In this paper, the femtocell resource allocation in the Orthogonal Frequency Division Multiple Access (OFDMA) integrated cognitive radio (CR) networks has been demonstrated to optimize the network performance, while reducing the outage users at cell edges. The sub-band sharing flexibility not only improves the spectral efficiency of the dynamic FFR scheme against conventional approach but also it alleviates outage probability and further noticeable amount of improvement can be found in superior performance parameters such as capacity, CDF of SINR and overall cell throughput. We yield an analytical framework to ascertain the orthogonality of the subcarriers by compensating carrier frequency offset (CFO) to ensure better OFDMA performance in the proposed network model and illustrates the influence of frequency reuse factor (FRF) at cell edges via simulation results.

This research focuses on the problem of cell edge user’s coverage in the context of femtocell networks operating within the locality of macrocell border where pathloss, shadowing, Rayleigh fading have been included into the environment. As macro cell edge users are located far-away from the macro base station (MBS), so that, the underprivileged users (cell edge users) get assisted by the cognitive-femto base station (FBS) to provide consistent quality of service (QoS). Considering various environment factors such as wall structure, number of walls, distance between MBS and users, interference effect (i.e., co-tier and cross-tier), we compute downlink (DL) throughput of femto user (FU) for single input single output (SISO) system over a particular sub-channel, but also based on spectrum allocation and power adaptation, performance of two tier network is analyzed considering network coverage as the performance metric. Finally, the effectiveness of the scheme is verified by extensive matlab simulation.

This research focuses on the problem of cell edge user coverage in the context of femtocell networks operating within the locality of macrocell border. The macro cell-edge users get assisted by the cognitive-femto base station (FBS) to receive a consistent quality of service (QoS) because of their long distance from the macro base station (MBS). Considering various environment factors such as wall structure, number of walls, distance between macrocell and users, interference effect (i.e., co-tier and cross-tier), we compute not only the overall outage probability of single input single output (SISO) system and single input multiple output (SIMO) system users but also based on subcarrier and power allocation the performance of the two tier network is analysed with macrocell throughput as the performance metric. Finally, the effectiveness of the scheme is verified by extensive matlab simulation.

In OFDMA femtocell networks, the licensed spectrum of the macro users (MUs) are available to the femto users (FUs), on the condition that they do not spark off notable interference to the MUs. We contemplate wireless data for femto user (FU) / secondary user (SU) in cognitive radio (CR) networks where the frame structure split up into sensing and data transmission slots. Moreover, we consider soft frequency reuse (SFR) technique to improve secondary network throughput by increasing the macrocell edge user power control factor. SFR applies a frequency reuse factor (FRF) of 1 to the terminal located at the cell centre for that all base stations (BSs) share the total spectrum. But for the transmission on each sub-carrier the BSs are confined to a certain power level. However, more than 1 FRF uses for the terminals near to the macrocell edge area. In this context, we conceptualize the cognitive femtocell in the uplink in which the femtocell access point (FAP) initially perceive by sensing to find out the availability of MU after that FAP revamps its action correspondingly. Appropriately, when the MU is sensed to be non-existent, the FU transmits at maximum power. In other respect, the FAP make the best use of the transmit power of the FU to optimize the secondary network throughput concern to outage limitation of the MU. Finally, effectiveness of the scheme is verified by the extensive matlab simulation.

In this paper, we initially dealt with the issue of spectrum allocation among macro (or “licensed”) and Femto (or “unlicensed”) users in an orthogonal frequency division multiple access (OFDMA) femtocell network of non- ooperative game theoretic frequency reuse approach. We formulate the difficulty based on spectrum bidding. Here individual Secondary Users (SU) create an auction for the amount of bandwidth and every PU can share the frequency band among SUs by itself according to the intelligence from SUs without lowering its own performance. Here, we consider that the bidding is a non- ooperative game and one of its solutions is a Nash Equilibrium (NE). The femto base stations (FBSs) are grouped into different cluster for mitigating the undesired interference among them. The game theoretical method deals with the inter-cluster frequency clashes.We exemplified a link between utility function and the number of players by non-cooperative game theoretic approach to guide the spectrum sharing decision at the cell edges. The convergence of the development mechanism is rigorously scrutinized and extensive numerical outcomes are presented to illustrate their potential merits.

In the rapid development of wireless communications Femtocells provide tremendous improvement in coverage and quality of service for users. Macro-Femto based networks are envisioned to be the de-facto solution for providing ultrahigh speed communications in next generation mobile wireless networks. This paper studies two-tier Macro-Femto networks and proposes a collection of novel technologies to address the interference problems. First, a novel user association scheme is proposed that aims to optimize the load among Femto base stations (FBSs). Second, a near-optimal ergodic search algorithm is proposed to regulate the power consumption at Macro base stations (MBSs) and improve energy efficiency. Third, a channel access mechanism is proposed for FBSs that aims to minimize inter-tier interference. For the proposed system, CDF of SINR is derived and used for performance investigation. Simulation results show that the proposed system can significantly outperform a popular, conventional cognitive radio based system for all the considered simulation scenarios.

This work presents the evaluation of the downlink (DL) performance of a dual-layer cellular networks by using energy efficiency (EE) metric, where femto base stations (FBSs), macro base stations (MBSs) and users (FUs) form independent spatial Poisson point processes (PPPs). The proposed network model is developed by considering number of antennas at each BS alongside a single antenna at each user with the use of the conventional spectrum re-utilization approach. Then, Coverage probability and EE expressions for the duallayer cellular networks are exclusively derived analytically. It is also demonstrated that simulation results are almost in-line with the analytical one in the PPP-based model. While coverage probability deteriorates with less margin in the lower FBS density region compared to the scheme presented in [10] signalled not much turnaround of the network performance, EE in the lower and the upper FBS density regions are likely to remain between 6x10^-3 to 9.2 x10^-3 Bits/Joule and 4.6 x10^-3 to 7.1x10^-3 Bits/Joule, respectively. Proposed scheme tells us that it is firmly on course to match up with Vehicular Ad-hoc NETworks (VANET) applications without incurring high cost as EE, low latency, coverage probability and low power adaptability are back on good growth path.

In downlink orthogonal frequency division multiple access (OFDMA) networks, an effective way of using the limited wireless spectrum resources can significantly improve network response. This paper presents a game-theoretic scheme with anticoordinated players by incorporating adaptation of femto base station (FBS) transmit power, attenuation of interference and utility function for open access mode and closed access mode respectively. The deployment of femtocells in the networks is to produce improved energy efficiency (EE) and optimized reponse of payoff function. In open access mode, each user belongs to the operator’s network can connect to the FBS and in closed access case, only a specified set of users can privately couple to the FBS whereas in the early access scenario it only allows authentic subscribers to take the advantage of femtocell networks. Additionally, the operating principle of spectrum sharing scheme has been discussed in which FBS as a player acquire knowledge from utility responses of their strategic communications and revise their strategies at each level of the game process. Here, an FBS is regarded as a player in the game to select the users who are satisfied to a greatest extent and an FBS plays a role of mentor. Thereafter, the equilibrium concept has been invoked to aid the anti-coordinated players for the strategies. Besides, a femtocell power adaptation algorithm has also been introduced based upon the set of enabled femtocells who can be used to retain its blocking probability that guarantees convergence to the stable strategy of the game, where the FBS monitors the subscribers’ actions and gives only limited data exchange. The simulations demonstrate that the proposed algorithm attains a high quality performance such as rapid convergence, interference attenuation to a greatest extent, noticeable EE improvement etc. Finally, validate the simulation results with its rarely studied extension in cognitive femtocell networks.

The network coverage and energy efficiency issues in heterogeneous cognitive-femtocell networks over the macrocell network is studied. Cognitive functions in wireless network nodes are serviceable with the macrocell infrastructure to achieve a balance between two desirable but incompatible features: coverage and energy efficiency. There are two basic but related aspects of cognitive radios (CRs) in the context of wireless communications: optimum CRs for energy efficiency and the act of the functioning of CRs with energy efficiency. To fully utilise the cognitive capability, a dual-tier network architecture is assumed where both the macrocell and the femtocell have a bearing on the cognitive capability. Owing to the salient features of femtocells, they can improve the coverage and enhance the spectrum efficiency by reutilising the frequency spectrum allocated to the macrocell, although, the resulting intercell interference accompanied by the same frequency coverage cannot be underestimated. The effectiveness of the scheme is verified by extensive Matlab simulation.

In this article, State-of-the-art deep learning models are evaluated and their performances in X-ray image classification is reported.

This is a survey on human action recognition from various data modalities.

We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.

We present Spectral Differential Privacy (SpDP), a novel form of differential privacy designed to protect the frequency content of time series data that come from wide sense stationary stochastic processes. This notion is motivated by privacy needs in applications with time series data over unbounded time, such as smart meters. First, a notion of differential privacy on the space of (discretized) spectral densities is introduced. A Gaussian-like mechanism for SpDP is then presented that provides differential privacy to the spectral density. Next, a novel streaming implementation is developed to enable real-time use of the proposed mechanism. The privacy guarantee provided by SpDP is independent of the time duration over which data is collected or shared. In contrast, time-domain trajectory-level differential privacy (TrDP) will require noise with large variance to provide privacy over an extended time duration. The technique is numerically evaluated using smart meter data from a single home to compare the utility of SpDP to that of time-domain trajectory-level differential privacy. The noise added by SpDP is substantially smaller than that added by time-domain TrDP, particularly when privacy over long time horizons is sought by TrDP.

In fog computing, processing, network, and storage resources are placed close to the end users to assure a low latency in comparison to the latency experienced when accessing services in the cloud. One limitation of this solution, however, is that fog nodes are usually fixed, whereas demands are variable over time at all locations, resulting in underutilization of the fog resources as well as unnecessary provisioning of fog resources. One way for dealing with this problem is the employment of mobile nodes to cope with the variability in resource demand. This paper studies how unmanned aerial vehicles (UAVs) equipped with processing capabilities can be used in this perspective, and proposes a solution to the fog node location problem considering both fixed and mobile nodes. It proposes the UAV Fog Node Location (UFL) algorithm to evaluate potential replacements of fixed servers by UAVs. The proposed algorithm can be used for long term planning under the assumption of changes in the prices of UAVs. An evaluation of the problem using data generated by real mobile users shows that UAVs can improve the design of future fog networks. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

In this work, we have investigated polymer-based flexible antennas from commercial and modified polymers, which are competitive to rigid PCB technology. Classical designs of the patch and bow-tie antennas have been realized and showed that the realized gain can get up to 9.16dBi for the patch and 7.9dBi for the bow-tie antennas. The effects of the dielectric loss and conductivity on the antennas’ performance in S-band have been analyzed in order to find limits for further material engineering and the optimum trade-off between microwave and mechanical performance. The bending effects have been investigated, and it has been found that E-plane bend inside can boost the antenna gain from 8.6dBi to 10.1dBi with the frequency shift from 2.5 GHz to 2.4 GHz for the patch and 7.9dBi to 11.3dBi at 3.1 GHz for the bow-tie antennas. The non-classical π-shaped conductors’ edges lead to additional fringing fields, which have an effect on the antenna’s gain and can be explored and exploited for further performance improvements. The new recipes for low-loss, low-Dk dielectric materials, and chemical integration between conducting

Position sensitive CdZnTe Compton imaging cameras are currently being studied for their use of proton beam range verification for radiotherapy applications. This work presents the use of an experimental large volume CdZnTe detector for the detection of prompt gamma rays that are emitted from proton-nuclei interaction within plastic (C2H4) targets. Two experiments were conducted where the incident angle and the dose profile of the beam were varied. The energy spectra from these experiments show that the angle at which the beam enters the target can influence the photopeak to Compton continuum ratios, resulting in more than 18% increase at 718 keV when the beam is parallel to the detector. Images of the 718 keV and 4.44 MeV characteristic prompt gamma ray emission from carbon-proton interactions are reconstructed using list-mode maximum likelihood expectation maximization (MLEM). Images from these prompt gamma emissions line up well with the expected location of the proton beam within the plastic targets.

In this paper, we analyze the low-SNR behavior of the cross-receiver mutual information (CMI) between two received signals corrupted by uncorrelated, additive Gaussian noise. This framework has use in distributed, passive sensor applications, such as passive radar and collaborative opportunistic navigation. For Gaussian and BPSK signaling, the CMI can be expressed in terms of the effective SNR between the receivers. On-off keying (OOK), while not optimal in terms of spectral efficiency for a single-receiver channel, is shown to have greater CMI than Gaussian or BPSK signaling. This is in spite of the fact that, given the same received SNRs, all three source distributions have the same linear correlation coefficient. This indicates that for OOK sources, effective SNR and correlation coefficient are not meaningful descriptors for passive receivers. Full-length version of conference paper submission.

This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence. The Pashto character database developed in this work is available at GitHub - mudaser37/pashtoCharacterDataset