Ennio Gambi

A new chaotic algorithm for video encryption

We present a new encryption algorithm for the video signal that, employing suitably arranged chaotic functions, allows to increase the security level while maintaining acceptable processing times. We analyze the performance of the new scheme either though simulation or via practical implementation, thus demonstrating its efficiency, also in comparison with previous proposals.

A Depth-Based Fall Detection System Using a Kinect® Sensor

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Coupled-soliton photonic logic gates: practical design procedures

The feasibility of and, or, and ex-or functions based on the interaction of optical solitons is proved by use of a five-layer dielectric structure with a nonlinear core. With the exception of the or logic gate, the design of these devices is rather flexible, offering a wide variety of choices with respect to both the geometrical parameters and the input power levels.

Chaos-Based Radars for Automotive Applications: Theoretical Issues and Numerical Simulation

This correspondence focuses on a possible application of chaotic signals as an alternative to more conventional spreading schemes in direct-sequence spread spectrum (DS-SS) automotive radars, the latter being a key component for future road safety systems. Due to their very good correlation properties, chaotic sequences are potentially able to outperform previous options, like Gold codes, with regard to the detection probability and the number of available sequences. Numerical examples are given, in some typical scenarios and under severe operation conditions, due to the presence of interfering radars.

Kinect as a Tool for Gait Analysis: Validation of a Real-Time Joint Extraction Algorithm Working in Side View

The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still limits the adoption of the device to a number of relevant applications. This paper presents an algorithm to locate and estimate the trajectories of up to six joints extracted from the side depth view of a human body captured by the Kinect device. The algorithm is then applied to extract data that can be exploited to provide an objective score for the “Get Up and Go Test”, which is typically adopted for gait analysis in rehabilitation fields. Starting from the depth-data stream provided by the Microsoft Kinect sensor, the proposed algorithm relies on anthropometric models only, to locate and identify the positions of the joints. Differently from machine learning approaches, this solution avoids complex computations, which usually require significant resources. The reliability of the information about the joint position output by the algorithm is evaluated by comparison to a marker-based system. Tests show that the trajectories extracted by the proposed algorithm adhere to the reference curves better than the ones obtained from the skeleton generated by the native applications provided within the Microsoft Kinect (Microsoft Corporation, Redmond, WA, USA, 2013) and OpenNI (OpenNI organization, Tel Aviv, Israel, 2013) Software Development Kits.

Remote health monitoring by OSGi technology and digital TV integration

Remote home care, enabled by Information and Communication Technologies, plays an important role in the delivery of pervasive health services. One of the most challenging drivers in the deployment of pervasive health care solutions is the population ageing phenomenon. In this paper, we present a wireless, home-centered, health monitoring system architecture that can efficiently manage medical devices in a blind manner, i.e. with very little or no action required by the user, in such a way as to be possibly targeted to elderly people. The Open Services Gateway initiative (OSGi) framework is used for constructing the service oriented architecture, according to a Declarative Services paradigm. The proposed architecture has been deployed in a prototype implementation, and some preliminary results are herein discussed.

Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion

Fall injury issues represent a serious problem for elderly in our society. These people want to live in their home as long as possible and technology can improve their security and independence. In this work we study the joint use of a camera based system and wearable devices, in the so called data fusion approach, to design a fall detection solution. The synchronization issues between the heterogeneous data provided by the devices are properly treated, and three different fall detection algorithms are implemented. Experimental results are also provided, to compare the proposed solutions.

Evaluation of the Wireless M-Bus standard for future smart water grids

The most recent Wireless Sensor Networks technologies can provide viable solutions to perform automatic monitoring of the water grid, and smart metering of water consumptions. However, sensor nodes located along water pipes cannot access power grid facilities, to get the necessary energy imposed by their working conditions. In this sense, it is of basic importance to design the network architecture in such a way as to require the minimum possible power. This paper investigates the suitability of the Wireless Metering Bus protocol for possible adoption in future smart water grids, by evaluating its transmission performance, through simulations and experimental tests executed by means of prototype sensor nodes.

Wireless M-Bus Sensor Networks for Smart Water Grids: Analysis and Results

Wireless sensor network technologies are experiencing an impressive development, as they represent one of the building blocks upon which new paradigms, such as Internet of Things and Smart Cities, may be implemented. Among the different applications enabled by such technologies, automatic monitoring of the water grid, and smart metering of water consumptions, may have a great impact on the preservation of one of the most valued, and increasingly scarce, natural resources. Sensor nodes located along water pipes cannot rely on the availability of power grid facilities to get the necessary energy imposed by their working conditions. In this sense, an energy-efficient design of the network architecture, and the evaluation of Energy Harvesting techniques to sustain its nodes, becomes of paramount importance. This paper investigates the suitability of a Wireless Metering Bus-based solution for the implementation of smart water grids, by evaluating network and node related performance, through simulations, prototype design, and experimental tests, which confirm the feasibility and efficiency of the proposal.