The conventional universal filtered-DFT-spread-OFDM (UF-DFTs-OFDM) can drastically improve the out-of-band emission (OOBE) caused by the discontinuity between symbols in the conventional cyclic prefix-based DFTs-OFDM (CP-DFTs-OFDM). However, the UF-DFTs-OFDM degrades the communication quality in a long-delay multipath fading environment due to the frequency-domain ripple derived from the long transition time of the low pass filter (LPF) corresponding to the guard interval (GI). In this paper, we propose an enhanced UF-DFTs-OFDM (eUF-DFTs-OFDM) that achieves significantly low OOBE and high communication quality even in a long-delay multipath fading environment. The eUF-DFTs-OFDM applies an LPF with quite short length in combination with the zero padding (ZP) or the CP process. Then, the characteristics of the OOBE, peak-to-average power ratio (PAPR), and block error rate (BLER) are evaluated by computer simulation with the LTE uplink parameters. The result confirms that the eUF-DFTs-OFDM can improve the OOBE by 22.5dB at the channel-edge compared to the CP-DFTs-OFDM, and also improve the ES/N0 to achieve BLER =10-3 by about 2.5dB for QPSK and 16QAM compared to the UF-DFTs-OFDM. For 64QAM, the proposed eUF-DFTs-ODFDM can eliminate the error floor of the UF-DFTs-OFDM. These results indicate that the proposed eUF-DFTs-OFDM can significantly reduce the OOBE while maintaining the same level of communication quality as the CP-DFTs-OFDM even in long-delay multipath environment.

Due to the superior performance, deep learning has been widely applied to various applications, including image classification, bioinformatics, and cybersecurity. Nevertheless, the research investigations on deep learning in the adversarial environment are still on their preliminary stage. The emerging adversarial learning methods, e.g., generative adversarial networks, have introduced two vital questions: to what degree the security of deep learning with the presence of adversarial examples is; how to evaluate the performance of deep learning models in adversarial environment, thus, to raise security advice such that the selected application system based on deep learning is resistant to adversarial examples. To see the answers, we leverage image classification as an example application scenario to propose a framework of Evaluating Deep Learning for Image Classification (EDLIC) to conduct comprehensively quantitative analysis. Moreover, we introduce a set of evaluating metrics to measure the performance of different attacking and defensive techniques. After that, we conduct extensive experiments towards the performance of deep learning for image classification under different adversarial environments to validate the scalability of EDLIC. Finally, we give some advice about the selection of deep learning models for image classification based on these comparative results.

To increase the number of wireless devices such as mobile or IoT terminals, cryptosystems are essential for secure communications. In this regard, random number generation is crucial because the appropriate function of cryptosystems relies on it to work properly. This paper proposes a true random number generator (TRNG) method capable of working in wireless communication systems. By embedding a TRNG in such systems, no additional analog circuits are required and working conditions can be limited as long as wireless communication systems are functioning properly, making TRNG method cost-effective. We also present some theoretical background and considerations. We next conduct experimental verification, which strongly supports the viability of the proposed method.

In the coming smart-home era, more and more household electrical appliances are generating more and more sensor data and transmitting them over the home networks, which are often connected to Internet through Point-to-Point Protocol over Ethernet (PPPoE) for desirable authentication and accounting. However, according to our knowledge, high-speed commercial home PPPoE router is still absent for a home network environment. In this paper, we first introduce and evaluate our programmable platform FLARE-DPDK for ease of programming network functions. Then we introduce our effort to build a compact 10Gbps software FLARE PPPoE router on a commercial mini-PC. In our implementation, the control plane is implemented with Linux PPPoE software for authentication-like signaling control. The data plane is implemented over FLARE-DPDK platform, where we get packets from physical network interfaces directly bypassing Linux kernel and distribute packets to multiple CPU cores for data processing in parallel. We verify our software PPPoE router in both lab and production network environment. The experimental results show that our FLARE software PPPoE router can achieve much higher throughput than a commercial PPPoE router tested in a production environment.

In this paper, hierarchical interference coordination is proposed that suppresses both intra- and inter-cluster interference (ICI) in clustered wireless networks. Assuming transmitters and receivers are equipped with multiple antennas and complete channel state information is shared among all transmitters within the same cluster, interference alignment (IA) is performed that uses nulls to suppress intra-cluster interference. For ICI mitigation, we propose a null-steering precoder designed on the nullspace of a principal eigenvector of the correlated ICI channels, which eliminates a significant amount of ICI power given the exchange of cluster geometry between neighboring clusters. However, as ICI is negligible for the system in which the distance between clusters are large enough, the proposed scheme may not improve the system performance compared with the pure IA scheme that exploits all spatial degrees of freedom (DoF) to increase multiplexing gain without ICI mitigation. For the efficient interference management between intra- and inter-cluster, we analyze the decision criterion that provides an adaptive transmission mode selection between pure IA and proposed ICI reduction in given network environments. Moreover, a low computational complexity based transmission mode switching algorithm is proposed for irregularly distributed networks.

Cross sections that cause single event upsets by heavy ions are sensitive to doping concentration in the source and drain regions, and the structure of the raised source and drain regions especially in FDSOI. Due to the parasitic bipolar effect (PBE), radiation-hardened flip flops with stacked transistors in FDSOI tend to have soft errors, which is consistent with measurement results by heavy-ion irradiation. Device-simulation results in this study show that the cross section is proportional to the silicon thickness of the raised layer and inversely proportional to the doping concentration in the drain. Increasing the doping concentration in the source and drain region enhance the Auger recombination of carriers there and suppresses the parasitic bipolar effect. PBE is also suppressed by decreasing the silicon thickness of the raised layer. Cgg-Vgs and Ids-Vgs characteristics change smaller than soft error tolerance change. Soft error tolerance can be effectively optimized by using these two determinants with only a small impact on transistor characteristics.

For the RF power amplifier, its exposed input and output are susceptible to damage from Electrostatic (ESD) damage. The bi-direction protection is required at the input in push-pull operating mode. In this paper, considering the process compatibility to the power amplifier, cascaded Grounded-gate NMOS (ggNMOS) and Polysilicon diodes (PDIO) are stacked together to form an ESD clamp with forward and reverse protection. Through Transmission line pulse (TLP) and CV measurements, the clamp is demonstrated as latch-up immune and low parasitic capacitance bi-direction ESD protection, with 18.67/17.34V holding voltage (Vhold), 4.6/3.2kV ESD protection voltage (VESD), 0.401/0.415pF parasitic capacitance (CESD) on forward and reverse direction, respectively.

End-to-end delay, aiming to realize how much time it will take for a traffic load generated by a Mobile Node (MN) to reach Sink Node (SN), is a principal objective of most new trends in a Wireless Sensor Network (WSN). It has a direct link towards understanding the minimum time delay expected where the packet sent by MN can take to be received by SN. Most importantly, knowing the average minimum transmission time limit is a crucial piece of information in determining the future output of the network and the kind of technologies implemented. In this paper, we take network load and transmission delay issues into account in estimating the Average Minimum Time Limit (AMTL) needed for a health operating cognitive WSN. To further estimate the AMTL based on network load, an end-to-end delay analysis mechanism is presented and considers the total delay (service, queue, ACK, and MAC). This work is proposed to answer the AMTL needed before implementing any cognitive based WSN algorithms. Various time intervals and cogitative channel usage with different application payload are used for the result analysis. Through extensive simulations, our mechanism is able to identify the average time intervals needed depending on the load and MN broadcast interval in any cognitive WSN.

Currently, mobile terminals face serious security threats. A Trusted Execution Environment (TEE) which can provide an isolated execution environment for sensitive workloads, is seen as a trusted relay for providing security services for any mobile application. However, mobile TEE's architecture design and implementation strategy are not unbreakable at present. The existing researches lack of detect mechanisms for attack behaviour and malicious software. This paper proposes a Malicious code Detection scheme for Trusted Execution Environment based on Homomorphic Encryption (HE-TEEMD), which is a novel detection mechanism for data and code in the trusted execution environment. HE-TEEMD uses the Paillier additive homomorphic algorithm to implement the signature matching and transmits the ciphertext information generated in the TEE to the normal world for detection by the homomorphism and randomness of the homomorphic encryption ciphertext. An experiment and security analysis proves that our scheme can achieve malicious code detection in the secure world with minimal cost. Furthermore, evaluation parameters are introduced to address the known plaintext attack problem of privileged users.

The modern radar signals are in a wide frequency space. The receiving bandwidth of the radar reconnaissance receiver should be wide enough to intercept the modern radar signals. The Nyquist folding receiver (NYFR) is a novel wideband receiving architecture and it has a high intercept probability. Chirp signals are widely used in modern radar system. Because of the wideband receiving ability, the NYFR will receive the concurrent multiple chirp signals. In this letter, we propose a novel parameter estimation algorithm for the multiple chirp signals intercepted by single channel NYFR. Compared with the composite NYFR, the proposed method can save receiving resources. In addition, the proposed approach can estimate the parameters of the chirp signals even the NYFR outputs are under frequency aliasing circumstance. Simulation results show the efficacy of the proposed method.

In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. This paper investigates the joint optimization of user association and ICIC in the downlink. We first formulate the joint optimization problem as a utility maximization problem. We then employ the logarithmic utility function known as the proportional fair criteria. The optimum user association and the ICIC are derived by solving a convex optimization problem based on the average spectral efficiencies of all users. We propose an iterative algorithm to obtain the optimum solution to this problem. We evaluate the performance of the proposed algorithm for the small cell deployments and shows that the proposed algorithm works well. We also compare the performance of the proposed algorithm based on utility maximization user association with the CRE, and show the superiority of the utility maximization. Furthermore, we show that intra-tier ICIC and inter-tier ICIC can effectively improve the throughput performance according to the conditions. It is also shown that the combined usage of inter-tier ICIC and intra-tier ICIC enhances the throughput performance compared to schemes employing either the inter- or intra-tier ICIC scheme.

Distributing codes to specific target sensors in order to fix bugs and/or install a new application is an important management task in WSNs (Wireless Sensor Networks). For the energy efficient dissemination of such codes to specific target sensors, it is required to select the minimum required number of forwarders with the fewest control messages. In this paper, we propose a novel RPL (Routing Protocol for Low-power and lossy networks)-based tree construction scheme for target-specific code dissemination, which is called R-TCS. The main idea of R-TCS is that by leveraging the data collection tree created by a standard routing protocol RPL, it is possible to construct the code dissemination tree with the minimum numbers of non-target sensors and control messages. Since by creating a data collection tree each sensor exchanges RPL messages with the root of the tree, every sensor knows which sensors compose its upwards route, i.e. the route towards the root, and downwards route, i.e. the route towards the leaves. Because of these properties, a target sensor can select the upward route that contains the minimum number of non-target sensors. In addition, a sensor whose downward routes do not contain a target sensor is not required to transmit redundant control messages which are related to the code dissemination operation. In this way, R-TCS can reduce the energy consumption which typically happens in other target-specific code dissemination schemes by the transmission of control messages. In fact, various performance evaluation results obtained by means of computer simulations show that R-TCS reduces by at least 50% energy consumption as compared to the other previous known target-specific code dissemination scheme under the condition where ratio of target sensors is 10% of all sensors.

This paper presents a two-dimensional (2D) DOA algorithm for double L-shaped arrays. The algorithm is applied to the underwater environment for eliminating the performance error caused by the sound speed uncertainty factor. By introducing the third dimensional array, the algorithm eliminates the sound velocity variable in the depression angle expression, so that the DOA estimation no longer considering the true value of unknown sound velocity. In order to determine the parameters of a three-dimensional array, a parameter matching method with the double L-shaped array is also proposed. Simulations show that the proposed algorithm outperforms the conventional 2D-DOA estimation algorithm in unknown sound velocity environment.

Attention mechanism, which selectively focuses on source-side information to learn a context vector for generating target words, has been shown to be an effective method for neural machine translation (NMT). In fact, generating target words depends on not only the source-side information but also the target-side information. Although the vanilla NMT can acquire target-side information implicitly by recurrent neural networks (RNN), RNN cannot adequately capture the global relationship between target-side words. To solve this problem, this paper proposes a novel target-attention approach to capture this information, thus enhancing target word predictions in NMT. Specifically, we propose three variants of target-attention model to directly obtain the global relationship among target words: 1) a forward target-attention model that uses a target attention mechanism to incorporate previous historical target words into the prediction of the current target word; 2) a reverse target-attention model that adopts a reverse RNN model to obtain the entire reverse target words information, and then to combine with source context information to generate target sequence; 3) a bidirectional target-attention model that combines the forward target-attention model and reverse target-attention model together, which can make full use of target words to further improve the performance of NMT. Our methods can be integrated into both RNN based NMT and self-attention based NMT, and help NMT get global target-side information to improve translation performance. Experiments on the NIST Chinese-to-English and the WMT English-to-German translation tasks show that the proposed models achieve significant improvements over state-of-the-art baselines.

A convolutional neural network (CNN) for broadband direction of arrival (DOA) estimation of far-field electromagnetic signals is presented. The proposed algorithm performs a nonlinear inverse mapping from received signal to angle of arrival. The signal model used for algorithm is based on the circular antenna array geometry, and the phase component extracted from the spatial covariance matrix is used as the input of the CNN network. A CNN model including three convolutional layers is then established to approximate the nonlinear mapping. The performance of the CNN model is evaluated in a noisy environment for various values of signal-to-noise ratio (SNR). The results demonstrate that the proposed CNN model with the phase component of the spatial covariance matrix as the input is able to achieve fast and accurate broadband DOA estimation and attains perfect performance at lower SNR values.

A context-aware system always needs to adapt its behaviors according to context changes; therefore, modeling context-aware requirements is a complex task. With the increasing use of mobile computing, research on methods of modeling context-aware requirements have become increasingly important, and a large number of relevant studies have been conducted. However, no comprehensive analysis of the challenges and achievements has been performed. The methodology of systematic literature review was used in this survey, in which 68 reports were selected as primary studies. The challenges and methods to confront these challenges in context-aware requirement modeling are summarized. The main contributions of this work are: (1) four challenges and nine sub-challenges are identified; (2) eight kinds of methods in three categories are identified to address these challenges; (3) the extent to which these challenges have been solved is evaluated; and (4) directions for future research are elaborated.

In this letter, an active complex filter with finite transmission zeros is proposed. In order to obtain a complex prototype ladder filter including no inductors, a new circuit transformation is proposed. This circuit is classified into the RiCR filter. It is shown that it includes no negative capacitors when it is obtained through a frequency transformation. The validity of the proposed method is confirmed through computer simulation.

In an OFDM communication system using quadrature amplitude modulation (QAM) signals, peak envelope powers (PEPs) of the transmitted signals can be well controlled by using QAM Golay complementary sequence pairs (CSPs). In this letter, by making use of a new construction, a family of new 16-QAM Golay CSPs of length N=2m (integer m≥2) with binary inputs is presented, and all the resultant pairs have the PEP upper bound 2N. However, in the existing such pairs from other references their PEP upper bounds can arrive at 3.6N when the worst case happens. In this sense, novel pairs are good candidates for OFDM applications.

A novel coarse and fine hybrid granular routing network architecture is proposed. Virtual direct links (VDLs) defined by the coarse granular routing to bridge distant node pairs, and routing via VDL mitigate the spectrum narrowing caused by optical filtering at wavelength-selective switches in ROADM (Reconfigurable Optical Add/Drop Multiplexing) nodes. The impairment mitigation yields denser channel accommodation in the frequency domain, which substantially increases fiber spectral efficiency. The proposed network simultaneously utilizes fine granular optical path level routing so that optical paths can be effectively accommodated in VDLs. The newly developed network design algorithm presented in this paper effectively implements routing and spectrum assignment to paths in addition to optimizing VDL establishment and path accommodation to VDLs. The effectiveness of the proposed architecture is demonstrated through both numerical and experimental evaluations; the number of fibers necessary in a network, and the spectrum bandwidth and hop count product are, respectively, reduced by up to 18% and increased by up to 111%.

Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.