Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-Hop Wireless Powered Communication Networks Assisted by Backscatter

In this paper, we introduce a backscatter assisted dual-hop wireless powered communication network (BADHWPCN) with a hybrid access point (HAP) and two users configured with energy-limited relays. To tackle the unfairness issue caused by the near-far problem in our proposed system, a protocol is considered that one relay backscatters the corresponding user’s data to the HAP when the other relay harvests energy from the HAP. By optimally allocating the time of backscattering, energy harvesting, and forwarding for the relays, we can not only optimize the total throughput of the system, but also guarantee the throughput fairness of the users. For this purpose, a minimum throughput maximization problem integrating backscatter mode and harvest-then-transmit (HTT) mode is formulated and then solved by an efficient algorithm. Our simulation results confirm our proposed design can indeed improve the throughput performance of the system compared with other conventional schemes.
Adaptive Activity-Aware Constellation List-Based Decision Feedback Detection for Massive Machine-Type Communications

In this work, we propose an adaptive list-based decision feedback detector along with an l0-norm regularized recursive least-squares algorithm that only requires pilot symbols (AA-CL-DF). The proposed detector employs a list strategy based on the signal constellation points to generate different candidates for detection. Simulation results show that the proposed AA-CL-DF successfully mitigates the error propagation and approaches the performance for the oracle LMMSE algorithm.
3D Sensor-Based UAV Localization for Bridge Inspection

Autonomous vehicles often benefit from the Global Positioning System (GPS) for navigational guidance as people do with their mobile phones or automobile radios. However, since GPS is not always available or reliable everywhere, autonomous vehicles need more reliable systems to understand where they are and where they should head to. Moreover, even though GPS is reliable, autonomous vehicles usually need extra sensors for more precise position estimation. In this work, we propose a localization method for autonomous Unmanned Aerial Vehicles (UAVs) for infrastructure health monitoring without relying on GPS data. The proposed method only depends on depth image frames from a 3D camera (Structure Sensor) and the 3D map of the structure. Captured 3D scenes are projected onto 2D binary images as templates, and matched with the 2D projection of relevant facade of the structure. Back-projections of matching regions are then used to calculate 3D translation (shift) as estimated position relative to the structure. Our method estimates position for each frame independently from others at a rate of 200Hz. Thus, the error does not accumulate with the traveled distance. The proposed approach provides promising results with mean Euclidean distance error of 13.4 cm and standard deviation of 8.4cm.
Ship Targets Detection with Dual Downscaling Spectral Residual Method in SAR Image

Reliable automatic ship detection in Synthetic Aperture Radar (SAR) image plays an important role in the surveillance of maritime activity. But, the coastal background has seriously affected the accuracy and speed of ship detection. Within this paper, we present a Dual Downscaling Spectral Residual (DDSR) method of sea-land segmentation-based spectral residual for ship detection that attempts to combine the downscaling thresholding operator, saliency computation and morphological features. For this, sea-land segmentation based on the downscaling thresholding operator and morphological features of the potential detection result is applied, which is followed by downscaling Spectral Residual detector so that the accurate detection result is obtained. Measured data illustrate that the proposed method obtain outstanding performance in ship targets detection.
Fuzzing IPC with Knowledge Inference

Sandboxing provides a strong security guarantee for applications, by isolating untrusted code into separated compartments. Untrusted code could only use IPC (inter-process communication) to launch sensitive actions, which are implemented in trusted (and maybe privileged) code. IPC-related security bugs in trusted code could facilitate jailbreaks of sandboxing, and thus are becoming high-value targets. However, finding vulnerabilities that could be triggered by IPC is challenging, due to the fact that IPC communication is stateful and format-sensitive. In this paper, we propose a new fuzzing solution to discover IPC bugs in IPC services without source code, by combining static analysis and dynamic analysis. We use static analysis to recognize format checks and help construct IPC messages of valid formats. We then use dynamic analysis to infer the constraints between IPC messages, and model the stateful logic with a probability matrix. Therefore, we are able to generate high-quality IPC messages to test IPC services, and discover deep and complex IPC bugs. Without loss of generality, we implemented a prototype MachFuzzer, for a specific complicated and crucial IPC service, i.e., WindowServer in macOS. This prototype helps us find 12 previously unknown vulnerabilities in WindowServer in 48 hours. Among them, three vulnerabilities are confirmed exploitable, and could be exploited to escape the sandbox and gain root privilege.
Utility-Based Precoding Optimization Framework for Large Intelligent Surfaces

The spectral efficiency of wireless networks can be made nearly infinitely large by deploying many antennas, but the deployment of very many antennas requires new topologies beyond the compact and discrete antenna arrays used by conventional base stations. In this paper, we consider the large intelligent surface scenario where small antennas are deployed on a large and dense two-dimensional grid. Building on the heritage of MIMO, we first analyze the beamwidth and sidelobes when transmitting from large intelligent surfaces. We compare different precoding schemes and determine how to optimize the transmit power with respect to different utility functions.
Methods for Training Neural Networks with Zero False Positives for Malware Detection

With the increase in malware samples in the last decade more antivirus products started to use machine learning algorithms in order to cope with the large volume of data. Thanks to the good results and advances in learning infrastructure the neural networks have become one of the preferred way of addressing this. However, these algorithms need to be fine tuned in order to not add an overhead of costly false positives. This paper presents a study that takes a closer look into two techniques used for false positive mitigation issue: one side training and weight class adjustment. The techniques are used to train a neural network with zero false positives and are compared in order to find out which one give the highest true positive rate. Using a large dataset constructed over several years we show that by using these techniques a 90% true positive rate can be obtained while training for 0 false positives.
Multiple-Criteria Policy Anaysis of Circular Debt in Pakistan

With the increase in electricity usage through developing countries, the financial crises has enveloped due to energy poverty. Issues such as circular debt are now a major factor that has a drastic impact on the economic condition of such countries. Power sector, being a major constituent of the economic structure of such countries, has a major role to play for changes in economic condition. This paper numerically evaluates the financial challenges in power sector and the inevitable shortcomings in policies with growing circular debt. The case study has been performed on Pakistan and solutions are proposed to mitigate this financial debt with comparative analysis of increasing share of renewables and inexpensive energy in electricity market.
Joint Scheduling and Power Allocation for 6G Terahertz Mesh Networks

Self-backhauling is a promising solution for ultra-dense networks. Since the Teraherz (THz) band can provide a very broad bandwidth, THz-based self-backhauling is an attractive technology for 6G ultra-dense networks to achieve “near-wired” transmissions and reduce cost. Based on THz, small base stations form a wireless mesh backhaul network, and a small base station can communicate with multiple neighbors concurrently. However, in a mesh network, time-slot must be carefully scheduled among directional links to fully exploit concurrent transmission opportunities while avoid cross-link interference and conflictions. Besides, since the THz band has frequency-selective and distance-selective properties, when allocating sub-bands to links with different transmission distance, the Long-User-Central-Window (LUCW) principle must be followed. Moreover, transmit power needs to be proper allocated to avoid interference. Therefore, the resource allocation problem in 6G THz backhaul networks is complicated. In this paper, a joint time-slot scheduling, sub-band scheduling, and power allocation (JTSP) scheme is proposed to solve the problem. This problem is formulated as a mixed integer nonlinear program (MINLP) problem, which is NP-Complete. To reduce the computational complexity, a greedy shrinking algorithm (GSA) is designed in JTSP to obtain a suboptimal solution. The effectiveness of JTSP is validated by simulations. Results illustrate that compared with traditional methods and algorithms, the proposed scheme can achieve 12.5%-60.7% throughput gain.
Wingsuit Flying Search—A Novel Global Optimization Algorithm

In this paper, a novel global optimization algorithm – Wingsuit Flying Search (WFS) is introduced. It is inspired by the popular extreme sport – wingsuit flying. The algorithm mimics the intention of a flier to land at the lowest possible point of the Earth surface within their range, i.e., a global minimum of the search space. This is achieved by probing the search space at each iteration with a carefully picked population of points. Iterative update of the population corresponds to the flier progressively getting a sharper image of the surface, thus shifting the focus to lower regions. The algorithm is described in detail, including the mathematical background and the pseudocode. It is validated using a variety of classical and CEC 2020 benchmark functions under a number of search space dimensionalities. The validation includes the comparison of WFS to several nature-inspired popular metaheuristic algorithms, including the winners of CEC 2017 competition. The numerical results indicate that WFS algorithm provides considerable performance improvements (mean solution values, standard deviation of solution values, runtime and convergence rate) with respect to other methods. The main advantages of this algorithm are that it is practically parameter-free, apart from the population size and maximal number of iterations. Moreover, it is considerably “lean” and easy to implement.
A Novel Multi-channel High-Resolution Imaging Method for MIMO-SAR System Based on Filterbank Framework Reconstruction Theory

Multi-input and multi-output SAR (MIMO-SAR) processing technique is the important development trend of the next-generation spaceborne SAR imaging system, due to the ability of achieving high resolution and large swath width (HRWS) imaging by exploiting more spatial degrees of freedom via adding multiple transmission channels. Considering that multichannel MIMO-SAR signal is nonuniformly sampled and most of the conventional reconstruction algorithms are time-consuming due to the matrix inversion or the eigenvalue decomposition, this paper proposes a fast nonuniform signal reconstruction algorithm based on filterbank framework theory, which combines the non-uniform signal reconstruction theory with a bank of filters to resolve the azimuth ambiguity by the point multiplication operation. Simulated processing results are used to validate the effectiveness of the proposed method.
Natural Language Processing Characterization of Recurring Calls in Public Security Services

Extracting knowledge from unstructured data silos, a legacy of old applications, is mandatory for improving the governance of today’s cities and fostering the creation of smart cities. Texts in natural language often compose such data. Nevertheless, the inference of useful information from a linguistic-computational analysis of natural language data is an open challenge. In this paper, we propose a clustering method to analyze textual data employing the unsupervised machine learning algorithms k-means and hierarchical clustering. We assess different vector representation methods for text, similarity metrics, and the number of clusters that best matches the data. We evaluate the methods using a real database of a public record service of security occurrences. The results show that the k-means algorithm using Euclidean distance extracts non-trivial knowledge, reaching up to 93% accuracy in a set of test samples while identifying the 12 most prevalent occurrence patterns.
A 146.7 GHz Transceiver with 5 GBaud Data Transmission using a Low-Cost Series-Fed Patch Antenna Array through Wirebonding Integration

We present a fully-packaged two-channel transmitter and a fully-packaged four-channel receiver using previously reported four-channel transceivers, designed in 45 nm CMOS SOI. We first present a low -cost antenna and packaging technologies to integrate with the transceivers. 8-element series-fed linear microstrip patch antenna arrays fabricated on an Isola Astra MT77 (${\mathcal{E}_r} = 3$, tanℽ=0.0017) printed circuit board (PCB) showed 13.6 dB gain, 9° E- plane and 65° H-plane 3-dB beam-widths, and 7 GHz bandwidth (S21), close to simulation. These antennas are connected to CMOS multi-channel transmitter and receiver ICs through wirebonding. Simulations of this interface predict 1.8 dB loss in the IC-antenna ball-bonds (20 μm diameter, 250 μm length) and 0.7 dB loss in the ~1.5 mm PCB microstrip line between the bond pads and the antenna feed point. Using one channel of these transmitter and receiver boards, we demonstrate 5 GBaud BPSK wireless data transmission over 25 cm air with 3x10-5 bit-error- rate (BER) and 19 dB signal - to-noise-ratio (SNR) at 146.7 GHz carrier frequency.
Joint Detection in Massive Overloaded Wireless Systems via Mixed-Norm Discrete Vector Decoding

We propose a novel ℓ0-norm based multi dimensional signal detection scheme for overloaded wireless systems such as Non-orthogonal Multiple Access (NOMA) and underdetermined Multiple-Input Multiple-Output (MIMO), in which the discreteness of maximum likelihood (ML) detection is transformed into a continuous ℓ0-norm constraint, subsequently convexized via fractional programming (FP). As a consequence, the proposed signal detection algorithm possesses the potential to achieve ML-like performance in terms of bit error rate (BER) by properly adjusting weighting parameters, at a fraction of the cost. Simulation comparisons with state-of-the-art (SotA) alternatives are given, which illustrate the effectiveness of the proposed method both in terms of its capability of outperforming the SotA and of possibilities of further improvement towards ML-like performance via the optimization of the weighting parameters.
Smart Antenna Allocation for Wireless Surveillance

This paper investigates the smart transmit/receive antenna allocation problem for a wireless surveillance system, in which a smart full-duplex (FD) legitimate node tries to improve its monitoring rate over a suspicious communication link via adaptively allocating part of its antennas to send jamming signals. We consider a practical surveillance scenario where the wireless channel is time-varying and the channel state information (CSI) is unavailable for the monitor. The monitor’s goal is to maximize the long-term monitoring rate by allocating its number of transmit/receive antennas in an adaptive way. However, it is challenging to obtain an optimal policy in the considered time-varying system. To that end, we formulate it as a Markov decision Process (MDP) and apply Q-Learning (QL) to learn the optimal transmit/receive antenna allocation through interactions with the environment. To further accelerate the learning process, a novelty adaptive method is also proposed, referred to as ALR-Q-Learning (ALRQL), which generates real-time learning rates according to the difference on number of symbols that the monitor decodes successfully between two adjacent time slots. Simulation results validate the efficacy of our proposed methods in terms of the robustness as well as adaptability to the changing surveillance environment.
Physical-Layer Security with Finite Blocklength over Slow Fading Channels

This paper studies physical-layer security over slow fading channels, considering the impact of finite-blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate the secrecy throughput (ST) of a legitimate user pair coexisting with an eavesdropper. Specifically, we devise both adaptive and non-adaptive optimization schemes to maximize the ST, where we derive optimal parameters including the transmission policy, blocklength, and code rates based on the instantaneous and statistical channel state information of the legitimate pair, respectively. Various important insights are provided. In particular, 1) increasing blocklength improves both reliability and secrecy with our transmission policy; 2) ST monotonically increases with blocklength; 3) ST initially increases and then decreases with secrecy rate, and there exists a critical secrecy rate that maximizes the ST. Numerical results are presented to verify theoretical findings.
Self-Organized Scheme for Unique Cell ID Assignment during Femtocell Deployment

Femtocell deployment has been a focus of wide research due to its potential to increase system capacity. There still are open challenges for the planning and management of femtocells caused by the potential interference issues that may arise when installing them under the coverage of larger cells (i.e., macrocells). This paper proposes a dynamic and self-organizing scheme based on the 3GPP standards. A centralized coordinator is deployed to enable the newly installed femtocell to avoid interference through acquiring a unique cell ID (PCI). Results show that the proposed scheme improves the management of small cell deployment and protects the control channels. By distributing PCIs uniquely, more efficient distributions and improved signal-to-noise interference ratio (SINR) have been achieved.
Design Strategies for Sparse Control Of Random Time-Varying NETWORKS

This paper considers the design of sparse controllers for driving dynamics defined over a network of agents whose connectivity changes randomly over time. This scenario is of interest when the underlying topology is affected by random link failures such as communication drops in sensor networks. To account for the randomness in the topology, it relies on the recent concept of controllability in the mean that aims driving the expected dynamic to the target state, while minimizing second-order statistical distances. We show that by departing from the conventional minimal energy solution, it is possible to recast the sparse control design problem as a semi-definite programming and use convex solvers to obtain a sparse solution. Moreover, these findings enrich the state-of-the-art design methods with design strategies that do not necessarily rely on the graph smoothness for the target state. The effectiveness of the proposed findings is corroborated with numerical results in Erdős-Réinyi and k—nearest neighbors graphs.
Online Bounded Component Analysis: A Simple Recurrent Neural Network with Local Update Rule for Unsupervised Separation of Dependent and Independent Sources

A low complexity recurrent neural network structure is proposed for unsupervised separation of both independent and dependent sources from their linear mixtures. The proposed network is generated based on Bounded Component Analysis (BCA) approach. We first propose an Online-BCA optimization setting. Then we derive the corresponding recurrent neural network (RNN) with iterative learning update expressions. The resulting 2-layer network has a fairly simple structure with feedforward synapses at the input layer, recurrent synapses at the output layer, and top-down connections from the output layer to the first layer. The synaptic weight updates of the proposed RNN are local, supporting its biological plausibility. We use correlated synthetic sources and natural images as examples to illustrate the correlated/dependent source separation capability of the proposed neural network.
Vesti: An In-Memory Computing Processor for Deep Neural Networks Acceleration

We present Vesti, a Deep Neural Network (DNN) accelerator optimized for energy-constrained hardware platforms such as mobile, wearable, and Internet of Things (IoT) devices. Vesti integrates instances of in-memory computing (IMC) SRAM macros with an ensemble of peripheral digital circuits for dataflow management. The IMC SRAM macros eliminate the data access bottleneck that hinders conventional ASIC implementations performing dot-product computation, while the peripheral circuits improve the macros’ parallelism and utilization for practical applications. Vesti supports large-scale DNNs with configurable activation precision, substantially improving chip-level energy-efficiency with favorable accuracy trade-off. The Vesti accelerator is designed and laid out in 65 nm CMOS, demonstrating ultra-low energy consumption of less than <20nJ for MNIST classification and <40µJ for CIFAR-10 classification at 1.0V supply.
A Secure and Efficient Mobile Payment Protocol with Fair-Exchange Feature

Mobile commerce helps transactions to be done via mobile devices in a wireless environment. Several mobile payment protocols have already been developed trying to provide essential security requirements of users. So far, there has been low attention to fair-exchange requirement in mobile payment protocols while it is considered as an important factor in users’ point of view. In most of mobile payment protocols, client must pay for the product before he delivers it. This is an unfair situation that is addressed in this paper by proposing a mobile payment protocol that not only provides the security requirements but also provides fairexchange feature. This protocol is based on the APSWPP protocol. In this protocol client receives committed product before paying and after paying for the product it can get the secret of committed product. Also to serve fair-exchange completely, some steps are added to main steps of this protocol. The security of our proposed protocol is validated by AVISPA tool.
Enhanced Complex Local Mean Decomposition for Micro-Doppler Separation in ISAR Imaging

In traditional inverse synthetic aperture radar (ISAR) imaging of targets with rotating parts, the rotational parts such as propellers produce micro-Doppler (m-D) effects. This phenomenon can influence the quality of the imaging result seriously, so we must search a method to separate the m-D signature from the rigid body parts. In this paper, a novel method based on enhanced complex local mean decomposition (ECLMD) is proposed. The results of signal and ISAR imaging simulation show that the proposed method is very effective.
Convex Combination of LMF and ZA-LMF for Variable Sparse System Identification

The objective of this work is to introduce a convex combination of two filters to perform a variable sparse system identification. The first filter is based on the Least Mean Fourth algorithm (LMF), whereas the second is based on its sparse aware version, i.e., the Zero-attractor-LMF (ZA-LMF) algorithm. The convex combination is proposed to solve the sparsity problem under non-Gaussian noise environments. The universality study of the filter indicates that the convex combination always chooses the component filter that offers the lowest Excess Mean Square Error (EMSE) possible. Computer Simulations are performed to confirm the theoretical findings.