CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

How Rapid is Rapid Prototyping? Analysis of ESPADON Programme Results

New methodologies, engineering processes, and support environments are beginning to emerge for embedded signal processing systems. The main objectives are to enable defence industry to field state-of-the-art products in less time and with lower costs, including retrofits and upgrades, based predominately on commercial off the shelf (COTS) components and the model-year concept. One of the cornerstones of the new methodologies is the concept of rapid prototyping. This is the ability to rapidly and seamlessly move from functional design to the architectural design to the implementation, through automatic code generation tools, onto real-time COTS test beds. In this paper, we try to quantify the term “rapid†and provide results, the metrics, from two independent benchmarks, a radar and sonar beamforming application subset. The metrics show that the rapid prototyping process may be sixteen times faster than a conventional process

From MARTE to Reconfigurable NoCs: A model driven design methodology

Due to the continuous exponential rise in SoC's design complexity, there is a critical need to find new seamless methodologies and tools to handle the SoC co-design aspects. We address this issue and propose a novel SoC co-design methodology based on Model Driven Engineering and the MARTE (Modeling and Analysis of Real-Time and Embedded Systems) standard proposed by Object Management Group, to raise the design abstraction levels. Extensions of this standard have enabled us to move from high level specifications to execution platforms such as reconfigurable FPGAs. In this paper, we present a high level modeling approach that targets modern Network on Chips systems. The overall objective: to perform system modeling at a high abstraction level expressed in Unified Modeling Language (UML); and afterwards, transform these high level models into detailed enriched lower level models in order to automatically generate the necessary code for final FPGA synthesis

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms

This article covers radar signal processing for sensing in the context of assisted living (AL). This is presented through three example applications: human activity recognition (HAR) for activities of daily living (ADL), respiratory disorders, and sleep stages (SSs) classification. The common challenge of classification is discussed within a framework of measurements/preprocessing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the three applications from a signal processing standpoint are detailed in their specific data processing and ad hoc classification strategies. Here, the focus is on recent trends in the field of activity recognition (multidomain, multimodal, and fusion), health-care applications based on vital signs (superresolution techniques), and comments related to outstanding challenges. Finally, this article explores challenges associated with the real-time implementation of signal processing/classification algorithms

Empirical Comparison of Chirp and Multitones on Experimental UWB Software Defined Radar Prototype

This paper proposes and tests an approach for an unbiased study of radar waveforms' performances. Using the ultrawide band software defined radar prototype, the performances of Chirp and Multitones are compared in range profile and detection range. The architecture was implemented and has performances comparable to the state of the art in software defined radar prototypes. The experimental results are consistent with the simulations