Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Adaptive Boltzmann Medical Dataset Machine Learning

The RBM is a stochastic energy-based model of an unsupervised neural network (RBM). RBM is a key pre-training for Deep Learning. Structure of RBM includes weights and coefficients for neurons. Better network structure allows us to examine data more thoroughly, which is good. We looked at the variance of parameters in learning on demand to fix the problem. To determine why RBM's energy function fluctuates, we'll look at its parameter variance. A neuron generation and annihilation algorithm is smeared with an adaptive RBM learning method to determine the optimal number of hidden neurons for attribute imputation during training. When the energy function isn't converged and parameter variance is high, a hidden neuron is generated. If the neuron doesn't disrupt learning, it'll destroy the hidden neuron. In this study, some yardstick PIMA data sets were tested

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Convolutional neural networks for hand gesture recognition with off-the-shelf radar sensor

openL'elaborato espone l'attività di ricerca riguardante l'applicazione di metodologie di Machine Learning per risolvere un problema di interazione uomo-macchina. L'obiettivo è riconoscere e classificare correttamente dei movimenti della mano eseguiti da un utente, i quali vengono catturati tramite un sensore radar. Il segnale viene successivamente processato e dato in input ad una rete neurale convoluzionale, seguita da un classificatore volto a riconoscere il movimento che viene eseguito.The thesis explains the research and metholodogies applied in order to solve a Human-Computer Interaction task by means of Machine Learning techniques. The goal is to recognize and classify hand gestures performed by the user, which are acquired with a radar sensor. The signal is then processed and given as input to a convolutional neural network, followed by a fully connected classifier that should be able to classify correctly the movement

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Artificial Neural Networks for Microwave Detection

Microwave detection techniques based on the theory of perturbation of cavity resonators are commonly used to measure the permittivity and permeability of objects of dielectric and ferrite materials at microwave frequencies. When a small object is introduced into a microwave cavity resonator, the resonant frequency is perturbed. Since it is possible to measure the change in frequency with high accuracy, this provides a valuable method for measuring the electric and magnetic properties of the object. Likewise, these microwave resonators can be used as sensors for sorting dielectric objects. Techniques based upon this principle are in common use for measuring the dielectric and magnetic properties of materials at microwave frequencies for variety of applications. This thesis presents an approach of using Artificial Neural Networks to detect material change in a rectangular cavity. The method is based on the theory of the perturbation of cavity resonators where a change in the resonant frequencies of the cavity is directly proportional to the dielectric constant of the inserted objects. A rectangular cavity test fixture was built and excited with a monopole antenna. The cavity was filled with different materials, and the reflection coefficient of each material was measured over a wide range of frequencies. An intelligent systems approach using an artificial neural network (ANN) methodology was implemented for the automatic material change detection. To develop an automatic detection model, a multi-layer perceptron (MLP) was designed with one hidden layer and gradient descent back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode, and after the training process was accomplished, the model was able to learn the rules without knowing any algorithm for automatic detection