6,829 research outputs found
A Semi-Blind Source Separation Method for Differential Optical Absorption Spectroscopy of Atmospheric Gas Mixtures
Differential optical absorption spectroscopy (DOAS) is a powerful tool for
detecting and quantifying trace gases in atmospheric chemistry
\cite{Platt_Stutz08}. DOAS spectra consist of a linear combination of complex
multi-peak multi-scale structures. Most DOAS analysis routines in use today are
based on least squares techniques, for example, the approach developed in the
1970s uses polynomial fits to remove a slowly varying background, and known
reference spectra to retrieve the identity and concentrations of reference
gases. An open problem is to identify unknown gases in the fitting residuals
for complex atmospheric mixtures.
In this work, we develop a novel three step semi-blind source separation
method. The first step uses a multi-resolution analysis to remove the
slow-varying and fast-varying components in the DOAS spectral data matrix .
The second step decomposes the preprocessed data in the first step
into a linear combination of the reference spectra plus a remainder, or
, where columns of matrix are known reference spectra,
and the matrix contains the unknown non-negative coefficients that are
proportional to concentration. The second step is realized by a convex
minimization problem ,
where the norm is a hybrid norm (Huber estimator) that helps to
maintain the non-negativity of . The third step performs a blind independent
component analysis of the remainder matrix to extract remnant gas
components. We first illustrate the proposed method in processing a set of DOAS
experimental data by a satisfactory blind extraction of an a-priori unknown
trace gas (ozone) from the remainder matrix. Numerical results also show that
the method can identify multiple trace gases from the residuals.Comment: submitted to Journal of Scientific Computin
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel
لقد تطورت مشكلة تقدير القناة اللاسلكية بسبب بعض التأثيرات غير المرغوب فيها للخواص الفيزيائية للقناة على الإشارات المرسلة. في نهاية المستقبل، التشوه، والتأخير، والتوهين، والتداخلات، ونوبات الطور هي أكثر المشكلات التي تواجهها مع الإشارات المستقبلة. من أجل التغلب على تأثيرات القناة وتوفير جودة كاملة تقريبًا لنقل البيانات، يلزم تقدير معلومات القناة. في أنظمة المخرجات متعددة المدخلات والمخرجات (MIMO)، يعتبر تقدير القناة خطوة أكثر تعقيدًا مقارنة بأنظمة المخرجات ذات المدخلات المفردة، SISO، نظرًا لأن عدد القنوات الفرعية التي تحتاج إلى تقدير أكبر بكثير من انظمة SISO. الهدف الأساسي من هذه الورقة البحثية هو مراجعة شاملة لاغلب الخوارزميات الشهيرة والفعالة التي تم ابتكارها لحل مشكلة تقدير قناة MIMO في أنظمة الاتصالات اللاسلكية. في هذه الورقة، تم تصنيف هذه التقنيات إلى ثلاث مجموعات: غير المكفوفين، شبه الأعمى وتقدير أعمى. لكل مجموعة، يتم تقديم توضيح مختصر لخوارزميات التقدير المألوفة. وأخيرًا، نقارن بين هذه التقنيات استنادًا إلى التعقيد الحسابي والكمون ودقة التقدير.The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy
Soft Computing for Robust Secure Wireless Reception
Soft computing is a collection of different computing methodologies that include neuro computing, fuzzy logic, evolutionary computing, and probabilistic reasoning. These are aimed to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. This paper presents a brief overview of soft computing components, followed by typical realization, via simulation of a wireless receiver employing a hybrid soft computing technique to illustrate its application in a fading signal propagation scenario.Defence Science Journal, 2009, 59(5), pp.517-523, DOI:http://dx.doi.org/10.14429/dsj.59.155
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