A fuzzy approach for selecting project membership to achieve cognitive style goals

Decision makers select employees for a project to match a particular set of goals pertaining to the multiple criteria mix of skills and competencies needed. Cognitive style influences how a person gathers and evaluates information and consequently, provides skills and competencies toward problem solving. The proposed fuzzy set-based model facilitates the manager’s selection of employees who meet the project goal(s) for the preferred cognitive style. The paper presents background information on cognitive styles and fuzzy logic with an algorithm developed based on belief in the fuzzy probability of a cognitive style fitting a defined goal. An application is presented with analysis and conclusions stated.     

Qualitative operators for reasoning maps: Evaluating multi-criteria options with networks of reasons

Cognitive/causal maps have been widely used as a powerful way of capturing decision-makers’ views about a problem, representing it as a cause–effect discourse. Several ways of making causal inferences from this type of model have been proposed in the Operational Research and Artificial Intelligence literatures, but none, as far as we are aware, has attempted to use a causal map structure to perform a multi-criteria evaluation of decision alternatives. Recently, we have proposed a new multi-criteria method, denominated as a Reasoning Map, which permits the use of decision-makers’ reasoning, structured as a network of means-and-ends (a particular type of causal map) to perform such an evaluation. In this manner, the model resembles the way that people talk and think about decisions in practice. The method also pays explicit attention to the cognitive limitations of decision-makers in providing preference information. Thus it employs qualitative assessment of preferences, utilises aggregation operators for qualitative data and provides also qualitative outputs. In this paper we discuss and evaluate possible ways of aggregating qualitative performance information in Reasoning Maps.     

Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data

Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online.     

A novel cognitive system model and approach to transformation of information granules

In this paper, a novel cognitive system model is established based on formal concept analysis to exactly describe human cognitive processes. Two new operators, extent–intent and intent–extent, are introduced between an object and its attributes. By analyzing the necessity and sufficient relations between the object and some of its attributes, the information granule concept is investigated in human cognitive processes. Furthermore, theories of transforming arbitrary information granule into necessary, sufficient, sufficient and necessary information granules are addressed carefully. Algorithm of the transformation is constructed, by which we can provide an efficient approach to the conversion among information granules. To interpret and help understand the theories and algorithm, an experimental computing program is designed and two cases is employed as case study. Results of the small scale case are calculated by the method presented in this paper. The large-scale case is calculated by the experimental computing program and validated by the proposed algorithm. The considered framework can provide a novel convenient tool for artificial intelligence researches.     

Opportunistic spectrum access for TDMA-based cognitive radio networks

In this paper, opportunistic spectrum access is proposed for TDMA-based cognitive radio networks. In TDMA-based networks, the time is divided into slots with fixed length one by one. If a primary user (PU) needs to transmit data, one or several slots will be used. Otherwise, the slots are idle and can be utilized by secondary users (SUs). When SUs want to use the licensed channel, they should sense the channel at the beginning period of each slot. Then SUs exchange their sensing results and make the same decision about the channel state (idle or used by PUs), which could reduce the probability of false sensing. The aforementioned duration is called spectrum sensing phase. When SUs decide there is an idle channel, they contend to use the channel at the rest time of the slot. The duration is called access phase. In this period, SUs contend the channel with backoff counters. When the remaining time is less than one data transmission duration, SUs cannot transmit data packets. Therefore, the remaining time is wasted. To solve this problem, SUs transmit control packets with small length in the remaining time instead. The SU who exchange control packets successfully reserves the channel and sends a data packet prior to other SUs in access phase of the next idle slot. Obviously, this reserved transmission is without collision. The independent spectrum sensing, channel state decision and control packets reservation influence the performance of SUs. The proposed scheme is formulated with all above factors. Simulations which consist with the numerical results show the proposed access scheme achieve higher throughput than the existed scheme without channel reservation.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

Spectrum sensing for OFDM signals using pilot induced cyclostationarity in the presence of cyclic frequency offset

In this paper, the problem of spectrum sensing of OFDM signals for cognitive radios is considered. It is proposed to detect the cyclostationary features introduced in an OFDM signal due to inter-pilot correlation. The performance of the proposed detector is derived and verified in case of AWGN channels. It is observed that the performance of cyclostationary detectors relies on the knowledge of the exact value of the cyclic frequency of the signal of interest. However, an offset in the cyclic frequency may arise due to several reasons. Therefore, for the proposed detector to perform reliably, there is a need to estimate the cyclic frequency offset. The Cramer–Rao bound for the cyclic frequency offset (CFO) estimator is derived, and based on it, two algorithms to estimate and compensate for the CFO are proposed. Simulation results are then used to study the performance of the proposed detection technique under Rayleigh fading both in the presence and the absence of CFO. The performance of the proposed system model is also studied under fast fading, and an alternative test statistic is proposed.     

感知无线传感网中能量检测的判决门限设定

The Internet of Things (IoT) is called the world’s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks (CRSN) technology can improve spectrum utilization efficiency and lay a solid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy detection, threshold will determine the probability of detection (P_d) and the probability of false  alarm P_f at the same time. While the threshold increases, P_d and P_f will both decrease. In this paper we focus on the maximum of the difference of P_d and P_f, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.     

Traffic-Aware Green Cognitive Radio

Cognitive Radio Network (CRN) has emerged as an effective solution to the spectrum under-utilization problem, by providing secondary users (SUs) an opportunistic access to the unoccupied frequency bands of primary users (PUs). Most of the current research on CRN are based on the assumption that the SU always has a large amount of data to transmit. This leads to the objective of SU throughput maximization with a constraint on the allowable interference to the PU. However, in many of the practical scenarios, the data arrival process of the SU closely follows an ON–OFF traffic model, and thus the usual throughput optimization framework may no longer be suitable. In this paper, we propose an intelligent data scheduling strategy which minimizes the average transmission power of the SU while maintaining the transmission delay to be sufficiently small. The data scheduling problem has been formulated as a finite horizon Markov Decision Process (MDP) with an appropriate cost function. Dynamic programming approach has been adopted to arrive at an optimal solution. Our findings show that the average transmitted power for our proposed approach can be as small as 36.5% of the power required for usual throughput maximization technique with insignificant increase in average delay.