With increasing technology scaling and the use of lower voltages, more research interest is being shown in variability-tolerant analog front end design. In this paper, we describe an adaptive amplitude control transmitter that is operated using differential signaling to reduce the temperature variability effect. It enables low power, low voltage operation by synergy between adaptive amplitude control and Vth temperature variation control. It is suitable for high-speed interface applications, particularly cable interfaces. By installing an aggressor circuit to estimate transmitter jitter and changing its frequency and activation rate, we were able to analyze the effects of the interface block on the input buffer and thence on the entire system. We also report a detailed estimation of the receiver clock-data recovery (CDR) operation for transmitter jitter estimation. These investigations provide suggestions for widening the eye opening of the transmitter.

In this paper, we propose a filter-and-forward relay scheme with energy harvesting for single-carrier transmission in frequency-selective channels. The relay node harvests energy from both the source node transmit signal and its own transmit signal by self-energy recycling. The signal received by the relay node is filtered to suppress the inter-symbol interference and then forwarded to the destination node using the harvested energy. We consider a filter design method based on the signal-to-interference-plus-noise power ratio maximization, subject to a constraint that limits the relay transmit power. In addition, we provide a golden-section search based algorithm to optimize the power splitting ratio of the power splitting protocol. The simulation results show that filtering and self-energy recycling of the proposed scheme are effective in improving performance. It is also shown that the proposed scheme is useful even when only partial channel state information is available.

In this paper, an analysis method for calculating balanced and unbalanced modes of a small antenna is summarized. Modal condactances which relate dissipated power of the antenna are directly obtained from standard S-parameters that we can measure by a 2-port network analyzer. We demonstrate the validity and effectiveness of the proposed method by simulation and measurement for a dipole antenna with unbalaned feed. The ratio of unbalanced-mode power to the total power (unbalanced-mode power ratio) calculated by the proposed method agrees precisely with that yielded by the conventional method using measured radiation patterns. Furthermore, we analyze a small loop antenna with unbalanced feed by the proposed method and show that the self-balancing characteristic appears when the loop is set in resonant state by loading capacitances or the whole length of the loop is less than 1/20th the wavelength.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

Linear feed-forward/feedback shift registers are used as an effective tool of testing circuits in various fields including built-in self-test and secure scan design. In this paper, we consider the issue of testing linear feed-forward/feedback shift registers themselves. To test linear feed-forward/feedback shift registers, it is necessary to generate a test sequence for each register. We first present an experimental result such that a commercial ATPG (automatic test pattern generator) cannot always generate a test sequence with high fault coverage even for 64-stage linear feed-forward/feedback shift registers. We then show that there exists a universal test sequence with 100% of fault coverage for the class of linear feed-forward/feedback shift registers so that no test generation is required, i.e., the cost of test generation is zero. We prove the existence theorem of universal test sequences for the class of linear feed-forward/feedback shift registers.

Recent studies suggest that learning “how to learn” is important because learners must be self-regulated to take more responsibility for their own learning processes, meta-cognitive control, and other generative learning thoughts and behaviors. The mechanism that enables a learner to self-regulate his/her learning strategies has been actively studied in classroom settings, but has seldom been studied in the area of real-world learning in out-of-school settings (e.g., environmental learning in nature). A feature of real-world learning is that a learner's cognition of the world is updated by his/her behavior to investigate the world, and vice versa. This paper models the mechanism of real-world learning for executing and self-regulating a learner's cognitive and behavioral strategies to self-organize his/her internal knowledge space. Furthermore, this paper proposes multimodal analytics to integrate heterogeneous data resources of the cognitive and behavioral features of real-world learning, to structure and archive the time series of strategies occurring through learner-environment interactions, and to assess how learning should be self-regulated for better understanding of the world. Our analysis showed that (1) intellectual achievements are built by self-regulating learning to chain the execution of cognitive and behavioral strategies, and (2) a clue to predict learning outcomes in the world is analyzing the quantity and frequency of strategies that a learner uses and self-regulates. Assessment based on these findings can encourage a learner to reflect and improve his/her way of learning in the world.

Recent top-performing object detectors usually depend on a two-stage approach, which benefits from its region proposal and refining practice but suffers low detection speed. By contrast, one-stage approaches have the advantage of high efficiency while sacrifice their accuracies to some extent. In this paper, we propose a novel single-shot object detection network which inherits the merits of both. Motivated by the idea of semantic enrichment to the convolutional features within a typical deep detector, we propose two novel modules: 1) by modeling the semantic interactions between channels and the long-range dependencies between spatial positions, the self-attending module generates both channel and position attention, and enhance the original convolutional features in a self-guided manner; 2) leveraging the class-discriminative localization ability of classification-trained CNN, the semantic activating module learns a semantic meaningful convolutional response which augments low-level convolutional features with strong class-specific semantic information. The so called self-attending and semantic activating network (ASAN) achieves better accuracy than two-stage methods and is able to fulfil real-time processing. Comprehensive experiments on PASCAL VOC indicates that ASAN achieves state-of-the-art detection performance with high efficiency.

A key driver of software business growth in developing countries is the survival of software small and medium-sized enterprises (SMEs). Quality of products is a critical factor that can indicate the future of the business by building customer confidence. Software development agencies need to be aware of meeting international standards in software development process. In practice, consultants and assessors are usually employed as the primary solution, which can impact the budget in case of small businesses. Self-assessment tools for software development process can potentially reduce time and cost of formal assessment for software SMEs. However, the existing support methods and tools are largely insufficient in terms of process coverage and semi-automated evaluation. This paper proposes to apply a knowledge-based approach in development of a self-assessment and gap analysis support system for the ISO/IEC 29110 standard. The approach has an advantage that insights from domain experts and the standard are captured in the knowledge base in form of decision tables that can be flexibly managed. Our knowledge base is unique in that task lists and work products defined in the standard are broken down into task and work product characteristics, respectively. Their relation provides the links between Task List and Work Product which make users more understand and influence self-assessment. A prototype support system was developed to assess the level of software development capability of the agencies based on the ISO/IEC 29110 standard. A preliminary evaluation study showed that the system can improve performance of users who are inexperienced in applying ISO/IEC 29110 standard in terms of task coverage and user's time and effort compared to the traditional self-assessment method.

Pinning control of multi-agent systems is a method that the external control input is added to some agents (pinning nodes), e.g., leaders. By the external control input, consensus to a certain target value and faster consensus are achieved. In this paper, we propose a new method of self-triggered predictive pinning control for the consensus problem. Self-triggered control is a method that both the control input and the next update time are calculated. Using self-triggered control, it is expected that the communication cost can be reduced. First, a new finite-time optimal control problem used in self-triggered control is formulated, and its solution method is derived. Next, as an on-line algorithm, two methods, i.e., the multi-hop communication-based method and the observer-based method are proposed. Finally, numerical examples are presented.

In this paper, we propose the low voltage CMOS current mode reference circuit using self-regulator with adaptive biasing technique. It drastically reduces the line sensitivity (LS) of the output voltage and the power supply voltage dependence of the temperature coefficient (TC). The self-regulator used in the proposed circuit adaptively generates the minimum voltage required the reference core circuit following the PVT (process, voltage and temperature) conditions. It makes possible to improve circuit performances instead of slightly increasing minimum power supply voltage. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, LS is reduced to 0.0065%/V under 0.8V < VDD < 3.0V. TC is 67.6ppm/°C under the condition that the temperature range is from -40°C to 125°C and VDD range is from 0.8V to 3.0V. The power supply rejection ratio (PSRR) is less than -80.4dB when VDD is higher than 0.8V and the noise frequency is 100Hz. According to the simulation results, we could confirm that the performances of the proposed circuit are improved compared with the conventional circuit.

The requirement of safety, roadway capacity and efficiency in the vehicular network, which makes vehicular platoons concept continue to be of interest. For the authentication in vehicular platoons, efficiency and cooperation are the two most important things. Cooperative authentication is a way to recognize false identities and messages as well as saving resources. However, taking part in cooperative authentication makes the vehicle more vulnerable to privacy leakage which is commonly done by location tracking. Moreover, vehicles consume their resources when cooperating with others during the process of cooperation authentication. These two significant factors cause selfish behaviors of the vehicles not to participate in cooperate cooperation actively. In this paper, an infinitely repeated game for cooperative authentication in vehicular platoons is proposed to help analyze the utility of all nodes and point out the weakness of the current collaborative authentication protocol. To deal with this weakness, we also devised an enhanced cooperative authentication protocol based on mechanisms which makes it easier for vehicles to stay in the cooperate strategy rather than tend to selfish behavior. Meanwhile, our protocol can defense insider attacks.

This paper studies the joint optimization of precoding, transmit power and data rate allocation for energy-efficient full-duplex (FD) cloud radio access networks (C-RANs). A new nonconvex problem is formulated, where the ratio of total sum rate to total power consumption is maximized, subject to the maximum transmit powers of remote radio heads and uplink users. An iterative algorithm based on successive convex programming is proposed with guaranteed convergence to the Karush-Kuhn-Tucker solutions of the formulated problem. Numerical examples confirm the effectiveness of the proposed algorithm and show that the FD C-RANs can achieve a large gain over half-duplex C-RANs in terms of energy efficiency at low self-interference power levels.

In-class species classification based on animal sounds is a highly challenging task even with the latest deep learning technique applied. The difficulty of distinguishing the species is further compounded when the number of species is large within the same class. This paper presents a novel approach for fine categorization of animal species based on their sounds by using pre-trained CNNs and a new self-attention module well-suited for acoustic signals The proposed method is shown effective as it achieves average species accuracy of 98.37% and the minimum species accuracy of 94.38%, the highest among the competing baselines, which include CNN's without self-attention and CNN's with CBAM, FAM, and CFAM but without pre-training.

In this letter, we investigate a design of efficient antenna allocation at the full duplex receiver (FDR) in a multi-input multi-output multi-eavesdropper (MIMOME) wiretap channel for physical layer security improvement. Specifically, we propose the allocation which are feasible for the practical scenario with self-interference (SI) taken into account, because the jamming signals from FDR not only confuse the eavesdropper but also inevitably cause SI at the FDR. Due to the nolinear and coupling of the antenna allocation optimization problem, we transform the original problem into an integer programming problem. Then, we derive the optimal solution and the corresponding beamforming matrices in closed-form by means of combining spatial alignment and null-space projection method. Furthermore, we present the feasibility condition and full-protection condition, which offer insight into principles that enable more efficient and effective use of FDR in the wiretap channel for security improvement. From the simulation results, we validate the theoretical analysis and demonstrate the outstanding performance of the proposed antennas allocation at FDR.

Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.

Software testing is an important problem to design a large software system and it is difficult to be solved due to its computational complexity. We try to use program nets to approach this problem. As the first step towards solving software testing problem, this paper provides a technique to generate subnets of a program net and applies this technique to software testing. Firstly, definitions and properties of program nets are introduced based on our previous works, and the explanation of software testing problem is given. Secondly, polynomial algorithms are proposed to generate subnets that can cover all the given program net. Finally, a case study is presented to show how to find subnets covering a given program net by using the proposed algorithms, as well as to show the input test data of the program net for software testing.

In full duplex (FD), which improves the system capacity (or cell throughput) and reduces the transmission delay (or latency) through simultaneous transmission and reception in the same frequency band, self-interference (SI) from the transmitter should be suppressed using antenna isolation, an analog SI canceler, and digital SI canceler (DSIC) to a level such that the data or control channel satisfies the required block error rate (BLER). This paper proposes a structure of iterative DSIC with alternating estimate subtraction (AES-IDSIC) for orthogonal frequency division multiplexing (OFDM) using FD. We first present the required SI suppression level considering SI, quantization noise of an analog-to-digital converter, and nonlinear distortion of a power amplifier and RF receiver circuit for a direct conversion transceiver using FD. Then, we propose an AES-IDSIC structure that iterates the generation of the SI estimate, the downlink symbol estimate, and then alternately removes one of the estimates from the received signal in the downlink including SI. We investigate the average BLER performance of the AES-IDSIC for OFDM using FD in a multipath fading channel based on link-level simulations under the constraint that the derived required signal-to-SI ratio must be satisfied.

Denser infrastructures can reduce terminal-to-infrastructure distance and thus improve the link budget in mobile communication systems. One such infrastructure, relaying can reduce the distance between the donor evolved node B (eNB) and user equipment (UE). However, conventional relaying suffers from geographical constraints, i.e., installation site, and difficulty in simultaneous transmission and reception on the same carrier frequency. Therefore, we propose a new type of fiber-optic relaying in which the antenna facing the eNB is geographically separated from the antenna facing the UE, and the two antennas are connected by an optical fiber. This structure aims to extend coverage to heavily shadowed areas. Our primary objective is to establish a design method for the proposed fiber-optic relaying in the presence of self-interference, which is the interference between the backhaul and access links, when the backhaul and access links simultaneously operate on the same carrier frequency. In this paper, we present the performance of the fiber-optic relaying in the presence of intra- and inter-cell interferences as well as self-interference. The theoretical desired-to-undesired-signal ratio for both uplink and downlink is investigated as parameters of the optical fiber length. We demonstrate the possibility of fiber-optic relaying with simultaneous transmission and reception on the same carrier frequency for the backhaul and access links. We validate the design method for the proposed fiber-optic relay system using these results.

This paper proposed an iterative soft interference canceller (IC) referred to as turbo equalizer for the self-coherent detection, and extrinsic information transfer (EXIT) chart based irregular low density parity check (LDPC) code optimization for the turbo equalizer in optical fiber short-reach transmissions. The self-coherent detection system is capable of linear demodulation by a single photodiode receiver. However, the self-coherent detection suffers from the interference induced by signal-signal beat components, and the suppression of the interference is a vital goal of self-coherent detection. For improving the error-free signal detection performance of the self-coherent detection, we proposed an iterative soft IC with the aid of forward error correction (FEC) decoder. Furthermore, typical FEC code is no longer appropriate for the iterative detection of the turbo equalizer. Therefore, we designed an appropriate LDPC code by using EXIT chart aided code design. The validity of the proposed turbo equalizer with the appropriate LDPC is confirmed by computer simulations.