This paper explores how high-throughput phenotyping can be integrated with machine learning models to efficiently forecast the Sterility Mosaic Disease using a small amount of training data. This approach is generalized through the use of a Dynamic Bayesian Network (DBN). To predict the spread of the virus, the entire network is decomposed into several distributed and cooperative learning modules. The EM algorithm is used to learn the parameters for each module. Upon iterative convergence, the estimated hidden state vector of one module serves as input control for the next. The parameter estimates of the final module are used to formulate a predictor capable of forecasting q-days ahead.To demonstrate the effectiveness of the proposed DBN, its performance is evaluated using real-world data from ICRISAT, Patancheru, Hyderabad, Telangana, India. Physiological data was collected using 3D point cloud technology, while environmental data was recorded by a local weather station.

Structural Health Monitoring (SHM) is crucial for ensuring the safety and longevity of critical infrastructures. Traditional methods for crack detection and damage assessment are often labor intensive and time consuming, highlighting the need for advanced technologies to enhance efficiency and accuracy. This paper introduces a novel approach that integrates continual learning frameworks within multi-layer recurrent neural networks to improve parameter estimation in SHM applications. By deploying the Generalized Expectation Maximization algorithm, we address challenges associated with dynamic operational environments and inherent uncertainties in sensor data. Our methodology enables real-time monitoring and adaptive learning, allowing the model to continuously refine its predictions based on new data. We demonstrate its effectiveness in automating structural anomaly detection from accelerometer readings, significantly enhancing the reliability of damage assessment. First results indicate that our framework not only improves crack detection accuracy, but also facilitates timely interventions, contributing to a more sustainable infrastructure management.

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space.Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities.We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy.To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker’s profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.

In many applications Internet of Things (IoT) supports decision taking on the base of continuous data acquisition. These data, usually streams of sensed data, are processed and analysed to produce high-level information. The latter task is usually achieved by means of artificial intelligence technologies. Among these, continual learning is emerging as a paradigm that combines well with IoT as it matches the ability of IoT to continuously produce new data. In this context, we address continual learning with Recurrent Neural Networks (RNN) under a stochastic perspective, in which we consider the RNN as a stationary state-space network. This led us to deploy the Generalized Expectation-Maximization algorithm, in a setting suitable for IoT. We demonstrate the effectiveness of our approach by considering a case study taken from digital agriculture, in which we adopt the continual learning model to assess the biomass prediction in the field of horticulture using IoT technology. Results demonstrate that RNNs embedded in the EM framework can learn on their own after a very short training phase covering a few time samples.

Progress in Information and Communication Technologies (ICT) can make a real difference in the quality-of-life of persons with Autism Spectrum Disorder (ASD) by acting on several aspects, from customized software for communication, to emotion recognition, to social behavior and also to provide systems for the observation of the wide spectrum of manifestations, to ease the diagnosis, to support the therapy, and to monitor the improvement and the growth of children with ASD. This has been achieved by the introduction of a large number of innovative technologies, spanning from Internet of Things, to robotics, virtual and augmented reality, etc. Differently from other surveys on the same research area, we focus this survey on innovative technologies used in this field, and we organize a classification of the papers based on three different but strictly crossed axis, namely the triad of impairment (either communication, social interaction, or social behaviors), research purpose (either diagnosis or therapy), and system activity (either monitoring or intervention).

Internet of Things (IoT) has become omnipresent in many applications, such as healthcare, vehicles, and precision farming. They sense data from dozens of sensors scheduled periodically in a synchronous fashion on mobile CPUs that are forwarded to the cloud or other IoT devices via an essentially stochastic wireless channel. Hence, the task response time becomes stochastic, preventing optimization at compile time. On the other hand, knowing response time at compile time along with jitter, availability, and scalability is crucial to ensure a certain level of Quality of Service. This contribution presents a stochastic framework for performance analyses of multiapplications on a possible multiprocessor platform. When annotated with (stochastic) execution time, a traditional synchronous dataflow (SDF) graph can be transformed into a directed acyclic workflow graph, revealing the timing of individual actors. A generalized version of the rejection sampling Monte Carlo algorithm explores the properties of the workflow graph, to determine the distribution of the response time in a single application as well as a multiapplication multiple access scenario. Mean and jitter are the moments of the distribution. An IoT toy example with a number of distributed smart sensors was deployed in real environments to assess the performance of the proposed framework. Our analysis framework works at compile time of the code, scales with the number of things, and has low computational complexity.

The paper proposes a narrowband stochastic system model for auditory signal processing. The parameters are ipsilateral, contralateral and interaural hearing losses, false positive and false negative responses, and patient response time. The auditory model is then used to realize a patient simulator (artificial patient), comprising out of two microphones, a skull simulator, sound cards and a noiseless personal computer. A locally stored database contains the simulated and the recorded patient data. First field trials in an audiometric test room at the University Medical Center, Utrecht, The Netherlands, indicate that the artificial patient resembles the behavior of a real patient within a band of 10 dB-HL over the entire audiometric frequency range.

Within the context of high-data rate transmissions over time varying multipath fading channels, this article presents a new receiver design for orthogonal frequency division multiplexing (OFDM) systems. The received signal is processed jointly in time and frequency domains. A Discrete Wavelet Transform is applied to the received signal before equalization which is based on the minimum mean square error (MMSE) principle. The paper shows that the proposed receiver is able to work also in highly time-variant channels where a traditional frequency domain equalizer gets very bad performance. Moreover, it is shown that this joint time-frequency equalization is able to exploit time diversity without coding. The performance of the proposed equalizer are shown both in case of perfect channel estimation and in presence of a MMSE linear channel estimator.

This paper deals with the derivation and optimization of an iterative receiver architecture performing joint multiuser decoding and channel estimation. We consider an asynchronous multirate convolutional coded DS-CDMA system that communicates over quasi-static flat Rayleigh fading channels. The proposed receiver is derived within the space-alternating generalized expectation-maximization (SAGE) framework in connection with the noise-splitting approach. The used theoretical framework guarantees convergence of the receiver, as opposed to many other iterative receiver structures. Furthermore, the noise-splitting approach provides a set of noise-weighting coefficients that can be optimized under weak constraints. The inputs to the single-user decoders are linear combinations of two kinds of soft values with weights determined by the noise-weighting coefficients. These two kinds of soft values can be interpreted as a priori information and extrinsic information, respectively, if the channels are known. In the case of unknown channels, they are asymptotically a priori and asymptotically extrinsic, i.e., they become a priori and extrinsic when the length of the observed frame tends to infinity. In most cases, the optimum coefficients lead to extrinsic or asymptotically extrinsic values fed to the input of the single-user decoders. Monte Carlo simulations show that the proposed receiver is resistant to channel estimation errors and supports high system loads.

An iterative receiver for joint multiuser-decoding and channel-estimation of coded CDMA is derived by applying the noise-splitting approach within the space alternating generalized expectation-maximization (SAGE) framework. We consider asynchronous single-rate DS/CDMA over flat Rayleigh fading channels. The resulting receiver structure comprises partial successive interference-cancellation (SIC), channel estimation, and soft-input/hard-output maximum likelihood sequence decoding (MLSD) for each user. Additionally, one obtains a set of noise-weighting coefficients that can be freely chosen within weak constraints. These coefficients determine the amount of feedback from the decoder output to the decoder input in the subsequent iteration, and thus, "how extrinsic" the decoder output values are. The noise-weighting coefficients strongly influence the system performance. Their optimization within the SAGE framework leads to extrinsic output values in most of the cases. The proposed receiver is evaluated by Monte-Carlo simulations, and it shows two major advantages: high load is supported and convergence is guaranteed.

In this paper, we present an efficient iterative receiver architecture of tractable complexity for joint multiuser detection and channel estimation (JDE) in DS/CDMA based on the EM algorithm. The EM algorithm provides a set of free parameters called weight coefficients which can be selected to optimize its performance. Two optimality criteria are defined and analytical expressions for the corresponding optimized weight coefficients are given. Monte Carlo simulations of an asynchronous multi-rate system operating in flat Rayleigh fading channel show that the proposed receiver is near-far resistant and robust against errors in estimation of the channel parameters.

We present two efficient iterative receiver structures of tractable complexity for joint multiuser detection and multichannel estimation (JDE) of direct-sequence code-division multiple-access signals. The schemes result from an application of the expectation-maximization (EM) and the space-alternating generalized expectation-maximization (SAGE) algorithms, respectively. The EM-JDE receiver updates the data bit sequences in parallel, while the SAGE-JDE receiver reestimates them successively. The channel parameters are updated in parallel in both schemes. The EM algorithm provides a set of free parameters, called weight coefficients, which can be selected to optimize its performance. Two optimality criteria are defined and analytical expressions for the corresponding optimized weight coefficients are given. Monte-Carlo simulations of a synchronous scenario show that the proposed JDE receivers have excellent multiuser efficiency and are robust against errors in the estimation of the channel parameters. Moreover, very short training sequences are required for the JDE schemes to converge. Simulation results further demonstrate that the SAGE-JDE receiver exhibits a better performance when the users' bit sequences are updated in the order of increasing signal strength, i.e., the bit sequence of the user with the weakest signal strength is updated first at each stage.

We present a method of tractable complexity for joint data detection and channel estimation of DS/CDMA signals. The scheme results from an application of the SAGE algorithm. Monte Carlo simulations of a synchronous scenario show that the method is near-far resistant and that it is robust against estimation errors of the channel parameters. Moreover, very short training sequences are required for the scheme to converge. Simulation results further demonstrate that a better performance is obtained when the users' bit sequences are cyclically updated in the order of increasing strength of the users' signals at the output of the whitening filter in the receiver.