Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Space-Air-Ground Integrated Network: A Survey

Consider a discrete source producing a sequence of message letters from a finite alphabet. A single-letter distortion measure is given by a non-negative matrix (d ij ). The entryd ij measures the ?cost? or ?distortion? if letteriis reproduced at the receiver as letterj. The average distortion of a communications system (source-coder-noisy channel-decoder) is taken to bed= ? i.j P ij d ij whereP ij is the probability ofibeing reproduced asj. It is shown that there is a functionR(d) that measures the ?equivalent rate? of the source for a given level of distortion. For coding purposes where a leveldof distortion can be tolerated, the source acts like one with information rateR(d). Methods are given for calculatingR(d), and various properties discussed. Finally, generalizations to ergodic sources, to continuous sources, and to distortion measures involving blocks of letters are developed. In this paper a study is made of the problem of coding a discrete source of information, given afidelity criterionor ameasure of the distortionof the final recovered message at the receiving point relative to the actual transmitted message. In a particular case there might be a certain tolerable level of distortion as determined by this measure. It is desired to so encode the information that the maximum possible signaling rate is obtained without exceeding the tolerable distortion level. This work is an expansion and detailed elaboration of ideas presented earlier [1], with particular reference to the discrete case. We shall show that for a wide class of distortion measures and discrete sources of information there exists a functionR(d) (depending on the particular distortion measure and source) which measures, in a sense, the equivalent rateRof the source (in bits per letter produced) whendis the allowed distortion level. Methods will be given for evaluatingR(d) explicitly in certain simple cases and for evaluatingR(d) by a limiting process in more complex cases. The basic results are roughly that it is impossible to signal at a rate faster thanC/R(d) (source letters per second) over a memoryless channel of capacityC(bits per second) with a distortion measure less than or equal tod. On the other hand, by sufficiently long block codes it is possible to approach as closely as desired the rateC/R(d) with distortion leveld. Finally, some particular examples, using error probability per letter of message and other simple distortion measures, are worked out in detail.

Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959.

In the latest years we have been witnessing an evolution of the technological framework thanks to the Internet of Things paradigm, which is enabling innovative smart services for many different applications. The healthcare system is among the main scenarios that are benefiting from this new trend, thus giving rise to the concept of eHealth. In this framework we propose SmartPANTS, an IoT-based wireless system specifically designed for the rehabilitation of lower limbs. Our system is conceived as a prototype of a medical platform to be employed during the physical therapy for patients recovering from a brain stroke condition. SmartPANTS includes a signal processing and machine learning algorithm able to automatically recognize the type of exercise the patient is performing, and to provide real-time feedback on the execution. Moreover, experimental tests show that our platform is able to estimate the execution time of the different exercises, providing values very close to real ones. Performance results show that the SmartPANTS system is able to correctly identify the type of exercise currently being performed with an accuracy of about 99%.

Towards IoT-Based eHealth Services: A Smart Prototype System for Home Rehabilitation

In this paper a method to realize the joint application layer coding for image transmission over deep space channels has been presented. In more technical detail, both image compression, based on algorithms such as JPEG2000 and CCSDS, and encoding techniques, such as LDPC codes, to protect the sent images are simultaneously applied by the proposed mechanism. It acts on the bases of the deep space channel conditions, in terms of Bit Error Rate, and it is based on the Multi-Attribute Decision Making theory. In practice, the proposal is aimed at protecting the essential informative contents of images sent through a deep space network and, at the same time, allows minimizing the load offered (the total amount of data to transmit) by the overall application layer coding process to the deep space network. The presented mechanism has been tested through simulations. The obtained results show the effectiveness of the proposal and open the door to further developments of the method in real systems.

https://www.scnl.diten.unige.it/publications/Globecom2011_2paper.pdf

Application Layer Joint Coding for Image Transmission over Deep Space Channels

Information related to recognizing the place in which a user is has become crucial to deliver efficient and tailored Location-Based Services (LBSs). Though plenty of solutions exist for recognizing indoor places, almost no idea is present in the literature for recognizing big outdoor places without the GPS employment. Though these solutions assure great accuracy they also have a strong request in terms of energy necessary to achieve such result. This paper proposes a Place of Interest (POI) recognition algorithm called Enhanced-LRACI. It is an evolution of LRACI (Location Recognition Algorithm for automatic Check-In applications), a former work reported in [1]. E-LRACI aims at recognizing big outdoor places only by using WiFi Access Points (APs) over mobile devices (smartphones). The main contributions of this work are: i) it solves the problem of big outdoor POI recognition without using GPS by leveraging on the concept of spot, ii) it proposes a novel fingerprint (FP) algorithm and iii) it outperforms the results obtained by other reference works in terms of recognition accuracy. Performance investigation reported in this paper, carried out on real data acquired over mobile devices (Android smartphones), shows that our proposal is able to correctly recognize big outdoor POIs in 95&#x0025; of the cases whereas other state of the art papers do not exceed 89&#x0025;.

Outdoor Places of Interest Recognition with WiFi Fingerprint Over Mobile Devices

Given the extreme diffusion of Alzheimer’s disease (AD) and Parkinson’s disease (PD), the necessity for a solution to early detect neurological symptoms of such diseases strongly arose. According to the medical literature, such early detection can be achieved by exploiting the correlation between PD and AD and some external symptoms: the Essential Tremor (ET) and the number of Eye Blinks (EBs). In this paper we present NeuroGlasses, a prototype of sensored glasses able to detect the presence of ET of the head and to count the number of EBs at the same time. To the best of authors’ knowledge this is the first attempt to monitor such external symptoms with a transparent and wearable device without any a-priori training. Numerical results prove the reliability of the proposed approach: NeuroGlasses are able to i) correctly recognize the ET with an overall accuracy above 97% and ii) count the number of EBs with a Root Mean Square Error (RMSE) around 0.4.

NeuroGlasses: A Wearable Prototype for Early Detection of Neurological Symptoms

Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09893814.pdf

Igor Bisio

Papers

Towards IoT-Based eHealth Services: A Smart Prototype System for Home Rehabilitation

Application Layer Joint Coding for Image Transmission over Deep Space Channels

Outdoor Places of Interest Recognition with WiFi Fingerprint Over Mobile Devices

NeuroGlasses: A Wearable Prototype for Early Detection of Neurological Symptoms

A Systematic Review of Drone Based Road Traffic Monitoring System