This paper focuses on a CDMA design study for future third-generation mobile and personal communication systems such as FPLMTS and UMTS. In the design study, a rigorous top down approach is adopted starting from the most essential objectives and requirements of universal third-generation mobile systems. Emphasis is laid on high flexibility with respect to the implementation of a wide range of services and service bit rates including variable rate and packet services. Flexibility in frequency and radio resource management, system and service deployment, and easy operation in mixed-cell and multioperator scenarios are further important design goals. The system concept under investigation is centered around an open and flexible radio interface architecture based on asynchronous direct-sequence CDMA with three different chip rates of approximately 1,5, and 20 Mchip/s

Design study for a CDMA-based third-generation mobile radio system

In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

/pdf/internet-of-things-for-current-covid-19-and-future-pandemics-1rwpef6nuc.pdf

Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study.

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems' decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper's main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review.

Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

In recent years, the Internet of Things (IoT) has drawn convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT enabled /linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Internet of Things for Current COVID-19 and Future Pandemics: An Exploratory Study

Classification and Detection of vessels from cluttered images is an actively researched field of computer vision. The complexity of the coastal area makes it more complex in distinguishing different vessels. Environment conditions and visual aspect of the vessels make classification problem more difficult. The visual aspect ratio is very small and similar to most type of vessels. Since the traditional CNNs methods are slow and not much accurate, we present the robust method for the classification of ships derived from Support Vector Machine, bag of features and Convolutional Neural Networks (CNNs). First of all Support Vector Machine which is a supervised transfer leaning technique is introduced to remove the false candidates. As the ships have wide range of variability, bag of features are applied to handle the diversified features of different categories of vessels. Finally CNNs framework is applied for the deep feature extraction to successfully classify the ships from database. The proposed algorithm is trained with 16 different categories of ships with a data set of more than 2700 images. The algorithm trains and validates the results in terms of confusion matrix and provides better accuracy to classify the ships. The proposed Convolutional Neural Networks (CNNs) model outperforms the other previous methods and gives an accuracy of 91.04 percentages.

Ship Detection from Highly Cluttered Images Using Convolutional Neural Network

Necessity is said to be the mother of invention and nowhere in recent memory has necessity been greater than during the COVID-19 pandemic. As the world struggles to contain the virus and diagnose and treat over a million affected patients, some forward-thinkers are looking to an unlikely source of support for healthcare professionals in combating the virus: drones. Unmanned Aerial Vehicles (UAVs), also known as drones, can play a significant role in military and civil emergency medicine. The main aim of this paper is to present real applications of drone technology that are becoming a game changer during the COVID-19 pandemic and to provide examples from all over the world.

Drone Technology - Game Changer to Fight Against COVID-19

Third-Generation based Wide Band Code Division Multiple Access (W-CDMA) networks have been characterized for high data rate services accessible to a large number of users. But multipath and co-channel interference puts a limit on system capacity, throughput and new multimedia information services like wireless internet or video streaming. Smart antenna technique that includes Direction of Arrival (DOA) approach and Adaptive Beam forming algorithm is a promising means to improve the over-all system capacity and to overcome limited spectrum problem by effectively reducing the multipath and co-channel interference. In this paper, DOA estimation is done by Multiple Signal Classification (MUSIC) algorithm and Minimum Variance Distortion less Response (MVDR) algorithm is used for adaptive beam forming that continuously adjusts the weight of antenna arrays for creating a beam to track the desired user automatically, and to place null towards the interference user. Here, we improve on the MVDR method in order to assign the weightings. Instead of using a single linear equality constraint, a variable constraint has been used to obtain the optimum weights that decrease the gain in the undesired direction and desired direction comes out with more gain and without distortion. Obtained results are analyzed for the conventional method and the proposed method by observing beam forming spectrum by first varying number of antenna elements and then by varying the spacing between elements. For both the cases, the performance is better for the proposed method in terms of lower noise floor, more relative power and sharper peaks in the desired user direction than conventional method.

http://ieeexplore.ieee.org/iel7/6917607/6918774/06918814.pdf

Modified Optimum Beamforming for Smart Antenna in WCDMA Network

This manuscript investigates the effect of inner gate engineering (IGE) on the linearity and harmonic distortion performance of junctionless (JL) silicon-nanotube (Si-NT) FETs for wireless applications. The three configurations of JL-Si-NT-FET namely DS-JL-Si-NT-FET (inner gate covers drain & source regions both in addition to channel region), D--JL-Si-NT-FET (inner gate covers only drain region and channel region), and S--JL-Si-NT-FET (inner gate covers only source region and channel region) are compared to find the best configuration for wireless applications. The simulation results divulge that S--JL-Si-NT-FET configurations shows the improved linearity and harmonic distortion performance in terms of the performance metrics HD2, HD3, VIP3, IIP3, 1-db compression point, and higher order coefficients of transconductance (gm2 & gm3). Consequently, the S-JL-Si-NT-FET is the best configuration to be used for RFIC design and wireless applications.

Linearity Performance and Harmonic Distortion Analysis of IGE Junctionless Silicon Nanotube-FET for Wireless Applications

Biometric Systems are the personal identification systems that use behavioral and physiological characteristics of a person. One of the main concerns in biometrics systems is template security. Fuzzy vault, a bio-cryptosystem, is used to provide security to the stored templates. Fuzzy vault has proven to be a very good security technique, nonetheless it lacks in providing revocability and security against correlation attacks. Thus for the enhancement of the security of fuzzy vault and to overcome the limitation of correlation attack, techniques like hybrid model and multimodal biometrics can be used. This paper gives a review of the above mentioned techniques, viz. hybrid and multimodal, and how they can be effective in enhancing the security of the system.

Techniques for Enhancing the Security of Fuzzy Vault: A Review

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