A Comparative Study of Scheduling Techniques for Multimedia Applications on SIMD Pipelines

Parallel architectures are essential in order to take advantage of the parallelism inherent in streaming applications. One particular branch of these employ hardware SIMD pipelines. In this paper, we analyse several scheduling techniques, namely ad hoc overlapped execution, modulo scheduling and modulo scheduling with unrolling, all of which aim to efficiently utilize the special architecture design. Our investigation focuses on improving throughput while analysing other metrics that are important for streaming applications, such as register pressure, buffer sizes and code size. Through experiments conducted on several media benchmarks, we present and discuss trade-offs involved when selecting any one of these scheduling techniques.Comment: Presented at DATE Friday Workshop on Heterogeneous Architectures and Design Methods for Embedded Image Systems (HIS 2015) (arXiv:1502.07241

Joint estimation of multiple RF impairments using deep multi-task learning

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments

Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

Millimeter-wave massive multiple-input-multiple-output (mmWave mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. Exploiting this sparsity enables the applicability of hybrid precoding and achieves pilot reduction. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. A motivation for the Fourier character of this transformation is the fact that the steering response vectors in antenna arrays are Fourier basis vectors. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First, is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second, is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. This is especially the case when the antenna array is not perfectly uniform. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks

Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impracticable for resource allocation, parameter optimization, and other problems. Machine learning (ML)-based solutions, thus, can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3rd generation partnership project standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.Comment: This work was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. 5200030 with the cooperation of Vestel and Istanbul Medipol Universit

Identification of distorted RF components via deep multi-task learning

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios

Abnormal 18F-FDG Uptake Detected with Positron Emission Tomography in a Patient with Breast Cancer: A Case of Sarcoidosis and Review of the Literature

18F-FDG PET is a useful and sensitive imaging method for a variety of malignancies, however, the specificity is low in active infections and inflammatory diseases. We describe a female patient with stage IIIA breast cancer in first complete remission with combination chemotherapy who developed nodular formations in the lung and axilla 12 years later. Imaging studies as well as FDG PET showed nodular lesions and increased metabolic activity which was interpreted as the progression of the primary disease. She was first given combination chemotherapy and hormonal therapy but was proven thereafter to have sarcoidosis by pathologic examination and was successfully treated with corticosteroid treatment

Mersin City-Lab: Co-creative and participatory design approach for a circular neighbourhood

While environmental, economical and social challenges that the world has been facing recently are increasing dramatically; cities have played critical role in generation many of these problems like negative impacts on environment and overconsumption of resources. Most of the cities today face severe sustainability challenges including sanitation, air pollution, environmental degradation, over population and lack of livability. However, cities may also raise answers to find solutions against many of such complex urban problems, since they are assumed as creative and innovative platforms for social ecosystem of ideas. In this sense, there is increasing interest in ‘City Laboratories’ or ‘Urban Living Labs’, which are established to provide creative experimental platforms with participation of city actors to discuss urban sustainability issues before implementation of deep and structural urban changes for citizens. They provide participatory, co-creative and experimental platforms for self-organizing cities. The aim of this paper is to discuss a collaborative City Laboratory approach -Mersin City Lab- to achieve sustainability principles during urban regeneration process for the selected case-study area located in Mersin. Mersin City Lab focuses on two aspects: Firstly, ‘City Lab’ approach, involves citizens and stakeholders into decision-making process. Secondly, it  focuses on urban transformation process with circularity principles including water, mobility, energy, waste management, food and circular economy to achieve sustainable neighborhood development. The paper starts with introduction of ‘city-gaming’ methodology which has been adopted as the main structure of participation of multi-stakeholders. It continues with discussions on stages of the case-study project through implementation of workshops and game sessions by participation of multi stake-holders. Following, the results gathered from overall evaluations of participants’ proposals regarding land-use, mobility and urban water management, local economy, urban development, urban agriculture and food strategies in neighborhood level are discussed. Finally, the paper concludes with impacts of City Labs approach and city-gaming methodology on decision-making process for real urban problems and urban settings

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results

IDENTIFICATION OF MEATS USING RANDOM AMPLIFIED POLYMORPHIC DNA (RAPD) TECHNIQUE

Use of a simpler, faster and reliable method for identification of species of origin in fresh and processed meat products is required to prevent unethical practices that may occur in the meat industry. The effectiveness of a random amplified polymorphic DNA (RAPD) method for identification of fresh meats from cattle, goat, sheep, camel, pork, wild swine, donkey, cat, dog, rabbit or bear origin was evaluated using a 10-base primer (ACGACCCACG). The method was also used to determine the species in a 1 : 1 mix of raw minced meat from sheep-pork, horse-beef or beef-sheep. Characteristic RAPD profiles for each species were obtained. However, efficacy of the technique in identifying species in meat mixtures varied depending on the species in the mix. These results indicate that RAPD may be useful for identification of meat samples from single species, such as intact meat samples, whereas caution should be exercised in identification of origin of species in minced meat that may consist of multiple species