Wireless Personal Communications最新文献

In this paper, a multiband microstrip antenna of compact size (0.2 × 0.25(:{lambda:}_{0}^{2}=0.05{lambda:}_{0}^{2})(:text{a}text{t}:text{l}text{o}text{w}text{e}text{r}:text{r}text{e}text{s}text{o}text{n}text{a}text{n}text{t}:text{f}text{r}text{e}text{q}text{u}text{e}text{n}text{c}text{y}:1.63:text{G}text{H}text{z})) is designed by loading multiple notches in the patch of antenna. The patch of antenna consist two L-shaped and two rectangular shaped notches. The modified patch of antenna provides triple resonating bands from 1.58 to 1.71 GHz, 1.89 to 2.06 GHz and 2.81 to 2.93 GHz under the − 10 dB scale resonating at frequency 1.63 GHz, 2 GHz and 2.87 GHz respectively. The bandwidth of triple band antenna is obtained 7.9% (130 MHz), 8.61% (170 MHz) and 4.18% (120 MHz) with − 16.6 dB, − 26.99 dB and − 31.88 dB reflection coefficient at resonating frequency. The lower and middle resonances are due to L-shape notches while higher resonance is due to rectangular shape notches. The presented antenna is fed by microstrip line feed of 50 Ω and the simulation is performed by IE3D software. It shows 2.6−3.1 dB, 2.9−3.5 dB and 4.2−5 dB simulated gain and 85−93%, 87−95% and 88−96% antenna efficiency in three resonating bands. The three resonating bands of proposed antenna can be used for the application of different wireless communications in L and S-bands. The proposed work is also compared with the previous published articles to find out the improvement.

This research examines the performance of two Z-shaped dielectric resonator antenna designs that employ silicon substrate material and operate at a frequency of 10 terahertz. CST software was used to simulate and create a two-Z shape DRA. Results of enhanced gain are analyzed and compared with earlier research. The suggested Two Z shape DRA has a gain of 7.31 dB and a return loss of -15.9 dB.The two Z-shaped dielectric resonator antennas have an impedance bandwidth of 66.66%.The VSWR of two Z-shaped dielectric resonator antennas is around 2 dB, which is acceptable. This proposed approach may be suitable for a wide range of communication applications, including wireless and terahertz applications.

A minimal interrupted communication link set-up is the primary objective of the medium access control (MAC) layer. The MAC layer is responsible for accessing the communication channel. At MAC, the control information is shared through the control channel to select a collision-free data channel. Therefore, the sharing of control information prior to data channel selection plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Random data channel selection is used in conventional CR scenarios. The random selection lacks in performance due to the unpredictability of primary user (PU) appearances in the data channels. This unpredictability in PU appearances will increase the number of handoffs in the scenario and sometimes may cause communication link failure. These adverse effects can be avoided if a common data channel selection scheme is implemented. The common data channel selection scheme selects a channel that has fewer chances of PU appearance. In the reported research paper, the work has been carried out in two parts. In the first part, a primary user free channel list (PCL) is formed at each node. Each node shares its PCL with all neighbour nodes. The sharing of PCL helps in implementing a common data channel selection scheme among nodes. The selected common channel is used for data transmission. The conventional OSI layered structure is used to observe the performances. In the second part, a cross-layer framework is proposed to improve the performance. The cross-layering has been done between the network and MAC layer. The proposed cross-layer framework helps in sharing PCL in an efficient manner. The need for an extra beacon to transfer PCL is avoided in the proposed cross-layer design. The performance of control overhead and average delay becomes better. The simulation results show that the proposed cross-layer design approach performs satisfactorily in terms of packet delivery ratio, average throughput, average delay, average energy consumption and control overhead. The observed results are also tested in the worst scenarios. The proposed scheme has applications in small throughput scenarios like cognitive radio aided sensor networks.

Deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. The shortage of large-sized datasets that are also adequately annotated is a key barrier that is preventing the continued advancement of deep learning models used in medical image analysis, despite the effectiveness of these models. Over the course of the previous 5 years, a great number of research have concentrated on finding solutions to this problem. In this work, we present a complete overview of the use of deep learning techniques in a variety of medical image analysis tasks by reviewing and summarizing the current research that have been conducted in this area. In particular, we place an emphasis on the most recent developments and contributions of state-of-the-art semi-supervised and unsupervised deep learning in medical image analysis. These advancements and contributions are shortened based on various application scenarios, which include image registration, segmentation, classification and detection. In addition to this, we explore the significant technological obstacles that lie ahead and provide some potential answers for the ongoing study.

Medical records are transmitted between medical institutions using cloud-based Electronic health record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic constrained message authentication (DCMA) technique is used in the proposed system to encrypt the outsourced medical data by using symmetric key cryptography, which uses the Seagull optimization algorithm (SOA) to choose the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The results of the proposed model are evaluated using performance metrics, and the model attained a security of about 98.58%, which is proven to be superior because it adopts advanced random secret key generation, which adds more security to the system.

Orthogonal Time Frequency Space (OTFS) modulation is proposed to support high-speed and reliable vehicular communication scenarios, which is a novel two-dimensional (2D) delay-Doppler domain modulation technique. Compared with traditional modulation schemes such as orthogonal frequency division multiplexing (OFDM), OTFS has displayed better performance in very high-mobility wireless channels. In this work, we have proposed the novel VLSI architecture for OTFS in the Additive White Gaussian Noise (AWGN) channel condition and shown the resource utilization report of our proposed architecture to implement on Field Programmable Gate Array (FPGA) board, along with the relationship between inputs and outputs of the OTFS signal in the delay-Doppler domain.

Radio frequency (RF) is perceived as one of the most popular wireless transmission candidate in the last decade however due to ever-increasing current generation bandwidth demands; it becomes incompetent to fulfil the requirements. A futuristic and high speed data transmission technique is replacing RF nowadays i.e. Free space optical communication (FSOC) because of its great potentials. Future generation communication systems require competent FSOC technology to sustain wireless traffic in 5G and 6G services. Because the features of FSOC and RF are complimentary, combining the two technologies is seen as a potential way to enable future communication systems. Hybrid RF/FSOC is a good way to overcome the limits of separate systems while still taking use of the beneficial aspects of each technology. Wireless systems use e.g. in hybrid RF/FSOC can increase individual network performance in terms of performance, dependability, and economical operations. In this work, a detailed review on FSOC systems is presented by doing the extensive literature review of reported research. Review covered key ideas consisting all sorts of FSOC systems, FSOC design with multiple and single beams, as part of the evaluation. The examination of rainfall and hazy effects on FSO signal transmission is also included in the review. The key benefits, future potential, and obstacles that must be addressed in order to successfully implement FSOC for 5G paradigms are presented.

A baby’s first spoken communication comes through crying. Before learning to convey their psychological/physiological needs or feelings using language, babies typically express how they feel by crying. Crying is a reaction to an inducement like pain, discomfort or hunger. Nevertheless, it is difficult sometimes to understand why a baby is crying. This will be annoying for a parents/guardian/caretaker, and therefore in this work, we are proposing an infant cry analysis and classification system to classify the kinds of crying of babies to assist parents/guardian/caretaker and attend to the needs of the babies. Presently, five distinct kinds of infant cries are identified: hunger (Neh), belly pain (Eairh), tiredness (Owh), discomfort (Heh) and burping (Eh). The database of this study consists of 456 audio recordings of 7 s each of 0–22-week-old babies. Feature extraction from each crying frame is carried out using Mel-frequency cepstral coefficients and the sequential forward floating selection is later used to choose highly discriminative features. Support Vector Machine and Random Forest classifiers are used for classification of infant crying. The results of the experiments has shown the performance of the proposed system with a accuracy of classification of 78% and 90.8% for Support Vector Machine and Random forest classifiers respectively.

The proliferation of the ever-increasing number of highly heterogeneous smart devices and the emerging of a wide range of diverse applications in 5G mobile network ecosystems impose to tackle new set of raising challenges related to agile and automated service orchestration and management. Fully leveraging key enablers technologies such as software defined network, network function virtualization and machine learning capabilities in such environment is of paramount importance to address service function chaining (SFC) orchestration issues according to user requirements and network constraints. To meet these challenges, we propose in this paper a deep reinforcement learning (DRL) approach to investigate online quality of experience (QoE)/quality of service (QoS) aware SFC orchestration problem. The objective is to fulfill intelligent, elastic and automated virtual network functions deployment optimizing QoE while respecting QoS constraints. We implement DRL approach through Double Deep-Q-Network algorithm. We investigate experimental simulations to apprehend agent behavior along a learning phase followed by a testing and evaluation phase for two physical substrate network scales. The testing phase is defined as the last 100 runs of the learning phase where agent reaches on average QoE threshold score ((QoE_{Th-Sc})). In a first set of experiments, we highlight the impact of hyper-parameters (Learning Rate (LR) and Batch Size (BS)) tuning on better solving sequential decision problem related to SFC orchestration for a given (QoE_{Th-Sc}). This investigation leads us to choose the more suitable pair (LR, BS) enabling acceptable learning quality. In a second set of experiments we examine DRL agent capacity to enhance learning quality while meeting performance-convergence compromise. This is achieved by progressively increasing (QoE_{Th-Sc}).

This paper describes a novel multiband monopole cavity-backed (MCB) microstrip antenna design with a wide axial ratio bandwidth (ARBW). This study explores the challenge of achieving a wide ARBW in MCB antennas for C-band applications. To attain the desired parameters, the proposed MCB antenna prints a circularly polarized monopole radiator onto the substrate. The cavity-array structures, known as resonating elements, strategically positioned vias on both the monopole-radiating patch and the substrate. The proposed MCB, which consists of semi-circular ring slots, improves impedance matching, radiation, and bandwidth (BW). We present two MCB antennas that meet specific frequency ranges and polarization. The first antenna, with a 5 mm patch size, covers the S, C, and X bands, while the second antenna, with a 3 mm patch size, resonates at the X and C bands. Furthermore, the 5 mm and 3 mm MCB antennas have wide ARBWs of 400 MHz (5.00–5.4 GHz) and 610 MHz (4.87–5.48 GHz), respectively. To determine its superiority, we compare the proposed MCB’s performance to that of other antenna designs. We also examine how the proposed MCB functions in two other configurations: one with circular cavities and one without, as well as semi-circular rings of varied radius. We validate the proposed antenna designs by comparing their simulated and measured results. We compare the suggested antenna to other antennas in terms of ARBW, gain, size, efficiency, BW, and applications.