Many EMD-type data hiding schemes have been proposed. However, the data hiding capacity is less than 2bpp when the embedding procedure uses formula operations. In order to improve the data hiding capacity from 1bpp to 4.5bpp, a new data hiding scheme is proposed in this paper based on a formula using the fully exploiting modification directions method (FEMD). By using our proposed theorem, the secret data can be embedded by formula operations directly without using a lookup matrix. The simulation results and performance analysis show the proposed scheme not only maintains good embedding capacity and stegoimage quality but also solves the overflow problem. It does so without using extra memory resources and performs within a reasonable computing time. The resource usage and capabilities of this scheme are well matched to the constraints and requirements of resource-scarce mobile devices.

This letter realizes direction of arrival (DOA) estimation by exploiting the noise subspace based estimator. Since single subspace feature extraction fails to achieve satisfactory results under unknown noise fields, we propose a two-step subspace feature extraction technique that is effective even in these fields. When a new noise subspace is attained, the proposed estimator without prewhitening can form the maximizing orthogonality especially for unknown noise fields. Simulation results confirm the effectiveness of the proposed technique.

The diverse broadcast means that will be available in the future will cause an increased demand for programs. When the input of the posture of an agent is used to manipulate a virtual computer graphics actor, it is better if the system does not require a special studio and devices. In the present paper, we propose a way to extract images from a single picture based on estimates of blooming. This is done using a partial auto-correlation analysis that carries out backward and forward predictions simultaneously. And, we divide targets into the stratified depth from a single image. An experiment was conducted using a picture taken with a digital camera, and satisfactory results were obtained.

A new simple method for extracting the capacitance coupling coefficients of sub-0.5-µm flash memory cells is proposed. Different from the previously proposed methods, this method is not affected by a dopant profile of source region because a band-to-band tunneling current from the interface between the drain and the substrate is probed. Use of a reference device eliminates the necessity to make assumptions concerning the electron transport mechanism. Comparison with the other methods shows that the proposed method is simple and accurate.

Learning correctly from queries" is a formal learning model proposed by Angluin. In this model, for a class Γ of language representations, a learner asks queries to a teacher of an unknown language Lq which can be represented by some GqΓ, and eventually outputs a language representation GΓ which represents Lq and halts. An algorithm (leaner) A is said to learn a class of languages represented by Γ in the weak definition if the time complexity of A is some polynomial of n and m, where n is the minimum size of the lagunage representations in Γ which represent Lq, and m is the maximum length of the counterexamples returned in an execution. On the other band, A is said to learn represented by Γ in the strong definition if at any point τ of the execution, the time consumed up to τ is some polynomial of n and m, where n is the same as above, and m is the maximum length of the counterexamples returned up to τ. In this paper, adequacy of the model is examined, and it is shown that both in the weak and strong definitions, there exist learners which extract a long counterexample, and identify Lq by using equivalence queries exhaustively. For example, there exists a learner which learns the class CFL of context-free languages represented by the class CFG of context-free grammars in the weak definition using only equivalence queries. Next, two restrictions concerning with learnability criteria are introduced. Proper termination condition is that when a teacher replies with yes" to an equivalence query, then the learner must halt immediately. The other condition, called LBC-condition, is that in the weak/strong definition, the time complexity must be some polynomial of n and log m. In this paper, it is shown that under these conditions, there still exist learners which execute exhaustive search. For instance, there exists a learner which learns CFL represented by CFG in the weak definition using membership queries and equivalence queries under the proper termination condition, and there also exists a learner that learns CFL represented by CFG in the strong definition using subset queries and superset queries under LBC-condition. These results suggest that the weak definition is not an adequate learning model even if the proper termination condition is assumed. Also, the model becomes inadequate in the strong definition if some combination of queries, such as subset queries and superset queries, is used instead of equivalence queries. Many classes of languages become learnable by our extracting long counterexample" technique. However, it is still open whether or not CFL represented by CFG is learnable in the strong definition from membership queries and equivalence queries, although the answer is known to be negative if at least one of (1) quadratic residues modulo a composite, (2) inverting RSA encryption, or (3) factoring Blum integers, is intractable.