Novel Compact and High Selectivity Dual-band BPF with Wide Stopband

A novel type of compact and high selectivity dual-band bandpass filter (BPF) incorporating a dual-mode defected ground structure resonator (DDGSR) and a dual-mode open-stub loaded stepped impedance resonator (DOLSIR) is proposed in this paper. Utilizing capacitive source-load coupling and the intrinsic characteristics of the two types of dual-mode resonators, compact dual-band BPF with multi transmission zeros near the passband edges as well as a wide stopband which can be used to achieve high selectivity is realized. An experimental dual-band BPF located at 2.4 and 3.2 GHz was designed and fabricated. The validity of the design approach is verified by good agreement between simulated and measurement results

Multi-learner based recursive supervised training

In this paper, we propose the Multi-Learner Based Recursive Supervised Training (MLRT) algorithm which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of the dataset, and utilize it to train the data accurately and efficiently. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the SPAM dataset and comparable performances on the VOWEL and the TWO-SPIRAL problems. In addition, for most datasets, the time taken by MLRT is considerably lower than the other systems with comparable accuracy. Two heuristic versions, MLRT-2 and MLRT-3 are also introduced to improve the efficiency in the system, and to make it more scalable for future updates. The performance in these versions is similar to the original MLRT system

Earnings Management and Long-Run Stock Underperformance of Private Placements

The study investigates whether private placement issuers manipulate their earnings around the time of issuance and the effect of earnings management on the long-run stock performance. We find that managers of U.S. private placement issuers tend to engage in income-increasing earnings management in the year prior to the issuance of private placements. We further speculate that earnings management serves as a likely source of investor over-optimism at the time of private placements. To support this speculation, we find evidence suggesting that the income-increasing accounting accruals made at the time of private placements predict the post-issue long-term stock underperformance. The study contributes to the large body of literature on earnings manipulation around the time of securities issuance

Window Dressing in Reported Earnings

The article discusses the use of the term window dressing, a wide range of techniques for auditing, by audit clients to enhance the financial position of an entity through manipulated disclosures. The term refers to the reporting practices adopted by firms to distort earnings by changing the way stakeholders perceived the financial figures. A research suggests that firms must engage in the type of manipulative behavior for the purpose of economic incentives

Two problems related to prescribed curvature measures

Existence of convex body with prescribed generalized curvature measures is discussed, this result is obtained by making use of Guan-Li-Li's innovative techniques. In surprise, that methods has also brought us to promote Ivochkina's $C^2$ estimates for prescribed curvature equation in \cite{I1, I}.Comment: 12 pages, Corrected typo

Accretion and Preservation of D-rich Organic Particles in Carbonaceous Chondrites: Evidence for Important Transport in the Early Solar System Nebula

We have acquired NanoSIMS images of the matrices of CI, CM, and CR carbonaceous chondrites to study, in situ, the organic matter trapped during the formation of their parent bodies. D/H ratio images reveal the occurrence of D-rich hot spots, constituting isolated organic particles. Not all the organic particles are D-rich hot spots, indicating that at least two kinds of organic particles have been accreted in the parent bodies. Ratio profiles through D-rich hot spots indicate that no significant self-diffusion of deuterium occurs between the D-rich organic matter and the depleted hydrous minerals that are surrounding them. This is not the result of a physical shielding by any constituent of the chondrites. Ab initio calculations indicate that it cannot be explained by isotopic equilibrium. Then it appears that the organic matter that is extremely enriched in D does not exchange with the hydrous minerals, or this exchange is so slow that it is not significant over the 4.5 billion year history on the parent body. If we consider that the D-rich hot spots are the result of an exposure to intense irradiation, then it appears that carbonaceous chondrites accreted organic particles that have been brought to different regions of the solar nebula. This is likely the result of important radial and vertical transport in the early solar system

Acoustic based safety emergency vehicle detection for intelligent transport systems

A system has been investigated for the detection of incoming direction of an emergency vehicle. Acoustic detection methods based on a cross microphone array have been implemented. It is shown that source detection based on time delay estimation outperforms sound intensity techniques, although both techniques perform well for the application. The relaying of information to the driver as a warning signal has been investigated through the use of ambisonic technology and a 4 speaker array which is ubiquitous in most modern vehicles. Simulations show that accurate warning information may be relayed to the driver and afford correct action

Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition

In this paper, a new method is proposed by combining ensemble empirical mode decomposition (EEMD) with order tracking techniques to analyse the vibration signals from a two stage helical gearbox. The method improves EEMD results in that it overcomes the potential deficiencies and achieves better order spectrum representation for fault diagnosis. Based on the analysis, a diagnostic feature is designed based on the order spectra of extracted IFMs for detection and separation of gearbox faults. Experimental results show this feature is sensitive to different fault severities and robust to the influences from operating conditions and remote sensor locations

Periodic Modulation Effect on Self-Trapping of Two weakly coupled Bose-Einstein Condensates

With phase space analysis approach, we investigate thoroughly the self-trapping phenomenon for two weakly coupled Bose-Einstein condensates (BEC) in a symmetric double-well potential. We identify two kinds of self-trapping by their different relative phase behavior. With applying a periodic modulation on the energy bias of the system we find the occurrence of the self-trapping can be controlled, saying, the transition parameters can be adjusted effectively by the periodic modulation. Analytic expressions for the dependence of the transition parameters on the modulation parameters are derived for high and low frequency modulations. For an intermediate frequency modulation, we find the resonance between the periodic modulation and nonlinear Rabi oscillation dramatically affects the tunnelling dynamics and demonstrate many novel phenomena. Finally, we study the effects of many-body quantum fluctuation on self-trapping and discuss the possible experimental realization of the model.Comment: 7 pages, 11 figure