Advanced Taste Sensors Based on Artificial Lipids with Global Selectivity to Basic Taste Qualities and High Correlation to Sensory Scores

Effective R&D and strict quality control of a broad range of foods, beverages, and pharmaceutical products require objective taste evaluation. Advanced taste sensors using artificial-lipid membranes have been developed based on concepts of global selectivity and high correlation with human sensory score. These sensors respond similarly to similar basic tastes, which they quantify with high correlations to sensory score. Using these unique properties, these sensors can quantify the basic tastes of saltiness, sourness, bitterness, umami, astringency and richness without multivariate analysis or artificial neural networks. This review describes all aspects of these taste sensors based on artificial lipid, ranging from the response principle and optimal design methods to applications in the food, beverage, and pharmaceutical markets

Dynamic Resource Allocation with Integrated Reinforcement Learning for a D2D-Enabled LTE-A Network with Access to Unlicensed Band

We propose a dynamic resource allocation algorithm for device-to-device (D2D) communication underlying a Long Term Evolution Advanced (LTE-A) network with reinforcement learning (RL) applied for unlicensed channel allocation. In a considered system, the inband and outband resources are assigned by the LTE evolved NodeB (eNB) to different device pairs to maximize the network utility subject to the target signal-to-interference-and-noise ratio (SINR) constraints. Because of the absence of an established control link between the unlicensed and cellular radio interfaces, the eNB cannot acquire any information about the quality and availability of unlicensed channels. As a result, a considered problem becomes a stochastic optimization problem that can be dealt with by deploying a learning theory (to estimate the random unlicensed channel environment). Consequently, we formulate the outband D2D access as a dynamic single-player game in which the player (eNB) estimates its possible strategy and expected utility for all of its actions based only on its own local observations using a joint utility and strategy estimation based reinforcement learning (JUSTE-RL) with regret algorithm. A proposed approach for resource allocation demonstrates near-optimal performance after a small number of RL iterations and surpasses the other comparable methods in terms of energy efficiency and throughput maximization

Dynamic Buffer Status-Based Control for LTE-A Network With Underlay D2D Communication

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier

Effective resource block allocation procedure for quality of service provisioning in a single-operator heterogeneous LTE-A network

We consider the problem of resource block (RB) allocation in the integrated pico/macrocell Long Term Evolution - Advanced (LTE-A) network. It is assumed that the network is controlled by a single service provider (SP) and all the operation of the picocells is coordinated with a macro-network. To improve the quality of service (QoS) for end-to-end applications, we take into account the individual traffic demands of the users and allocate the RBs to minimize the sum of user utilities which are expressed in terms of the size of their queues. The formulated RB allocation problem belongs to the family of the multiple knapsack problems (MKPs) and, therefore, it is non-deterministic polynomial time (NP) hard in the strong sense. To reduce the complexity of this problem, we propose a simple heuristic technique to find the suitable (but not necessarily optimal) solution. The proposed RB allocation procedure requires only two additional signalling steps (necessary to maintain the coordination among different cells) and, therefore, its impact of the control signalling overhead is neglectable. It was shown (using OPNET-based simulations) that the proposed technique has low complexity, fast solution time, and shows improved performance when compared to other relevant schemes. (C) 2016 Elsevier B.V. All rights reserved

Performance optimization of iterative receiver for wireless communications based on realistic channel conditions

Adopting orthogonal frequency division multiplexing (OFDM) to low-density parity check (LDPC) coded multiple-input multiple-output (MIMO) is attractive scheme for wireless communication systems. An iterative receiver design for LDPC coded MIMO-OFDM system is proposed as the foundation for enhancing the wireless link performance can deliver the coverage, speed, throughput and reliability. However, in previous works, evaluations are basically assumed for a certain channel scenario and it is inefficient in incorporating different channel scenarios. The aim of this paper is to improve the system range for equivalent error rate, while not significantly increasing system complexity compared to conventional iterative receiver solution under realistic channel environment. We show that our proposed iteration adaptation at receiver can considerably adopt the system to realistic change environment, and reach very high reliability. Simulations of our optimization reveal superior error rate performance and lower computational cost vs. conventional LDPC coded MIMO OFDM system. Our simulation results also capture the effects of realistic vs. typical channel fading types (i.e., Rician vs. Rayleigh, respectively) and fading parameter models (average vs. random azimuth spread and K factor) on system performance and complexity. (C) 2014 Elsevier Ltd. All rights reserved

A Noise-Robust Continuous Speech Recognition System Using Block-Based Dynamic Range Adjustment

A new approach to speech feature estimation under noise circumstances is proposed in this paper. It is used in noise-robust continuous speech recognition (CSR). As the noise robust techniques in isolated word speech recognition, the running spectrum analysis (RSA), the running spectrum filtering (RSF) and the dynamic range adjustment (DRA) methods have been developed. Among them, only RSA has been applied to a CSR system. This paper proposes an extended DRA for a noise-robust CSR system. In the stage of speech recognition, a continuous speech waveform is automatically assigned to a block defined by a short time length. The extended DRA is applied to these estimated blocks. The average recognition rate of the proposed method has been improved under several different noise conditions. As a result, the recognition rates are improved up to 15% in various noises with 10 dB SNR

QoS-Oriented Mode, Spectrum, and Power Allocation for D2D Communication Underlaying LTE-A Network

This paper investigates the problem of resource allocation for device-to-device (D2D) communication in a ThirdGeneration Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) network. The users in the network can operate either in a traditional cellular mode, communicating with each other via the evolved NodeB (eNB), or in a D2D mode, communicating with each other without traversing the eNB. In the considered model, the D2D users and cellular users share the same radio resources. Particularly, each resource block (RB) within the available bandwidth can be occupied by one cellular and several D2D users. Hence, the problem of interference management is crucial for effective performance of such a network. The twofold aim of the proposed algorithm is to 1) mitigate the interference between cellular and D2D users and 2) improve the overall user-perceived quality of service (QoS). To control the interference, for each user, we define a certain target interference level and constrain the interference from the other users to stay below this level. The corresponding optimization problem maximizes the QoS of the users by minimizing the size of the buffers of user equipments (UEs). The performance of the algorithm has been evaluated by using the OPNET-based simulations. The algorithm shows improved performance in terms of mean packet end-to-end delay and loss for UEs when compared to other relevant schemes

An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks

We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic noncooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution-advanced network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations