem and has advanced drastically in the last years. However, wearable sensors impose several design constraints. They must process data in real-time and provide highly accurate diagnosis and adapt to different cases. At the same time, to perform long-term monitoring, they must maximize battery lifetime, hence, usability. However, the need for more accurate algorithms and the need to obtain energy-efficient implementations can work against each other. For this reason, enhancing the energy-accuracy trade-off is essential. Several works in the literature have addressed the energy-accuracy trade-off problem. The general approach is to first develop offline methodologies that maximize the algorithm's accuracy, using signal processing and machine learning. Then, these methods are optimized to be implemented as online designs in resource-constrained ultra-low power platforms. However, different problems can occur in these two steps. Most methods use highly variable datasets (mainly having different subjects), others use fixed parameters tailored to specific conditions, which overall decreases the robustness of the algorithm. In traditional single-core devices, some optimizations whose goal is to lower the algorithms' complexity and computational burden, such as downsampling and features reduction, lead to a loss in precision. With the advances of ultra-low power platforms and new machine learning strategies, even more challenges arise. However, these advances allow to exploit the growing capabilities of the platforms and use innovative and more complex strategies that achieve high levels of accuracy, robustness and energy-efficiency. In this thesis, I propose a set of adaptive strategies in the context of remote health and wellness monitoring for an enhanced energy-accuracy trade-off in wearable sensors. First, I present three methodologies for multi-biosignal monitoring and pathology detection, which adapt to the specific physiological conditions by means of personalization to the subject and knowledge acquired from the signal. Second, in the context of modern heterogeneous wearable platforms, I propose a modular approach to software parallelization and hardware acceleration for biomedical applications to maximize the attainable speed-up and, therefore, minimizing energy consumption. Moreover, I propose an approach to scale computing resources and independent memory banks based on the specific characteristics of the patient in modern wearable sensors. Finally, in the context of intensive physical exercise, I propose an online design that adapts to the sudden physiological changes occurring in the signal. This method combines a lightweight algorithm with a more robust though more complex one to reduce energy consumption while maintaining a very high accuracy. Moreover, this adaptive strategy exploits the heterogeneity of modern platforms by matching the complexity of each algorithm with the capabilities of each core, which further enhances the energy-accuracy trade-off.

EPFL2021