EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.Comment: Accepted by 30th International Conference on Neural Information Processing (ICONIP 2023

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Adaptive Selection Problems in Networked Systems

Networked systems are composed of interconnected nodes that work collaboratively to maximize a given overall utility function. Typical examples of such systems are wireless sensor networks (WSNs) and participatory sensing systems: sensor nodes, either static or mobile, are deployed for monitoring a certain physical field. In these systems, there are a set of problems where we need to adaptively select a strategy to run the system, in order to enhance the efficiency of utilizing the resources available to the system. In particular, we study four adaptive selection problems as follows. We start by studying the problem of base-station (BS) selection in WSNs. Base stations are critical sensor nodes whose failures cause severe data losses. Deploying multiple fixed BSs improves the robustness, yet this scheme is not energy efficient because BSs have high energy consumptions. We propose a scheme that selects only one BS to be active at a time; other BSs are kept passive and act as regular sensor nodes. This scheme substantially reduces the energy supplies required by individual BSs. Then, we propose an algorithm for adaptively selecting the active BS so that the spatially and temporally varying energy resources are efficiently utilized. We also address implementation issues and apply the proposed algorithm on a real WSN. Field experiments have shown the effectiveness of the proposed algorithm. We generalize the BS selection problem by considering both the energy efficiency of regular sensor nodes and that of BSs. In this scheme, a subset of active BSs (instead of only one) is adaptively selected and the routing of regular sensor nodes is adjusted accordingly. Because BSs have high fixed-energy consumptions and because the number of candidate subsets of active BSs is exponential with the number of BSs, this general BS selection problem is NP-hard. We propose a polynomial-time algorithm that is guaranteed, under mild conditions, to achieve a network lifetime at least 62% of the optimal one. Through extensive numerical simulations, we verify that the lifetime achieved by the proposed algorithm is always very close to the optimum. We then study the problem of scheduling the sparse-sensing patterns in WSNs. We observe that the traditional scheme of periodically taking sensing samples is not energy efficient. Instead, we propose to adaptively schedule when and where to activate sensors for sampling a physical field, such that the energy efficiency is enhanced and the sensing precision is maintained. The schedules are learnt from the temporal signal models derived from the collected measurements. Then, using the obtained signal models and the sparse sensing-measurements, the original signal can be effectively recovered. This proposed method requires minimal on-board computation, no inter-node communications and achieves an appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse-sensing schemes, particularly when the measured data is characterized by a strong temporal correlation. In the last part of the thesis, we discuss the sparse-sensing framework by exploiting the spatial correlations rather than the temporal correlations among the captured measurements. In this framework, application-specific utility functions can be employed. By adaptively selecting a small subset of active sensors for sensing, a certain utility is guaranteed and the efficiency of the sensing system is enhanced. We apply this framework both in static WSNs and participatory sensing systems where sensors move in an uncoordinated manner. Through extensive simulations, we show that our proposed algorithm enhances the resource efficiency

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Design and Development of a Human Gesture Recognition System in Tridimensional Interactive Virtual Environment

This thesis describes the design and the development of a recognition system for human gestures. The main goal of this work is to demonstrate the possibility to extract enough information, both semantic and quantitative, from the human action, to perform complex tasks in a virtual environment. To manage the complexity and the variability adaptive systems are exploited, both in building a codebook (by unsupervised neural networks), and to recognize the sequence of symbols describing a gesture (by Hidden Markov models)

Agricultural Monitoring System using Images through a LPWAN Network

Internet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Gobernación de Boyacá, Colfuturo, Colciencias, Universidad Santo Tomás, Pontificia Universidad JaverianaInternet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0002-3554-1531https://scholar.google.com/citations?user=5_dx9REAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.d

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks