Green Communications: Techniques and Challenges

Green technology has drawn a huge amount of attention with the development of the modern world. Similarly with the development in communication technology the industries and researchers are focusing to make this communication as green as possible. In cellular technology the evolution of 5G is the next step to fulfil the user demands and it will be available to the users in 2020. This will increase the energy consumption by which will result in excess emission of co2. In this paper different techniques for the green communication technology and some challenges are discussed. These techniques include device-to-device communication (D2D), massive Multiple-Input Multiple-Output (MIMO) systems, heterogeneous networks (HetNets) and Green Internet of Things (IoT)

A Compact Size Implantable Antenna for Bio-medical Applications

Implantable antennas play a vital role in implantable sensors and medical devices. In this paper, we present the design of a compact size implantable antenna for biomedical applications. The antenna is designed to operate in ISM band at 915 MHz and the overall size of the antenna is 4×4×0.3mm3. A shorting pin is used to lower the operating frequency of the antenna. For excitation purpose a 50-ohm coaxial probe feed is used in the design. A superstrate layer is placed on the patch to prevent the direct contact between the radiating patch and body tissues. The antenna is simulated in skin layer model. The designed antenna demonstrates a gain of 3.22 dBi while having a -10 dB bandwidth of 240 MHz with good radiation characteristics at 915 MHz. The simulated results show that this antenna is an excellent candidate for implantable applications

A Tri-Band Implantable Antenna for Biotelemetry Applications

In this paper we propose a compact size rectangular implantable tri-band patch antenna for biotelemetry applications. Rogers RT6010 is used as substrate and superstrate material. The resonant frequency is further lowered by using a shorting pin which also reduces patch resistance. For excitation 50-ohm microstrip line is used. The antenna operates in MICS band (402405) MHz, ISM band (902-928) MHz and (2.4-2.48) GHz at 402 MHz, 915 MHz and 2.4 GHz. The gain of the antenna is 2.05 dBi, 2.67 dBi and 5.39 dBi with bandwidth of 120 MHz, 166 MHz and 190 MHz at relevant frequencies when simulated in a fat layer box. SAR values are within allowable limits. The simulated results show that the antenna is an excellent choice for implantable applications as it can be used for data transmission, wakeup signal and wireless power transfer by using three frequency bands

A New Approach to Information Extraction in User-Centric E-Recruitment Systems

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs

A Low Profile Antenna for Millimeter-Wave Body-Centric Applications

Millimeter-Wave (mm-Wave) frequencies are a front runner contender for the next generation body-centric wireless communications. In this paper, the design of a very low-profile antenna is presented for body-centric applications operating in the mm-Wave frequency band centered at 60 GHz. The antenna has an overall size of 14 × 10.5 × 1.15 mm 3 and is printed on a flexible printed circuit board. The performance of the antenna is evaluated in off-body, on-body, and bodyto-body communication scenarios using a realistic numerical phantom and verified through measurements. The antenna has a bandwidth of 9.8 GHz and offers a gain of 10.6 dBi in off-body (free space) configuration, while 12.1 dBi in on-body configuration. It also achieves an efficiency of 74% in off-body and 63% in on-body scenario. The small and flexible structure of the antenna along with excellent impedance matching, broad bandwidth, high gain, and good efficiency makes it a suitable candidate to attain simultaneous data transmission/reception at mm-Wave frequencies for the 5G body-centric applications

Implantable antennas for bio-medical applications

Biomedical telemetry has gained a lot of attention with the development in the healthcare industry. This technology has made it feasible to monitor the physiological signs of patient remotely without traditional hospital appointments and follow up routine check-ups. Implantable Medical Devices(IMDs) play an important role to monitor the patients through wireless telemetry. IMDs consist of nodes and implantable sensors in which antenna is a major component. The implantable sensors suffer a lot of limitations. Various factors need to be considered for the implantable sensors such as miniaturization, patient safety, bio-compatibility, low power consumption, lower frequency band of operation and dual-band operation to have a robust and continuous operation. The selection of the antenna is a challenging task in implantable sensor design as it dictates performance of the whole implant. In this paper a critical review on implantable antennas for biomedical applications is presented

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets