Power-optimized waveforms (POWs) are the enabling technology for realizing an internet-of-things (IoTs). An IoT will require billions or trillions of sensors, which must rely on passive, backscatter communication to facilitate the wireless transfer of information. Passive, backscatter sensors are uniquely suited for an IoT because of their ease of installation, low-cost, and lack of potentially toxic batteries. POW's primary benefit is that they can greatly improve the energy-harvesting efficiency of passive sensors, which increases their range and reliability. An overview of POWs is presented followed by measured results validated by a theoretical model and computer simulations. These measured results conducted at 5.8 GHz demonstrate the highest reported efficiency of a low-power, microwave energy-harvesting circuit of 26.3% at an input power of −10.2 dBm when using an excitation signal with a peak-to-average-power ratio of 12.

The alignment of WordNet and Wikipedia has received wide attention from researchers of computational linguistics, who are building a new lexical knowledge source or enriching the semantic information of WordNet entities. The main challenge of this alignment is how to handle the synonymy and ambiguity issues in the contents of two units from different sources. Therefore, this paper introduces mapping method that links an Arabic WordNet synset to its corresponding article in Wikipedia. This method uses monolingual and bilingual features to overcome the lack of semantic information in Arabic WordNet. For evaluating this method, an Arabic mapping data set, which contains 1,291 synset–article pairs, is compiled. The experimental analysis shows that the proposed method achieves promising results and outperforms the state-of-the-art methods that depend only on monolingual features. The mapped method has also been used to increase the coverage of Arabic WordNet by inserting new synsets from Wikipedia.

We present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from sequential frames are then matched and a rank order statistics based robust estimator is employed to refine the matches and estimate a rigid-body transformation between the frames. Experimental evaluations show that the proposed keypoint extraction method is highly repeatable and outperforms the state of the art methods in terms of accuracy and repeatability. We show that the performance of the registration algorithm is also comparable to other well-known methods in texture-less environments.

Consider a two-station, heterogeneous parallel queueing system in which each station operates as an independent M/M/1 queue with its own infinite-capacity buffer. The input to the system is a Poisson process that splits among the two stations according to a Bernoulli splitting mechanism. However, upon arrival, a strategic customer initially joins one of the queues selectively and decides at subsequent arrival and departure epochs whether to jockey (or switch queues) with the aim of reducing her own sojourn time. There is a holding cost per unit time, and jockeying incurs a fixed non-negative cost while placing the customer at the end of the other queue. We examine individually optimal joining and jockeying policies that minimize the strategic customer's total expected discounted (or undiscounted) costs over finite and infinite time horizons. The main results reveal that, if the strategic customer is in station 1 with ℓ customers in front of her, and q1 and q2 customers in stations 1 and 2, respectively (excluding herself), then the incentive to jockey increases as either ℓ increases or q2 decreases. Numerical examples reveal that it may not be optimal to join, and/or jockey to, the station with the shortest queue or the fastest server.

Under a proper translation, the languages of propositional (and quantified relevant logic) with an absurdity constant are characterized as the fragments of first order logic preserved under (world-object) relevant directed bisimulations. Furthermore, the properties of pointed models axiomatizable by sets of propositional relevant formulas have a purely algebraic characterization. Finally, a form of the interpolation property holds for the relevant fragment of first order logic.

The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control.

This paper extends Kripke’s theory of truth to a language with a variably strict conditional operator, of the kind that Stalnaker and others have used to represent ordinary indicative conditionals of English. It then shows how to combine this with a different and independently motivated conditional operator, to get a substantial logic of restricted quantification within naive truth theory.

Two stochastic knapsack problem (SKP) models are considered: the static broken knapsack problem (BKP) and the SKP with simple recourse and penalty cost problem. For both models, we assume: the knapsack has a constant capacity; there are n types of items and each type has an infinite supply; a type i item has a deterministic reward v i and a random weight with known distribution F i . Both models have the same objective to maximize expected total return by finding the optimal combination of items, that is, quantities of items of each type to be put in knapsack. The difference between the two models is: if knapsack is broken when total weights of items put in knapsack exceed the knapsack's capacity, for the static BKP model, all existing rewards would be wiped out, while for the latter model, we could still keep the existing rewards in knapsack but have to pay a fixed penalty plus a variant cost proportional to the overcapacity amount. This paper also discusses the special case when knapsack has an exponentially distributed capacity.

It has been suggested that current research in computer-assisted language learning (CALL) should seek to understand the conditions and circumstances that govern students’ use of technology (Steel & Levy, 2013). This paper attempts to identify critical factors accounting for student choices, first, by investigating advanced learners’ reported use as well as their views on the potential of specific technological resources for language learning, and, second, by widening the perspective and surveying students’ ideal learning environments. Learners’ reasons for preferring teacher-fronted classes, blended learning, immersion or technology-mediated settings yield useful information on how students perceive the strengths and weaknesses of interaction/engagement with material (i.e. technological) as well as social (i.e. human) resources, and how the roles of teachers/classes can be conceptualised today.

Data was collected via a survey of 175 Austrian university students which included Likert-type ratings and free text responses to open questions. Findings indicate that though the cohort routinely use a wide range of technology tools in their everyday lives and show awareness of the potential of ICT for language learning, a number of barriers exist based on learner beliefs/conceptions and learning aims. Thus the notion that enhancement of communicative competence is intrinsically tied to personal interaction with native speakers means that the potential of communication technologies such as Skype is not fully appreciated. It was further established that though many students are well versed in blending different technological resources in line with the criteria identified, thus displaying the hallmarks of autonomous learners, there was a clear preference for real-life compared to virtual environments.

Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects' state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics.

Within the field of computer assisted language learning (CALL), scant literature exists regarding the effectiveness and practicality for secondary students to utilize data-driven learning (DDL) for vocabulary acquisition. In this study, there were 100 participants, who had a mean age of thirteen years, and were attending an international school in Ho Chi Minh City, Vietnam. This particular milieu unsurprisingly comprised ‘third culture kids’ (TCKs) and ‘cross-cultural kids’ (CCKs). They were assigned to a control and experimental group; both had several intensive weeks of online-dictionary learning training, while the experimental group also experienced intensive DDL training. This was done prior to the start of the eight-week longitudinal study. Major findings included a significant longitudinal main effect for both groups, significantly overall higher results for the experimental group than the control, and a significant difference among subjects’ grade level was discovered. Furthermore, the experimental group exhibited a significantly marked increase in vocabulary results in the later weeks of the experiment. In general, these results show that DDL can be successful in the secondary school English as a foreign language (EFL) context and that it promotes significantly better vocabulary acquisition when used in conjunction with online-dictionary vocabulary learning methods, especially for a sustained longitudinal period of time.

One of the best-known methods for discriminating λ-terms with respect to β-convertibility is due to Corrado Böhm. The idea is to compute the infinitary normal form of a λ-term M, the Böhm Tree (BT) of M. If λ-terms M, N have distinct BTs, then M ≠βN, that is, M and N are not β-convertible. But what if their BTs coincide? For example, all fixed point combinators (FPCs) have the same BT, namely λx.x(x(x(. . .))).

We introduce a clocked λ-calculus, an extension of the classical λ-calculus with a unary symbol τ used to witness the β-steps needed in the normalization to the BT. This extension is infinitary strongly normalizing, infinitary confluent and the unique infinitary normal forms constitute enriched BTs, which we call clocked BTs. These are suitable for discriminating a rich class of λ-terms having the same BTs, including the well-known sequence of Böhm's FPCs.

We further increase the discrimination power in two directions. First, by a refinement of the calculus: the atomic clocked λ-calculus, where we employ symbols τp that also witness the (relative) positions p of the β-steps. Second, by employing a localized version of the (atomic) clocked BTs that has even more discriminating power.