Miracle-dz/UniFAR-FTUs huggingface dataset:
Cache-Aware Multicast Beamforming Design for Multicell Multigroup Multicast

To promote the massive video content delivery and to realize the long-term overall cost, the caching and computing functions have to be installed at some intermediate nodes within the networks. This paper presents a cache-aware multicast beamforming design for multicell multigroup multicast, where information-centric networking and mobile edge computing techniques are brought in the multicell multicast system to cache and transcode the contents passing through the nodes. The proposed cache-aware multicast beamforming design jointly optimizes the multicast mode, the caching strategy, and the network-wide beamforming vector, and focuses on minimizing the energy cost of the caching, computing, and communications. To make the formulated problem tractable, a two step method is proposed in this paper, where the first step is devoted to the cache-aware multicast approach design, while the second step is focused on the sparse multicast beamforming design. Furthermore, in order to promote the stabilization of the system, we further design a robust joint optimization strategy for the scenario with the imperfect channel state information. Extensive simulations are conducted to evaluate the performance of our proposed schemes.
Cloud-Supported Formation Control of Second-Order Multiagent Systems

This paper addresses a formation problem for a network of autonomous agents with second-order dynamics and bounded disturbances. Coordination is achieved by having the agents asynchronously upload (download) data to (from) a shared repository, rather than directly exchanging data with other agents. Well-posedness of the closed-loop system is demonstrated by showing that there exists a lower bound for the time interval between two consecutive agent accesses to the repository. Numerical simulations corroborate the theoretical results.
Exploiting Parallelism With Random Linear Network Coding in High-Speed Ethernet Systems

Parallelism has become one of the key architectural features in 40-/100-/400-Gb Ethernet multi lane distribution (MLD) standards. The MLD packetizes and distributes traffic adaptively over parallel lanes and maps them to parallel network interfaces for wide area transmission, typically over optical networks. As such, the MLD creates not only new network topology abstractions but also enables modular implementations of various new features to improve the system performance. In this paper, we study the performance of the parallelized Ethernet in combination with erasure coding and more specifically random linear network coding (RLNC). We present a novel theoretical modeling framework, including the derivation of upper and lower bounds of differential delay and the resulting receiver queue size—a critical performance measure in the high-speed Ethernet. The results show benefits of a combined usage of parallelism and RLNC: with a proper set of design parameters, the differential delay and the receiver buffer size can be reduced significantly, while cross-layer design and path computation greatly simplified.
Power-Based Broadband RF Interference Detector for Wireless Communication Systems

As broadband radio frequency interference (RFI) affects various systems operating radio frequencies, it has to be detected and mitigated. Accordingly, a computationally simple power-based detector (PD) is investigated. By assuming reception over the Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channels, exact closed-form expressions for the probabilities of RFI detection and of false alarm are derived and validated via simulations. Simulations also demonstrate that PD outperforms kurtosis detector.
AP-Atoms: A High-Accuracy Data-Driven Client Aggregation for Global Load Balancing

In Internet mapping, IP address space is divided into a set of client aggregation units, which are the finest-grained units for global load balancing. Choosing the proper level of aggregation is a complex problem, which determines the number of aggregation units that a mapping system has to maintain and client redirection. In this paper, using Internet-wide measurements provided by a commercial global load balancing service provider, we show that even for the best existing client aggregation, almost 17% of clients have latency more than 50 ms apart from the average latency of clients in the same aggregation unit. To address this, we propose a data-driven client aggregation, AP-atoms, which can trade off scalability for accuracy and adapt for changing network conditions. Since AP-atoms are obtained from the passive measurements of existing traffic between server providers and clients, no extra measurement overheads are incurred. Our experiments show that by using the same scale of client aggregations, AP-atoms can reduce the number of widely dispersed clients by almost $2\times $ and the 98th percentile difference in clients’ latencies by almost 100 ms.
Robust Representations for unconstrained Face Recognition and its Applications

Title of dissertation: ROBUST REPRESENTATIONS FOR UNCONSTRAINED FACE RECOGNITION AND ITS APPLICATIONS Jun-Cheng Chen, Doctor of Philosophy, 2016 Dissertation directed by: Professor Rama Chellappa Department of Computer Science Face identification and verification are important problems in computer vision and have been actively researched for over two decades. There are several applications including mobile authentication, visual surveillance, social network analysis, and video content analysis. Many algorithms have shown to work well on images collected in controlled settings. However, the performance of these algorithms often degrades significantly on images that have large variations in pose, illumination and expression as well as due to aging, cosmetics, and occlusion. How to extract robust and discriminative feature representations from face images/videos is an important problem to achieve good performance in uncontrolled settings. In this dissertation, we present several approaches to extract robust feature representation from a set of images/video frames for face identification and verification problems. We first present a dictionary approach with dense facial landmark features. Each face video is segmented into K partitions first, and the multi-scale features are extracted from patches centered at detected facial landmarks. Then, compact and representative dictionaries are learned from dense features for each partition of a video and then concatenated together into a video dictionary representation for the video. Experiments show that the representation is effective for the unconstrained video-based face identification task. Secondly, we present a landmark-based Fisher vector approach for video-based face verification problems. This approach encodes over-complete local features into a highdimensional feature representation followed by a learned joint Bayesian metric to project the feature vector into a low-dimensional space and to compute the similarity score. We then present an automated system for face verification which exploits features from deep convolutional neural networks (DCNN) trained using the CASIA-WebFace dataset. Our experimental results show that the DCNN model is able to characterize the face variations from the large-scale source face dataset and generalizes well to another smaller one. Finally, we also demonstrate that the model pre-trained for face identification and verification tasks encodes rich face information which benefit other face-related tasks with scarce annotated training data. We use apparent age estimation as an example and develop a cascade convolutional neural network framework which consists of age group classification and age regression, and a deep networks is fine-tuned using the target data. ROBUST REPRESENTATIONS FOR UNCONSTRAINED FACE RECOGNITION AND ITS APPLICATIONS
Secure Relay Selection for Two Way Amplify-and-Forward Untrusted Relaying Networks

Relay assisted communication helps to extend the transmission coverage area. In more realistic scenarios, the available relays are untrusted, which try to decode the confidential signals during their main relaying function. Thus, these untrusted relays are regarded as potential eavesdroppers, and the confidential signals are vulnerable. A positive secrecy rate can be achieved at the physical layer without any cryptography, in particular, the mutual interference achieved from two-way relaying network can be used to secure the confidential signals against eavesdroppers attack. In this paper, the relay selection of two-way untrusted relays network is presented, and a directed beamforming is aligned to zero force the transmission at all other non-selected relays. Particularly, we propose a selection scheme based on max–min criterion, which requires an exhaustive search. Then, the lower bound (LB) of the secrecy rate is introduced to minimize the complexity of the selection criterion. The closed forms of the secure outage probability (SOP) for the max–min LB scheme is formulated and compared with other schemes. The simulations results validate the proposed analytical forms, and the performance of max–min LB scheme appears to be quite close to that of the max–min scheme with limited complexity.
Joint Coverage Enhancement by Power Allocation in Poisson Clustered Out-of-Band D2D Networks

In this paper, we consider a Poisson clustered out-of-band device-to-device (D2D) network operating in Rayleigh fading channels and propose two optimization schemes based on the joint coverage probability to allocate the transmit power to D2D user equipment (DUE) in both the target cluster and the whole network. The joint coverage probability accounts for the spatial correlation of the interference among the DUE. With a differing amount of location information being available among the DUE in centralized and localized communication solutions, the joint coverage probability is formulated based on the coherent, non-coherent, and single-cluster approximated pairwise coverage probabilities. By maximizing the joint coverage probability formulated in this way, the robustness of the network can be enhanced. Meanwhile, to fulfill the green aspect of future 5G communications, the battery life of the DUE will be prolonged by minimizing the power consumption for a given joint coverage probability requirement. The formulations of the two optimization problems mentioned above are not convex but are transformed to convex by using geometric programming. The simulation results show that, compared with the conventional LTE open-loop and fixed power schemes, the joint coverage probability and power consumption are, respectively, optimized by applying the proposed schemes.
Survey on features for fingerprint indexing

Nowadays, several biometric databases already contain millions of entries of individuals. With an increasing number of enrolled individuals, the response time of queries grows and can become critical. Fingerprint indexing offers a set of techniques to reduce the workload of entries, which have to be compared thoroughly. This work surveys research on such techniques. It focuses on the features of fingerprints, which are used as input. This survey also provides an assessment of the quality of the body of research in this field. Deficiencies herein are identified, e.g. there is a lack of common datasets and metrics used for testing.
TCP Alternative Backoff with ECN (ABE)

Active Queue Management (AQM) mechanisms allow for burst tolerance while enforcing short queues to minimise the time that packets spend enqueued at a bottleneck. This can cause noticeable performance degradation for TCP connections traversing such a bottleneck, especially if there are only a few flows or their bandwidth-delay- product is large. The reception of a Congestion Experienced (CE) ECN mark indicates that an AQM mechanism is used at the bottleneck, and therefore the bottleneck network queue is likely to be short. Feedback of this signal allows the TCP sender-side ECN reaction in congestion avoidance to reduce the Congestion Window (cwnd) by a smaller amount than the congestion control algorithm's reaction to inferred packet loss. This specification therefore defines an experimental change to the TCP reaction specified in RFC3168, as permitted by RFC 8311.
Signaling Maximum SID Depth (MSD) Using OSPF

This document defines a way for an Intermediate System to Intermediate System (IS-IS) router to advertise multiple types of supported Maximum SID Depths (MSDs) at node and/or link granularity. Such advertisements allow entities (e.g., centralized controllers) to determine whether a particular SID (Segment ID) stack can be supported in a given network. This document only defines one type of MSD (Base MPLS Imposition), but defines an encoding that can support other MSD types. This document focuses on MSD use in a Segment Routing enabled network, but MSD may also be useful when Segment Routing is not enabled.
Simultaneous Positioning and Orientating for Visible Light Communications: Algorithm Design and Performance Analysis

Visible light communication (VLC) based simultaneous positioning and orientating (SPAO), using received signal strength (RSS) measurements, is studied in this paper. RSS-based SPAO for VLCs of great challenge as it is essentially a non-convex optimization problem due to the nonlinear RSS model. To address this non-convexity challenge, a novel particle-assisted stochastic search (PASS) algorithm is proposed. The proposed PASS-based SPAO scheme does not require the knowledge of the height of receiver, the perfect alignment of transceiver orientations or inertial measurements. This is a huge technical improvement over the existing VLC localization solutions. The algorithmic convergence is established to justify the proposed PASS algorithm. In addition, a closed-form Cramer–Rao lower bound (CRLB) on localization error is derived and analyzed to gain insights into how the VLC-based SPAO performance is related to system configurations. It is shown that the receiver's position and orientation accuracy is linear with signal-to-noise ratio and direction information. In addition, the position accuracy decays with six powers of the transceiver distance, while the orientation accuracy decays with four powers of the transceiver distance. Finally, simulation results verify the performance gain of the proposed PASS algorithm for VLC-based SPAO.
Optimal Stochastic Signaling Under Average Power and Bit Rate Constraints

The optimal stochastic signaling based on the joint design of prior distribution and signal constellation is investigated under an average bit rate and power constraints. First, an optimization problem is formulated to maximize the average probability of correct decision over the set of joint distribution functions for prior probabilities and the corresponding constellation symbols. Next, an alternative problem formulation, for which the optimal joint distribution is characterized by a randomization among at most three mass points, is provided, and it is shown that both formulations share the same solution. Three special cases of the problem are investigated in detail. First, in the absence of randomization, the optimal prior probability distribution is analyzed for a given signal constellation and a closed-form solution is provided. Second, the optimal deterministic pair of prior probabilities and the corresponding signal levels are considered. Third, a binary communication system with scalar observations is investigated in the presence of a zero-mean additive white Gaussian noise, and the optimal solution is obtained under practical assumptions. Finally, numerical examples are presented to illustrate the theoretical results. It is observed that the proposed approach can provide improvements in terms of average symbol error rate over the classical scheme for certain scenarios.
Device-Customized Multi-Carrier Network Access on Commodity Smartphones

Accessing multiple carrier networks (T-Mobile, Sprint, AT&T, and so on) offers a promising paradigm for smartphones to boost its mobile network quality. However, the current practice does not achieve the full potential of this approach because it has not utilized fine-grained, cellular-specific domain knowledge. Our experiments and code analysis discover three implementation-independent issues: 1) it may not trigger the anticipated switch when the serving carrier network is poor; 2) the switch takes a much longer time than needed; and 3) the device fails to choose the high-quality network (e.g., selecting 3G rather than 4G). To address them, we propose iCellular, which exploits low-level cellular information at the device to improve multi-carrier access. iCellular is proactive and adaptive in its multi-carrier selection by leveraging existing end-device mechanisms and standards-complaint procedures. It performs adaptive monitoring to ensure responsive selection and minimal service disruption and enhances carrier selection with online learning and runtime decision fault prevention. It is readily deployable on smartphones without infrastructure/hardware modifications. We implement iCellular on commodity phones and harness the efforts of Project Fi to assess multi-carrier access over two U.S. carriers: T-Mobile and Sprint. Our evaluation shows that, iCellular boosts the devices’ throughput with up to <inline-formula> <tex-math notation="LaTeX">$3.74\times $ </tex-math></inline-formula> throughput improvement, <inline-formula> <tex-math notation="LaTeX">$6.9\times $ </tex-math></inline-formula> suspension reduction, and <inline-formula> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> latency decrement over the state of the art, with moderate CPU, and memory and energy overheads.
Sum Rate Maximization With Shortened Cyclic Prefix in a MIMO-OFDM System

This correspondence paper addresses linear precoding design for sum rate maximization in low-latency orthogonal frequency-division multiplexing systems. In order to mitigate the overhead of cyclic prefix (CP) originating from shortened symbol duration for low-latency communications, 5G wireless systems need to adopt short CP lengths. As channel delay spread must be less than the CP length, we first derive the effective root-mean-square (RMS) delay spread and the achievable rate using the zero-forcing assumption. We construct a sum rate optimization problem for each user subject to delay spread constraints and then convert the problem into a solvable convex problem along with a semi-definite relaxation technique. The precoding matrix is finally obtained by solving optimization problems for all users. Numerical results reveal that the proposed scheme attains superior performance to the conventional sum rate optimization, as well as small RMS delay spread.
Simultaneous Wireless Information and Power Transfer at 5G New Frequencies: Channel Measurement and Network Design

Simultaneous wireless information and power transfer (SWIPT) technique offers a potential solution to ease the contradiction between high data rate and long standby time in the fifth generation (5G) mobile communication systems. In this paper, we focus on the SWIPT network design and optimization with 5G new frequencies. To design an efficient SWIPT network, we first investigate the propagation properties of 5G low-frequency (LF) and high-frequency (HF) channels. Specifically, a measurement campaign focusing on 3.5 GHz and 28 GHz is conducted in both outdoor and outdoor-to-indoor scenarios. Motivated by the measurement results, we design a dual-band SWIPT network, where the HF band is used for short-distance information delivery, while the LF band is used for short-distance energy transfer and long-distance information delivery. The designed network has a win-win architecture which can enhance the throughput of cell-edge users and improve the energy-harvesting efficiency of cell-center users. To further boost the network performance, we devise a joint power-and-channel allocation algorithm, which has the advantages of low complexity and fast convergence. Finally, simulation results demonstrate that the designed dual-band network outperforms the conventional single-band network in terms of energy-harvesting efficiency and user fairness, and the proposed algorithm can further upgrade the network performance significantly.
Security under Imperfect channel Knowledge in Wireless Networks

Title of dissertation: SECURITY UNDER IMPERFECT CHANNEL KNOWLEDGE IN WIRELESS NETWORKS Pritam Mukherjee, Doctor of Philosophy, 2016 Dissertation directed by: Professor Şennur Ulukuş Department of Electrical and Computer Engineering This dissertation studies physical layer security in wireless networks using an information theoretic framework. The central theme of this work is exploring the effect of delayed or no channel state information (CSI) on physical layer security in various wireless channel models. We begin with the fast Rayleigh fading wiretap channel, over which a legitimate transmitter wishes to have secure communication with a legitimate receiver in the presence of an eavesdropper. Subject to an average power constraint on the input, and with no CSI at any user, we show that the input distribution that achieves the secrecy capacity for this wiretap channel is discrete with a finite number of mass points. This enables us to evaluate the exact secrecy capacity of this channel numerically. Next, we consider multi-user models, specifically, the wiretap channel with M helpers, the K-user multiple access wiretap channel, and the K-user interference channel with an external eavesdropper, when no eavesdropper’s CSI is available
Image Captioning via Semantic Guidance Attention and Consensus Selection Strategy

Recently, a series of attempts have incorporated spatial attention mechanisms into the task of image captioning, which achieves a remarkable improvement in the quality of generative captions. However, the traditional spatial attention mechanism adopts latent and delayed semantic representations to decide which area should be paid more attention to, resulting in inaccurate semantic guidance and the introduction of redundant information. In order to optimize the spatial attention mechanism, we propose the Semantic Guidance Attention (SGA) mechanism in this article. Specifically, SGA utilizes semantic word representations to provide an intuitive semantic guidance that focuses accurately on semantic-related regions. Moreover, we reduce the difficulty of generating fluent sentences by updating the attention information in time. At the same time, the beam search algorithm is widely used to predict words during sequence generation. This algorithm generates a sentence according to the probabilities of words, so it is easy to push out a generic sentence and discard some distinctive captions. In order to overcome this limitation, we design the Consensus Selection (CS) strategy to choose the most descriptive and informative caption, which is selected by the semantic similarity of captions instead of the probabilities of words. The consensus caption is determined by selecting the one with the highest cumulative semantic similarity with respect to the reference captions. Our proposed model (SGA-CS) is validated on Flickr30k and MSCOCO, which shows that SGA-CS outperforms state-of-the-art approaches. To our best knowledge, SGA-CS is the first attempt to jointly produce semantic attention guidance and select descriptive captions for image captioning tasks, achieving one of the best performance ratings among any cross-entropy training methods.