Adaptive joint detection of cochannel signals for TDMA handsets

In mobile communication systems, downlink (forward link) system capacity is limited by the ability of mobile receivers to recover the desired signal in the presence of cochannel interference (CCI). Joint detection of the desired and cochannel signals is a useful approach to improving receiver performance, thus increasing system capacity. In this paper, we show that a practical single-antenna joint-detection receiver can provide significant gains in system capacity for the time-division multiple-access (TDMA) standard Telecommunications Industry Association/Electronic Industry Association/Interim Standard-136 (TIA/EIA/IS-136 or IS-136). For a sectorized system, joint detection provides a capacity gain of 47% in a typical urban environment. When used in conjunction with transmit beamforming, the synergy between the two approaches leads to a capacity gain of over 200%. In determining these gains, practical aspects of the IS-136 system are considered, namely, unsynchronized networks, limited receiver complexity, and adaptability. A semiblind acquisition process, which uses the training sequence of the desired user only, is employed, because the desired and interfering base stations are not synchronized. The receiver complexity is controlled by processing only one sample per symbol period, even though it is shown that multiple samples per symbol period should ideally be used. Finally, because receiver performance may be limited by its own intersymbol interference instead of CCI, an adaptive joint-detection process is used which selects between joint demodulation and single-user equalization for each slot.     

Designing for impact: the effect of rigor and case study design on citations of qualitative case studies in management

Scientometrics - One of the most highly cited papers in management is Eisenhardt’s (Acad Manag Rev 14(4):532–550, 1989) piece on the importance of case study research, in particular...     

Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

In computational and clinical environments, autoclassification of brain magnetic resonance image (MRI) slices as normal and abnormal is challenging. The purpose of this study is to investigate the computer vision and machine learning methods for classification of brain magnetic resonance (MR) slices. In routine health-care units, MR scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro-oncologists. It is almost impossible to analyze each slice manually due to the large amount of data produced by MRI devices at each moment. Irrefutably, if an automated protocol performing this task is executed, not only the radiologist will be assisted, but a better pathological assessment process can also be expected. Numerous schemes have been reported to address the issue of autoclassification of brain MRI slices as normal and abnormal, but accuracy, robustness and optimization are still an open issue. The proposed method, using Gabor filter and support vector machines, classifies brain MRI slices as normal or abnormal. Accuracy, sensitivity, specificity and ROC-curve have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on Whole Brain Atlas-Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and ROC-curve as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and ROC-curve (0.97) were achieved. Comparison with state-of-the-art methods proved the overall efficacy of the proposed method.     

Revisiting the environmental impact of financial development on economic growth and carbon emissions: evidence from South Asian economies

Clean Technologies and Environmental Policy - It is a global challenge to achieve sustainable economic growth by improving the environment. The present study discussed the role of the financial...     

Conductive and transparent submicron polymer lens array fabrication for electrowetting applications

In this study, we have demonstrated a simple and inexpensive process to fabricate electrowetting lens arrays with various curvatures (micron to submicron) on conductive and transparent polydimethylsiloxane (PDMS) molds without additional metal layers. The microlens arrays were fabricated using one-step dual diffuser lithography process, which utilizes a pair of diffusers to diffract the incident rays of UV light at wide angles before approaching the photoresist. Dimensional control and high fill factor was achieved by just varying the exposure energy and gap between the patterns in the photomask, respectively. The patterns were replicated in conductive and transparent Ag(n)-PDMS (5–20% Ag) with 15 μm thickness. High conductivity of 4.6 × 10^?1 S/m and high transmission efficiency of 90% was demonstrated by Ag(n)-PDMS molds. Micro-nanolens arrays fabricated by the optimized corelation were utilized to demonstrate switchable wettability behavior of water droplet at different applied voltages. The electrowetting microlens array fabrication method introduced in this work has high potential to be incorporated in optoelectronics and biomedical devices.     

Residual bond strength of polymer adhesive anchored reinforcement subjected to high temperatures

The results are presented of an experimental project into the residual strength characteristics of polymer adhesives used in anchoring steel reinforcement bars, following exposure to elevated temperatures. The adhesives tested were polyester resin and epoxy resin grouts. Two types of experiment were undertaken designed to investigate the residual bond strength as well as the compressive strength of specimens. For the bond strength determination, pull-out tests were carried out on 150 mm cubes. Compressive tests were undertaken on small cylindrical specimens. All specimens were exposed to different temperatures in pre-heated ovens and then allowed to cool down to normal laboratory temperatures prior to testing. In the bond tests, two types of failure were observed: splitting and slipping. Splitting of the cubes occurred in specimens exposed to temperatures below about 200° C, and the adhesive characteristics were in general enhanced. Slipping failures, by pulling through the adhesive, were obtained in specimens exposed to higher temperatures than about 200°C, and both the residual bond and compressive strengths were observed to diminish with increasing temperature. Additionally, the texture of the adhesive had changed, losing its cohesiveness. Based on this observation, an approach for assessing thein situ bond capacity of anchors is proposed.