Dynamic hybrid automatic repeat request (DHARQ) for WiMAX – Mobile multihop relay using adaptive power control

Wireless multihop network is currently attracting much attention as a new wireless broadband access technology due to numerous benefits. This work proposes a power control scheme for WiMAX multihop relay system. In contrast to existing power control and optimization approaches, our proposed method uses an adaptive Channel Quality Measurement for a relay station to reduce interferences to other mobile station (MS) or relay station (RS) within the same cell and hence increase the number of hops per link and consequently maximize the spatial reuse. The proposed power control is applied to a new dynamic HARQ algorithm for adaptive channel quality enhancement. Simulation results have indicated that the proposed approach achieves superior BER/PER performance enhancement in comparison to previous related works.     

Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

AR-based serious game framework for post-stroke rehabilitation

Stroke is considered one of the main causes of death around the world. Survivors often suffer different kinds of disabilities in terms of their cognitive and motor capabilities, and are therefore unable to perform their day-to-day activities. To regain some of their cognitive as well as motor abilities, they require rehabilitation. To this end, we present a serious game framework based on augmented reality technology that may motivate the patients’ involvement in the rehabilitation exercise. Additionally, we analyze the requirements for such a framework and describe the concept and implementation of the proposed approach. Furthermore, we designed a wireless vibrotactile output device that is attached to a tangible object. The tangible object that is connected to the framework can give haptic as well as audio-visual feedback to the patient in a more motivating and entertaining environment for rehabilitation exercises. The suitability and utility of the proposed framework was evaluated with real stroke patients and compared against the performance of a healthy control group, thus facilitating occupational therapists in assessing a patient’s progress. Our evaluations show that the serious games with vibrotactile feedback are well accepted by patients.     

Functional Diversity,Absorptive Capability and Product Success: The Moderating Role of Project Complexity in New Product Development Teams

Absorptive capability appears to be an appealing concept in the technology and innovation management literature. Though absorptive capability attracts researchers from a variety of disciplines, team‐level empirical research on it is scant. In this study, we operationalized team absorptive capability as a multidimensional construct involving knowledge acquisition, assimilation and exploitation. This study also explores the moderating effect of project complexity between team absorptive capability and new product success. In studying the data from 239 new product development projects using partial least squares structural equation modelling, we found that team functional diversity is a significant determinant of team absorptive capability. Moreover, regarding the relationships between team absorptive capability and new product success, we uncovered that (i) new product success is dependent on the ability to understand the acquired knowledge, and (ii) the teams appear to be more cautious in putting the assimilated knowledge into practice to the extent that project complexity increases.     

Practical entanglement concentration of nonlocal polarization-spatial hyperentangled states with linear optics

We present some different hyperentanglement concentration protocols (hyper-ECPs) for nonlocal N-photon systems in partially polarization-spatial hyperentangled states with known parameters, resorting to linear optical elements only, including those for hyperentangled Greenberger–Horne–Zeilinger-class states and the ones for hyperentangled cluster-class states. Our hyper-ECPs have some interesting features. First, they require only one copy of nonlocal N-photon systems and do not resort to ancillary photons. Second, they work with linear optical elements, neither Bell-state measurement nor two-qubit entangling gates. Third, they have the maximal success probability with only a round of entanglement concentration, not repeating the concentration process some times. Fourth, they resort to some polarizing beam splitters and wave plates, not unbalanced beam splitters, which make them more convenient in experiment.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

An optical solution to the 3-SAT problem using wavelength based selectors

In this paper, an optical wavelength-based method to solve a well-known NP-complete problem 3-SAT is provided. In the 3-SAT problem, a formula F in the form of conjunction of some clauses over Boolean variables is given and the question is to find if F is satisfiable or not. The provided method uses exponential number of different wavelengths in a light ray and considers each group of wavelengths as a possible value-assignment for the variables. It then uses optical devices to drop wavelengths not satisfying F from the light ray. At the end, remaining wavelengths indicate satisfiability of the formula. The method provides two ways to arrange the optical devices to select satisfying wavelengths for a given clause: simple clause selectors and combined clause selectors, both requiring exponential preprocessing time. After preprocessing phase, the provided method requires polynomial time and optical devices to solve each problem instance.     

Inverse random under sampling for class imbalance problem and its application to multi-label classification

In this paper, a novel inverse random under sampling (IRUS) method is proposed for the class imbalance problem. The main idea is to severely under sample the majority class thus creating a large number of distinct training sets. For each training set we then find a decision boundary which separates the minority class from the majority class. By combining the multiple designs through fusion, we construct a composite boundary between the majority class and the minority class. The proposed methodology is applied on 22 UCI data sets and experimental results indicate a significant increase in performance when compared with many existing class-imbalance learning methods. We also present promising results for multi-label classification, a challenging research problem in many modern applications such as music, text and image categorization.