فهرست مقالات Support Vector Machine (SVM)

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  1 - Performance Analysis of SVM-Type Per Tone Equalizer Using Blind and Radius Directed Algorithms for OFDM Systems
  Babak Haji Bagher Naeeni
  10.7508/jist.2013.02.001
  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost funct چکیده کامل

  In this paper, we present Support Vector Machine (SVM)-based blind per tone equalization for OFDM systems. Blind per tone equalization using Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA) are used as the comparison benchmark. The SVM-based cost function utilizes a CMA-like error function and the solution is obtained by means of an Iterative Re-Weighted Least Squares Algorithm (IRWLS). Moreover, like CMA, the error function allows to extend the method to multilevel modulations. In this case, a dual mode algorithm is proposed. Dual mode equalization techniques are commonly used in communication systems working with multilevel signals. Practical blind algorithms for multilevel modulation are able to open the eye of the constellation, but they usually exhibit a high residual error. In a dual mode scheme, once the eye is opened by the blind algorithm, the system switches to another algorithm, which is able to obtain a lower residual error under a suitable initial ISI level. Simulation experiments show that the performance of blind per tone equalization using support vector machine has better than blind per tone equalization using CMA and MMA, from viewpoint of average Bit-Error Rate (BER). پرونده مقاله
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  2 - Facial Expression Recognition Using Texture Description of Displacement Image
  Hamid Sadeghi Abolghasem Asadollah Raie Mohammad Reza Mohammadi
  10.7508/jist.2014.04.002
  In recent years, facial expression recognition, as an interesting problem in computer vision has been performed by means of static and dynamic methods. Dynamic information plays an important role in recognizing facial expression. However, using the entire dynamic inform چکیده کامل

  In recent years, facial expression recognition, as an interesting problem in computer vision has been performed by means of static and dynamic methods. Dynamic information plays an important role in recognizing facial expression. However, using the entire dynamic information in the expression image sequences is of higher computational cost compared to the static methods. To reduce the computational cost, instead of entire image sequence, only neutral and emotional faces can be employed. In the previous research, this idea was used by means of DLBPHS method in which facial important small displacements were vanished by subtracting LBP features of neutral and emotional face images. In this paper, a novel approach is proposed to utilize two face images. In the proposed method, the face component displacements are highlighted by subtracting neutral image from emotional image; then, LBP features are extracted from the difference image. The proposed method is evaluated on standard databases and the results show a significant accuracy improvement compared to DLBPHS. پرونده مقاله
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  3 - Automatic Facial Emotion Recognition Method Based on Eye Region Changes
  Mina  Navraan charkari charkari Muharram Mansoorizadeh
  10.7508/jist.2016.04.003
  Emotion is expressed via facial muscle movements, speech, body and hand gestures, and various biological signals like heart beating. However, the most natural way that humans display emotion is facial expression. Facial expression recognition is a great challenge in the چکیده کامل

  Emotion is expressed via facial muscle movements, speech, body and hand gestures, and various biological signals like heart beating. However, the most natural way that humans display emotion is facial expression. Facial expression recognition is a great challenge in the area of computer vision for the last two decades. This paper focuses on facial expression to identify seven universal human emotions i.e. anger, disgust, fear, happiness, sadness, surprise, and neu7tral. Unlike the majority of other approaches which use the whole face or interested regions of face, we restrict our facial emotion recognition (FER) method to analyze human emotional states based on eye region changes. The reason of using this region is that eye region is one of the most informative regions to represent facial expression. Furthermore, it leads to lower feature dimension as well as lower computational complexity. The facial expressions are described by appearance features obtained from texture encoded with Gabor filter and geometric features. The Support Vector Machine with RBF and poly-kernel functions is used for proper classification of different types of emotions. The Facial Expressions and Emotion Database (FG-Net), which contains spontaneous emotions and Cohn-Kanade(CK) Database with posed emotions have been used in experiments. The proposed method was trained on two databases separately and achieved the accuracy rate of 96.63% for spontaneous emotions recognition and 96.6% for posed expression recognition, respectively پرونده مقاله
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  4 - بهبود دقت مدل GMM با استفاده از کرنل PSK در کاربرد تشخيص زبان گفتاري
  فهیمه قاسمیان محمدمهدی همایون‌پور
  مدل مخلوط گاوسي (GMM)، روشي ساده و مؤثر براي مدل‌کردن آماري فضاي ويژگي‌هاست که به‌طور گسترده در کاربرد تشخيص زبان مورد استفاده قرار گرفته و از الگوريتم بيشينه‌سازي اميد رياضي براي آموزش پارامترهاي اين مدل استفاده مي‌شود. در اين مقاله با توجه به مشکلي که در آموزش مدل GMM چکیده کامل

  مدل مخلوط گاوسي (GMM)، روشي ساده و مؤثر براي مدل‌کردن آماري فضاي ويژگي‌هاست که به‌طور گسترده در کاربرد تشخيص زبان مورد استفاده قرار گرفته و از الگوريتم بيشينه‌سازي اميد رياضي براي آموزش پارامترهاي اين مدل استفاده مي‌شود. در اين مقاله با توجه به مشکلي که در آموزش مدل GMM وجود دارد، مدلي جديد با نام PAW-GMM ارائه شده است. در اين مدل، قدرت هر مؤلفه از مدل GMMدر تمايز يک زبان از ساير زبان‌ها، براي تعيين وزن هر مؤلفه در نظر گرفته مي‌شود. مدل PAW-GMM به‌دليل در نظر گرفتن خواص تمايزي مؤلفه‌هاي مخلوط گاوسي، سبب افزايش دقت سيستم‌هاي تشخيص زباني مي‌شود که از اين مدل به‌عنوان جايگزين مدلGMM استفاده مي‌کنند. همچنين يکي از مشکلاتي که در سيستم GMM-PSK-SVMکه يکي از بهترين سيستم‌هاي تشخيص زبان است وجود دارد، پيچيدگي محاسباتي بالا خصوصاً با اضافه‌شدن تعداد زبان‌هاست. از اين رو سيستم UBM-PSK-SVM ارائه شده است که با ثابت نگه داشتن دقت سيستم GMM - PSK - SVM، سبب کاهش پيچيدگي محاسباتي آن شده و در نتيجه قدرت تعميم به زبان‌هاي بالاتر را افزايش مي‌دهد. آزمايش‌هاي صورت‌گرفته بر روي 4 سيستم تشخيص زبان مختلف با استفاده از داده‌هاي مربوط به 4 زبان انگليسي، فارسي، فرانسوي و آلماني دادگان OGI، کارايي تکنيک‌هاي ارائه‌شده را نشان مي‌دهد. پرونده مقاله