Vahid Pourahmadi

Ali Mirzaei, Vahid Pourahmadi, Hamid Sheikhzadeh et al.

This paper introduces a novel approach to active feature acquisition for classification, which is the task of sequentially selecting the most informative subset of features to achieve optimal prediction performance during testing while minimizing cost. The proposed approach involves a new lazy model that is significantly faster and more efficient compared to existing methods, while still producing comparable accuracy results. During the test phase, the proposed approach utilizes Fisher scores for feature ranking to identify the most important feature at each step. In the next step the training dataset is filtered based on the observed value of the selected feature and then we continue this process to reach to acceptable accuracy or limit of the budget for feature acquisition. The performance of the proposed approach was evaluated on synthetic and real datasets, including our new synthetic dataset, CUBE dataset and also real dataset Forest. The experimental results demonstrate that our approach achieves competitive accuracy results compared to existing methods, while significantly outperforming them in terms of speed. The source code of the algorithm is released at github with this link: https://github.com/alimirzaei/FCwSFS.

Mohamadreza Jafaryani, Hamid Sheikhzadeh, Vahid Pourahmadi

Typically, voice conversion is regarded as an engineering problem with limited training data. The reliance on massive amounts of data hinders the practical applicability of deep learning approaches, which have been extensively researched in recent years. On the other hand, statistical methods are effective with limited data but have difficulties in modelling complex mapping functions. This paper proposes a voice conversion method that works with limited data and is based on stochastic variational deep kernel learning (SVDKL). At the same time, SVDKL enables the use of deep neural networks' expressive capability as well as the high flexibility of the Gaussian process as a Bayesian and non-parametric method. When the conventional kernel is combined with the deep neural network, it is possible to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem and, unlike the exact Gaussian process, allows for the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which considers both data fitting and model complexity. Considering the complexity of the model reduces the amount of training data by increasing the resistance to overfitting. To evaluate the proposed scheme, we examined the model's performance with approximately 80 seconds of training data. The results indicated that our method obtained a higher mean opinion score, smaller spectral distortion, and better preference tests than the compared methods.

Hadi Khorsand, Vahid Pourahmadi

In the active learning paradigm, using an oracle to label data has always been a complex and expensive task, and with the emersion of large unlabeled data pools, it would be highly beneficial If we could achieve better results without relying on an oracle. This research introduces OFAL, an oracle-free active learning scheme that utilizes neural network uncertainty. OFAL uses the model's own uncertainty to transform highly confident unlabeled samples into informative uncertain samples. First, we start with separating and quantifying different parts of uncertainty and introduce Monte Carlo Dropouts as an approximation of the Bayesian Neural Network model. Secondly, by adding a variational autoencoder, we go on to generate new uncertain samples by stepping toward the uncertain part of latent space starting from a confidence seed sample. By generating these new informative samples, we can perform active learning and enhance the model's accuracy. Lastly, we try to compare and integrate our method with other widely used active learning sampling methods.

Mohamadreza Jafaryani, Saeed Khorram, Vahid Pourahmadi et al.

Patients with sleep disorders can better manage their lifestyle if they know about their special situations. Detection of such sleep disorders is usually possible by analyzing a number of vital signals that have been collected from the patients. To simplify this task, a number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. However, due to the non-stationary nature of sleep signals, such schemes are not leading an acceptable accuracy. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time. To augment the time resolution, each standard epoch is segmented into 5 sub-epochs. Additionally, to enhance the accuracy, we employ three classifiers with different properties and then use an ensemble method as the ultimate classifier. The simulation results show that the proposed method enhances the accuracy of conventional ASSR methods.

Mohammad Hadi Kefayati, Vahid Pourahmadi, Hassan Aghaeinia

Objects in an environment affect electromagnetic waves. While this effect varies across frequencies, there exists a correlation between them, and a model with enough capacity can capture this correlation between the measurements in different frequencies. In this paper, we propose the Wi2Vi model for associating variations in the WiFi channel state information with video frames. The proposed Wi2Vi system can generate video frames entirely using CSI measurements. The produced video frames by the Wi2Vi provide auxiliary information to the conventional surveillance system in critical circumstances. Our implementation of the Wi2Vi system confirms the feasibility of constructing a system capable of deriving the correlations between measurements in different frequency spectrums.

Pooya Khandel, Amir Hossein Rassafi, Vahid Pourahmadi et al.

In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.

Shirin Seyedsalehi, Vahid Pourahmadi, Hamid Sheikhzadeh et al.

Channel, as the medium for the propagation of electromagnetic waves, is one of the most important parts of a communication system. Being aware of how the channel affects the propagation waves is essential for designing, optimization and performance analysis of a communication system. For this purpose, a proper channel model is needed. This paper presents a novel propagation channel model which considers the time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative Adversarial Networks. Moreover, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN as an image-to-image translation technique is used to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using existing metrics. Furthermore, to capture 2D similarity in both time and frequency, a new metric is introduced. Using this metric, the generated channels show significant statistical similarity to the measurement data.

Ali Mirzaei, Vahid Pourahmadi, Mehran Soltani et al.

High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a 'student' network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.

Mohammad Sadegh Safari, Vahid Pourahmadi, Shabnam Sodagari

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for the efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD systems. Therefore, users usually feed the DL-CSI back to the transmitter. To remove the need for feedback (and thus having less signaling overhead), we propose to use two recent deep neural network structures, i.e., convolutional neural networks and generative adversarial networks (GANs) to infer the DL-CSI by observing the UL-CSI. The core idea of our data-driven scheme is exploiting the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from the UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To overcome incorrect latent domain and the problem of oversimplistic assumptions, in this work, we did not use any specific parametric model and instead used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN (BEGAN). For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments in FDD communications.

Peyman Yazdanian, Vahid Pourahmadi

The widespread mobile devices facilitated the emergence of many new applications and services. Among them are location-based services (LBS) that provide services based on user's location. Several techniques have been presented to enable LBS even in indoor environments where Global Positioning System (GPS) has low localization accuracy. These methods use some environment measurements (like Channel State Information (CSI) or Received Signal Strength (RSS)) for user localization. In this paper, we will use CSI and a novel deep learning algorithm to design a robust and efficient system for indoor localization. More precisely, we use supervised autoencoder (SAE) to model the environment using the data collected during the training phase. Then, during the testing phase, we use the trained model and estimate the coordinates of the unknown point by checking different possible labels. Unlike the previous fingerprinting approaches, in this work, we do not store the {CSI/RSS} of fingerprints and instead we model the environment only with a single SAE. The performance of the proposed scheme is then evaluated in two indoor environments and compared with that of similar approaches.

Amirhossein Yazdani Abyaneh, Ali Hosein Gharari Foumani, Vahid Pourahmadi

The first step of a secure communication is authenticating legible users and detecting the malicious ones. In the last recent years, some promising schemes proposed using wireless medium network's features, in particular, channel state information (CSI) as a means for authentication. These schemes mainly compare user's previous CSI with the new received CSI to determine if the user is in fact what it is claiming to be. Despite high accuracy, these approaches lack the stability in authentication when the users rotate in their positions. This is due to a significant change in CSI when a user rotates which mislead the authenticator when it compares the new CSI with the previous ones. Our approach presents a way of extracting features from raw CSI measurements which are stable towards rotation. We extract these features by the means of a deep neural network. We also present a scenario in which users can be {efficiently} authenticated while they are at certain locations in an environment (even if they rotate); and, they will be rejected if they change their location. Also, experimental results are presented to show the performance of the proposed scheme.

Mehran Soltani, Vahid Pourahmadi, Ali Mirzaei et al.

In this paper, we present a deep learning (DL) algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a two-dimensional image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR) and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, an implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics and it is better than ALMMSE (an approximation to linear MMSE). The results confirm that this pipeline can be used efficiently in channel estimation.