STMicroelectronics

About: STMicroelectronics is a company organization based out in Geneva, Switzerland. It is known for research contribution in the topics: Transistor & Signal. The organization has 17172 authors who have published 29543 publications receiving 300766 citations. The organization is also known as: SGS-Thomson & STM.

Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems

Abstract: We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.

IEEE 802.22: The first cognitive radio wireless regional area network standard

Abstract: This article presents a high-level overview of the IEEE 802.22 standard for cognitive wireless regional area networks (WRANs) that is under development in the IEEE 802 LAN/MAN Standards Committee.

Roadmap on silicon photonics

Abstract: Silicon photonics research can be dated back to the 1980s. However, the previous decade has witnessed an explosive growth in the field. Silicon photonics is a disruptive technology that is poised to revolutionize a number of application areas, for example, data centers, high-performance computing and sensing. The key driving force behind silicon photonics is the ability to use CMOS-like fabrication resulting in high-volume production at low cost. This is a key enabling factor for bringing photonics to a range of technology areas where the costs of implementation using traditional photonic elements such as those used for the telecommunications industry would be prohibitive. Silicon does however have a number of shortcomings as a photonic material. In its basic form it is not an ideal material in which to produce light sources, optical modulators or photodetectors for example. A wealth of research effort from both academia and industry in recent years has fueled the demonstration of multiple solutions to these and other problems, and as time progresses new approaches are increasingly being conceived. It is clear that silicon photonics has a bright future. However, with a growing number of approaches available, what will the silicon photonic integrated circuit of the future look like? This roadmap on silicon photonics delves into the different technology and application areas of the field giving an insight into the state-of-the-art as well as current and future challenges faced by researchers worldwide. Contributions authored by experts from both industry and academia provide an overview and outlook for the silicon waveguide platform, optical sources, optical modulators, photodetectors, integration approaches, packaging, applications of silicon photonics and approaches required to satisfy applications at mid-infrared wavelengths. Advances in science and technology required to meet challenges faced by the field in each of these areas are also addressed together with predictions of where the field is destined to reach.

SPIRAL: Code Generation for DSP Transforms

Abstract: Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL, which considers this problem for the performance-critical domain of linear digital signal processing (DSP) transforms. For a specified transform, SPIRAL automatically generates high-performance code that is tuned to the given platform. SPIRAL formulates the tuning as an optimization problem and exploits the domain-specific mathematical structure of transform algorithms to implement a feedback-driven optimizer. Similar to a human expert, for a specified transform, SPIRAL "intelligently" generates and explores algorithmic and implementation choices to find the best match to the computer's microarchitecture. The "intelligence" is provided by search and learning techniques that exploit the structure of the algorithm and implementation space to guide the exploration and optimization. SPIRAL generates high-performance code for a broad set of DSP transforms, including the discrete Fourier transform, other trigonometric transforms, filter transforms, and discrete wavelet transforms. Experimental results show that the code generated by SPIRAL competes with, and sometimes outperforms, the best available human tuned transform library code.

Widely linear estimation with complex data

Abstract: Mean square estimation of complex and normal data is not linear as in the real case but widely linear. The purpose of the paper is to calculate the optimum widely linear mean square estimate and to present its main properties. The advantage with respect to the linear procedure is especially analyzed. >