Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.

Virtual reality (VR) head-mounted displays (HMD) appear to be effective research tools, which may address the problem of ecological validity in neuropsychological testing. However, their widespread implementation is hindered by VR induced symptoms and effects (VRISE) and the lack of skills in VR software development. This study offers guidelines for the development of VR software in cognitive neuroscience and neuropsychology, by describing and discussing the stages of the development of Virtual Reality Everyday Assessment Lab (VR-EAL), the first neuropsychological battery in immersive VR. Techniques for evaluating cognitive functions within a realistic storyline are discussed. The utility of various assets in Unity, software development kits, and other software are described so that cognitive scientists can overcome challenges pertinent to VRISE and the quality of the VR software. In addition, this pilot study attempts to evaluate VR-EAL in accordance with the necessary criteria for VR software for research purposes. The VR neuroscience questionnaire (VRNQ; Kourtesis et al., 2019b) was implemented to appraise the quality of the three versions of VR-EAL in terms of user experience, game mechanics, in-game assistance, and VRISE. Twenty-five participants aged between 20 and 45 years with 12-16 years of full-time education evaluated various versions of VR-EAL. The final version of VR-EAL achieved high scores in every sub-score of the VRNQ and exceeded its parsimonious cut-offs. It also appeared to have better in-game assistance and game mechanics, while its improved graphics substantially increased the quality of the user experience and almost eradicated VRISE. The results substantially support the feasibility of the development of effective VR research and clinical software without the presence of VRISE during a 60-minute VR session.

Research suggests that the duration of a VR session modulates the presence and intensity of VRISE, but there are no suggestions regarding the appropriate maximum duration of VR sessions. The implementation of high-end VR HMDs in conjunction with ergonomic VR software seems to mitigate the presence of VRISE substantially. However, a brief tool does not currently exist to appraise and report both the quality of software features and VRISE intensity quantitatively. The VRNQ was developed to assess the quality of VR software in terms of user experience, game mechanics, in-game assistance, and VRISE. Forty participants aged between 28 and 43 years were recruited (18 gamers and 22 non-gamers) for the study. They participated in 3 different VR sessions until they felt weary or discomfort and subsequently filled in the VRNQ. Our results demonstrated that VRNQ is a valid tool for assessing VR software as it has good convergent, discriminant, and construct validity. The maximum duration of VR sessions should be between 55-70 minutes when the VR software meets or exceeds the parsimonious cut-offs of the VRNQ and the users are familiarized with the VR system. Also. the gaming experience does not seem to affect how long VR sessions should last. Also, while the quality of VR software substantially modulates the maximum duration of VR sessions, age and education do not. Finally, deeper immersion, better quality of graphics and sound, and more helpful in-game instructions and prompts were found to reduce VRISE intensity. The VRNQ facilitates the brief assessment and reporting of the quality of VR software features and/or the intensity of VRISE, while its minimum and parsimonious cut-offs may appraise the suitability of VR software. The findings of this study contribute to the establishment of rigorous VR methods that are crucial for the viability of immersive VR as a research and clinical tool.

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “\emph{where to look}’‘ and “\emph{when to look}”. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty. A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.

Obtaining large spectral efficiency (SE) and energy efficiency (EE) subject to quality of experience (QoE) is one of the prime concerns for the wireless next generation networks, however a major confrontation with its trade-off which is becoming apparent while optimizing both SE and EE parameters concurrently. In this work, an analytical framework for a cognitive-femtocell network is proposed to be dealt with and overcome the situations regarded as unwelcome. Here, the conflict of SE-EE trade-off in downlink (DL) transmission is expressed methodically by Pareto Optimal Set (POS) based on a multi-empirical most effective use of a resource scheme as a function of femto base station (FBS) and macro base station (MBS) transmit power and base station (BS) density, respectively. Then, SE and EE are formulated in a utility function by applying Cobb-Douglas production function to transform the multi- mpirical difficulty into the single-empirical optimization case. Besides, it is analytically shown that the SE-EE trade-off can be optimize through a distinctive universal optimum among the Pareto optimal by fine tuning the weighting metric other than BS transmit power and density, respectively. Simulation results validate that it is possible to obtain the EE-SE trade-off with SINR threshold at different weighting factor.

Strictures caused by esophageal cancer can narrow down the esophageal lumen, leading to dysphagia. Palliation of dysphagia has driven the development of a Robotic Soft Esophagus (RoSE) to provide a novel in vitro platform for esophageal stent testing and food viscosity studies. In RoSE, peristaltic wave generation and control were done in an open-loop manner since the conduit lacked visibility and embedded sensing capability. Hence, in this work, RoSE version 2.0 (RoSEv2.0) was designed with embedded Time Of Flight (TOF) and pressure sensors to measure conduit displacement and air pressure, respectively, for modeling and control. Model Predictive Control (MPC) of RoSEv2.0 was implemented to govern the peristalsis and air pressure profile autonomously. The implemented MPC used Sparse Identification Nonlinear Dynamics with Control (SINDYC) models to estimate the future states of ROSEv2.0. The dynamic models were discovered from the TOF and pressure sensors captured data. Peristalsis waves of speed 20 mm/s, wavelength 75 mm, and amplitudes 5, 7.5, and 10 mm were successfully generated by the MPC. Additionally, RoSEv2.0 with the MPC was employed to perform stent migration testing with various food boluses consistencies.

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

The conventional mental healthcare regime follows a reactive, symptom-focused, and episodic approach in a non-continuous manner, wherein the individual discretely records their biomarker levels or vital signs for a short period prior to a subsequent doctor’s visit. Recognizing that each individual is unique and requires continuous stress monitoring and personally tailored treatment, we propose a holistic hybrid edge-cloud Wearable Internet of Things (WIoT)-based online stress monitoring solution to address the above needs. To eliminate the latency associated with cloud access, appropriate edge models—Spiking Neural Network (SNN), Conditionally Parameterized Convolutions (CondConv), and Support Vector Machine (SVM)—are trained, enabling low-energy real-time stress assessment near the subjects on the spot. This work leverages design-space exploration for the purpose of optimizing the performance and energy efficiency of machine learning inference at the edge. The cloud exploits a novel multimodal matching network model that outperforms six state-of-the-art stress recognition algorithms by 2-7% in terms of accuracy. An offloading decision process is formulated to strike the right balance between accuracy, latency, and energy. By addressing the interplay of edge-cloud, the proposed hierarchical solution leads to a reduction of 77.89% in response time and 78.56% in energy consumption with only a 7.6% drop in accuracy compared to the IoT-Cloud scheme, and it achieves a 5.8% increase in accuracy on average compared to the IoT-Edge scheme.

Automatic repeat request (ARQ) schemes, and in particular hybrid-ARQ (HARQ) schemes, which jointly adopt forward error correction (FEC) and ARQ, are essential to provide reliable data transmission in wireless communications systems. However, the feedback from the receiver to the transmitter and the retransmission process used in ARQ incurs significant cost in terms of power efficiency, throughput, computational power and delay. Unfortunately, such drawbacks can limit their applications to several current and emerging technologies. More specifically, the increasing number of wireless users has create spectrum scarcity, relying on small-size batteries create power constraints, deployment of real-time applications boost the demand for ultralow delay networks, and the ultra-small low-cost internet of things (IoT) devices has limited signal processing and computation capabilities. Consequently, extensive research efforts have been dedicated to overcome the limitations inherent in HARQ. This survey paper provides an extensive literature review of the state-of-the-art HARQ techniques and discusses their integration in various wireless technologies. Moreover, it provides insights on advantages and disadvantages of particular ARQ types and discusses open problems and future directions.

We propose an analytical framework of significant influence of deployment of femto base stations (FBSs) in a dual-tier network constituted of macro base stations (MBSs). As because of uncoordinated deployment, FBSs results in destructive interference to MBSs and viceversa. But, throughput efficiency of indoor (Femtocell) and outdoor (Macrocell) environment substantially increases due to an optimum reutilization of available spectrum. The quality of service (QoS) is further getting optimized in terms of throughput and network coverage because of incorporation of steerable beamforming. Finally, the effectiveness of the scheme is verified by extensive matlab simulation.

This research work explores small cell densification as a key technique for next generation wireless network (NGWN). Small cell densification comprises space (i.e, dense deployment of femtocells) and spectrum (i.e., utilization of frequency band at large). The usage of femtocells not only improves the spectral efficiency (SE) of the Heterogeneous two-tier networks against conventional approach, but also it alleviates outage probability and enhances the achievable capacity. We yield an analytical framework to establish the density of the femto base station (FBS) to a monotonically increasing or decreasing function of distance or radius, respectively. This ensures the enhanced performance in spectrum sharing Orthogonal Frequency Division Multiple Access (OFDMA) femtocell network models. We also illustrate the influence of active Femto users (i.e., users in femtocells, and they are usually low mobility and located closer to the cell centre with less fading), cluster size (i.e., a group of adjacent macrocells which use all of the systems frequency assignments) via simulation results.

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 Devision Multiple Access (OFDMA) based dual-layer femtocell networks. This research contribution will lead to basic coin in the design of next generation (5G) wireless networks. This manuscript exemplifies the trade off issue of energy efficiency (EE) and spectral efficiency (SE) with both cooperative and non-cooperative architecture in cognitive femtocell networks. The pivotal concepts for each technology are described, along with their potential impact on 5G and the research challenges that remain. Further, the trade-off between EE and SE is reposed with respect to the state of the art. The obtained insightful observations from the trade-off analysis of EE ans SE can lead to provide design guideline for 5G wireless Networks.

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 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.