EQUITAS: A tool-chain for functional safety and reliability improvement in automotive systems

To support advanced features such as hybrid engine control, intelligent energy management, and advanced driver assistance systems, automotive embedded systems must use advanced technologies. As a result, systems are becoming distributed and include dozens of Electronic Control Units (ECU). On the one hand, this tendency raises the issue of robustness and reliability, due to the increase in the error ratio with the integration level and the clock frequency. On the other hand, due to a lack of automation, software Validation and Verification (V&V) tends to swallow up 40% to 50% of the total development cost. The ``Enhanced Quality Using Intensive Test Analysis on Simulators'' (EQUITAS¹) project aims (1) to improve reliability and functional safety and (2) to limit the impact of software V&V on embedded systems costs and time-to-market. These two achievements are obtained by (1) developing a continuous tool-chain to automate the V&V process, (2) improving the relevance of the test campaigns by detecting redundant tests using equivalence classes, (3) providing assistance for hardware failure effect analysis (FMEA) and finally (4) assessing the tool-chain under the ISO 26262 requirements.     

HNCP: A many-core microprocessor ASIC approach dedicated to embedded image processing applications

Highly regular many-core architectures tend to be more and more popular as they are suitable for inherently highly parallelizable applications such as most of the image and video processing domain. In this article, we present a novel architecture for many-core microprocessor ASIC dedicated to embedded video and image processing applications. We propose a flexible many-core approach with two architectures one implemented in CMOS 65 nm technology containing 16 open-source tiles and the other implemented in CMOS FD-SOI 28 nm technology containing 64 open-source tiles. Each tile of these architectures can choose its communication links depending on the most relevant overall parallelism scheme for a targeted application. Both chips are fully functional in simulation. The layouts are presented with frequency, area and power consumption results. Various case studies are presented to illustrate the proposed flexible many-core architectures and enable to focus on architecture exploration, instantiated scheme of parallelization and timing performance.     

Monitoring skin condition using life activities on the SNS user documents

Multimedia Tools and Applications - Social networks are not new to the IT landscape. Starting from bulletin boards and chat rooms, they have evolved to include desktop and mobile device...     

MRI segmentation fusion for brain tumor detection

The process of manually generating precise segmentations of brain tumors from magnetic resonance images (MRI) is time-consuming and error-prone. We present a new algorithm, Potential Field Segmentation (PFS), and propose the use of ensemble approaches that combine the results generated by PFS and other methods to achieve a fused segmentation. For the PFS method, we build on our recently proposed clustering algorithm, Potential Field Clustering, which is based on an analogy with the concept of potential field in Physics. We view the intensity of a pixel in an MRI as a “mass” that creates a potential field. Specifically, for each pixel in the MRI, the potential field is computed and, if smaller than an adaptive potential threshold, the pixel is associated with the tumor region. This “small potential” segmentation criterion is intuitively valid because tumor pixels have larger “mass” and thus the potential of surrounding regions is also much larger than in other regions of smaller or no “mass”. We evaluate the performance of the different methods, including the ensemble approaches, on the publicly available Brain Tumor Image Segmentation (BRATS) MRI benchmark database.     

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.     

A delay-aware schedule method for distributed information fusion with elastic and inelastic traffic

Information fusion is an efficient way to detect the specified events and extract useful information, especially in the context of big data. As a large-scale data-gathering system, Internet of Things (IoT) has the traffic with the mixed timing characteristics. The real-time observations with various delay constraints and the non-real-time observations are needed in information fusion. In order to guarantee the performance of Distributed Information Fusion (DIF), the paper focuses on the communication mechanism from the perspective of real-time delivery of sensing data. An online scheduling algorithm and its distributed implementation, named Delay-Guaranteed CSMA, are proposed. Both the timing constraints and the historical transmission statistics of sensors are taking into consideration. The simulation results have shown that the proposed policy achieves good delay-guaranteed satisfaction. The goal of real-time data delivery for distributed information fusion is achieved.     

Efficient integration of RSSI for tracking using Wireless Camera Networks

This paper proposes a scheme that efficiently exploits synergies between RSSI and camera measurements in cluster-based target tracking using Wireless Camera Networks (WCNs). The scheme is based on the combination of two main components: a training method that accurately trains RSSI-range models adapted to the conditions of the particular local environment; and a sensor activation/deactivation method that decides on the individual activation of sensors balancing the different information contributions and energy consumptions of camera and RSSI measurements involved in sensing. The scheme also includes a distributed Extended Information Filter that integrates all available measurements. The combination of these components originates self-regulated behaviors that drastically reduce power consumption and computational effort with no significant tracking degradation w.r.t. existing schemes based exclusively on cameras. Besides, it shows better robustness to target occlusions. The proposed scheme has been implemented and validated in real experiments.     

Ontology based user query interpretation for semantic multimedia contents retrieval

Users who are familiar with the existing keyword-based search have problems of not being able to configure the formal query because they don’t have generic knowledge on knowledge base when using the semantic-based retrieval system. User wants the search results which are more accurate and match the user’s search intents with the existing keyword-based search and the same search keyword without the need to recognize what technology the currently used retrieval system is based on to provide the search results. In order to do the semantic analysis of the ambiguous search keyword entered by users who are familiar with the existing keyword-based search, ontological knowledge base constructed based on refined meta-data is necessary, and the keyword semantic analysis technique which reflects user’s search intents from the well-established knowledge base and can generate accurate search results is necessary. In this paper, therefore, by limiting the knowledge base construction to multimedia contents meta-data, the applicable prototype has been implemented and its performance in the same environment as Smart TV has been evaluated. Semantic analysis of user’s search keyword is done, evaluated and recommended through the proposed ontological knowledge base framework so that accurate search results that match user’s search intents can be provided.     

Performance analysis of a finite-buffer discrete-time queue with bulk arrival,bulk service and vacations

This paper considers a finite-buffer discrete-time Geo^X/G^Y/1/K+B queue with multiple vacations that has a wide range of applications including high-speed digital telecommunication systems and various related areas. The main purpose of this paper is to present a performance analysis of this system. For this purpose, we first derive a set of linear equations to compute the steady-state departure-epoch probabilities based on the embedded Markov chain technique. Next, we present numerically stable relationships for the steady-state probabilities of the queue lengths at three different epochs: departure, random, and arrival. Finally, based on these relationships, we present various useful performance measures of interest such as the moments of the number of packets in the queue at three different epochs, the mean delay in the queue of a packet, the loss probability and the probability that server is busy with computational experiences.