Vehicle and pedestrian detection (VaPD) is one of the most critical tasks in an advanced driver assistance system which help the driver to drive safely and save the pedestrian life. VaPD is a typical object detection problem that requires a trade-off among accuracy, speed, and memory consumption. Most existing methods focus on improving detection accuracy, while ignoring VaPD requires real-time detection speed with limited computational resources. Thus, it is of primary importance to study light-weight and real-time VaPD methods for embedded devices, that is hardware platforms with limited computation and memory resources. To deal with these issues, this paper proposes a low-rank (LR) Tiny YOLO v3 architecture that meets the requirements of real-time VaPD on embedded systems. The architecture has been developed starting from Tiny YOLO v3 adopting a convolutional neural network compression technique based on Tucker tensor decomposition, able to reduce the computational complexity of the network. A wide experimentation has been carried out on two embedded platforms, Raspberry Pi 4 and NVIDIA Jetson Nano 2 GB, and two datasets commonly used for VaPD, PASCAL VOC and KITTI dataset, showing the superiority of the LR Tiny YOLO v3 with respect to the state-of-the-art networks in obtaining the best compromise between inference time, accuracy and memory occupancy. Moreover, the proposed architecture meets the requirements of VaPD on embedded systems using only 22% of the memory required by the baseline Tiny YOLO v3 Darknet, and always providing better inference time (36.46 FPS) with only a marginal decrease in accuracy ( $\sim$ 2%).

Esca is one of the most common grape leaf diseases that seriously affect grape yield, causing a loss of global production in the range of 20%-40%. Therefore, a timely and effective identification of the disease could help to develop an early treatment approach to control its spread while reducing economic losses. For this purpose the use of computer vision and machine learning techniques for recognizing plant diseases have been extensively studied in recent years. The aim of this paper is to propose an image detector based on a high-performance convolutional neural network (CNN) implemented in a low cost, low power platform, to monitor the Esca disease in real-time. To meet the severe constraints typical of an embedded system, a new low-rank CNN architecture (LR-Net) based on CANDECOMP/PARAFAC (CP) tensor decomposition has been developed. The compressed CNN network so obtained has been trained on a specific dataset and implemented in a low-power, low-cost Python programmable machine vision camera for real-time classification. An extensive experimentation has been conducted and the results achieved show the superiority of LR-Net with respect to the state-of-the-art networks both in terms of inference time and memory occupancy.

Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

Road Condition Monitoring is a critical task for the management and maintenance of the pavement network infrastructure by the authorities. The application of Artificial Intelligence (AI) techniques in this domain has experienced a significant growth in the recent years, driven by the continual advancement in AI algorithms. This paper presents a comprehensive review of the latest advancements in Road Condition Monitoring approaches using AI methods, in particular Deep Learning techniques, focusing on works published from 2020 onwards. It discusses novel approaches that have not been fully explored in existing literature reviews. The literature review was conducted by categorizing studies based on the type of signal data, distinguishing between acoustic, vibrational, and vision-based approaches. For each data type, the paper examines and discuss the latest advancements and improvements achieved through AI techniques, providing an overview of future directions and identifying open challenges in the field.

Real-time semantic segmentation on embedded devices has recently enjoyed significant gain in popularity, due to the increasing interest in smart vehicles and smart robots. In particular, with the emergence of autonomous driving, low latency and computation-intensive operations lead to new challenges for vehicles and robots, such as excessive computing power and energy consumption. The aim of this paper is to address semantic segmentation, one of the most critical tasks for the perception of the environment, and its implementation in a low power core, by preserving the required performance of accuracy and low complexity. To reach this goal a low-rank convolutional neural network (CNN) architecture for real-time semantic segmentation is proposed. The main contributions of this paper are: i) a tensor decomposition technique has been applied to the kernel of a generic convolutional layer, ii) three versions of an optimized architecture, that combines UNet and ResNet models, have been derived to explore the trade-off between model complexity and accuracy, iii) the low-rank CNN architectures have been implemented in a Raspberry Pi 4 and NVIDIA Jetson Nano 2 GB embedded platforms, as severe benchmarks to meet the low-power, low-cost requirements, and in the high-cost GPU NVIDIA Tesla P100 PCIe 16 GB to meet the best performance.