A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments

Abstract Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains

A Hard Real-Time Kernel for Motorola Microcontrollers

This paper describes a real-time kernel for running embedded applications on a recent family of Motorola microcontrollers. Both periodic and aperiodic real-time tasks are managed, as well as non real-time tasks. The kernel has been called Yartos, and uses a hard real-time scheduling algorithm based on an EDF approach for the periodic task; aperiodic tasks are executed with a Total Bandwith Server

Privacy-Preserving OLAP-based monitoring of data streams: The PP-OMDS approach

In this paper, we propose PP-OMDS (Privacy-Preserving OLAP-based Monitoring of Data Streams), an innovative framework for supporting the OLAP-based monitoring of data streams, which is relevant for a plethora of application scenarios (e.g., security, emergency management, and so forth), in a privacy-preserving manner. The paper describes motivations, principles and achievements of the PP-OMDS framework, along with technological advancements and innovations. We also incorporate a detailed comparative analysis with competitive frameworks, along with a trade-off analysis

A genetic-fuzzy algorithm for the articulatory imitation of facial movements during vocalization of a humanoid robot

of a humanoid robo

A Real-Time Embedded Kernel for Nonvisual Robotic Sensors

We describe a novel and flexible real-time kernel, called Yartek, with low overhead and low footprint suitable for embedded systems. The motivation of this development was due to the difficulty to find a free and stable real-time kernel suitable for our necessities. Yartek has been developed on a Coldfire microcontroller. The real-time periodic tasks are scheduled using nonpreemptive EDF, while the non-real-time tasks are scheduled in background. It uses a deferred interrupt mechanism, and memory is managed using contiguous allocation. Also, a design methodology was devised for the nonpreemptive EDF scheduling, based on the computation of bounds on the periodic task durations. Finally, we describe a case study, namely, an embedded system developed with Yartek for the implementation of nonvisual perception for mobile robots. This application has been designed using the proposed design methodology

Dynamic background modeling for moving objects detection using a mobile stereo camera

Background updating is fundamental in mobile objects detection applications. This paper proposes a background updating method with a moving stereo camera. The proposed algorithm is based on the detection of the regions in the image that have major color intensity in the scene (called light zones). From these light zones some keypoints are extracted and matched between the previous background and the current foreground images. Image registration is performed by moving the old background image according to the keypoints matching so that the foreground and background images are mostly aligned. The proposed method requires that the camera moves slowly and it is used for moving objects detection with background subtraction. Three types of keypoints are tested using the same homography: light zone, SIFT and SURF keypoints. We show experimentally that, on the average, light zone keypoints performances are equal to or better than SIFT keypoints, and are faster to compute; moreover, the SURF keypoints perform worse. To get better performances, when the light zone keypoints fail, then the SIFT keypoints are used in a data fusion ramework