Operational amplifiers revisited for low field magnetic resonance relaxation time measurement electronics

Advances in permanent magnet technology has seen more reports of sensor applications of low field magnetic resonance. Whilst most are either in the 10–20 MHz range or in the earth’s field, measurements at below 1 MHz are beginning to become more widespread. This range is below the need for careful radio frequency electronics design but above the audio domain and represents an interesting cross over. Many commercial spectrometers do not include the pulse power amplifier, duplexer and preamplifier as these depend on the frequency range used. In this work we demonstrate that, with the current specifications of the humble operational amplifier, the most simple form of an inverting design using only two resistors and decoupling, can effectively provide this ‘front end’ electronics. The low powers used mean crossed Ge diodes provide an excellent duplexer and it is suitable for battery powered applications

A Deep Learning Approach to Radio Signal Denoising

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy

Using autoencoders for radio signal denoising

We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions. The core component used in this work is a Convolutional De-noising AutoEncoder, known to be very effective in image processing. The key of our approach consists in transforming the radio signal into a representation suitable to the CDAE: we transform the time-domain signal into a 2D signal using the Short Time Fourier Transform. We report about the performance of the approach in preamble denoising across protocols of the IEEE 802.11 family, studied using simulation data. This approach could be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A perspective advantage of using the AutoEncoders in that pipeline is that they can be co-trained with the downstream classifier, to optimize the classification accuracy

The state of HRM in the Middle East:Challenges and future research agenda

Based on a robust structured literature analysis, this paper highlights the key developments in the field of human resource management (HRM) in the Middle East. Utilizing the institutional perspective, the analysis contributes to the literature on HRM in the Middle East by focusing on four key themes. First, it highlights the topical need to analyze the context-specific nature of HRM in the region. Second, via the adoption of a systematic review, it highlights state of development in HRM in the research analysis set-up. Third, the analysis also helps to reveal the challenges facing the HRM function in the Middle East. Fourth, it presents an agenda for future research in the form of research directions. While doing the above, it revisits the notions of “universalistic” and “best practice” HRM (convergence) versus “best-fit” or context distinctive (divergence) and also alternate models/diffusion of HRM (crossvergence) in the Middle Eastern context. The analysis, based on the framework of cross-national HRM comparisons, helps to make both theoretical and practical implications

Exopolysaccharide produced by the potential probiotic Lactococcus garvieae C47: Structural characteristics, rheological properties, bioactivities and impact on fermented camel milk

Fermented camel milk possesses a weak (liquid-like) gel structure. We aimed to 1) investigate the characteristics, bioactivities and rheological properties of the exopolysaccharide (EPS) produced by Lactococcus garvieae-C47 (exopolysaccharide-C47 product), a potential probiotic bacterium, on milk extracted from camels and 2) examine the rheological properties of the fermented camel milk produced by L. garvieae-C47. Exopolysaccharide-C47 product (molecular weight: 7.3 7 106 Da) was composed of the following monosaccharides: glucose (82.51%), arabinose (5.32%) and xylose (12.17%). The antioxidant, antitumor and \u3b1-amylase inhibitory activities of exopolysaccharide-C47 product reached up to 67.52, 59.35 and 91.0%, respectively. The apparent viscosity of exopolysaccharide-C47 product decreased with the increase in shear rate and declined by increasing the temperature up to 50 \ub0C. The rheological properties of exopolysaccharide-C47 product are influenced by the salt type and pH value. The exopolysaccharide product produced by L. garvieae C47 possesses valuable health benefits and has the ability to improve the weak structure of fermented camel milk