Correctly understanding microarchitectural bottlenecks is important to optimize performance and energy of OoO (Out-of-Order) processors. Although CPI (Cycles Per Instruction) stack has been utilized for this purpose, it stacks architectural events heuristically by counting how many times the events occur, and the order of stacking affects the result, which may be misleading. It is because CPI stack does not consider the execution path of dynamic instructions. Critical path analysis (CPA) is a well-known method to identify the critical execution path of dynamic instruction execution on OoO processors. The critical path consists of the sequence of events that determines the execution time of a program on a certain processor. We develop a novel representation of CPCI stack (Cycles Per Critical Instruction stack), which is CPI stack based on CPA. The main challenge in constructing CPCI stack is how to analyze a large number of paths because CPA often results in numerous critical paths. In this paper, we show that there are more than ten to the tenth power critical paths in the execution of only one thousand instructions in 35 benchmarks out of 48 from SPEC CPU2006. Then, we propose a statistical method to analyze all the critical paths and show a case study using the benchmarks.

We have fabricated multiple-stacked Si quantum dots (QDs) with and without Ge core embedded in a SiO2 network on n-Si(100) and studied their field electron emission characteristics under DC bias application. For the case of pure Si-QD stacks with different dot-stack numbers, the average electric field in dot-stacked structures at which electron emission current appeared reached minimum value at a stack number of 11. This can be attributed to optimization of the electron emission due to enhanced electric field concentration in the upper layers of the dot-stacked structures and reduction of the electron injection current from the n-Si substrate, with an increased stack number. We also found that, by introducing Ge core into Si-QDs, the average electric field for the electron emission can be reduced below that from pure Si-QDs-stacked structures. This result implies that the electric field is more concentrated in the upper Si-QDs with Ge core layers due to deep potential well for holes in the Ge core.

We fabricate and characterize a GaAsSb/InGaAs backward diode (BWD) toward a realization of high sensitivity zero bias microwave rectification for RF wave energy harvest. Lattice-matched p-GaAsSb/n-InGaAs BWDs were fabricated and their current-voltage (I-V) characteristics and S-parameters up to 67 GHz were measured with respect to several sorts of mesa diameters in μm order. Our theoretical model and analysis are well fitted to the measured I-Vs on the basis of WKB approximation of the transmittance. It is confirmed that the interband tunneling due to the heterojunction is a dominant transport mechanism to exhibit the nonlinear I-V around zero bias regime unlike recombination or diffusion current components on p-n junction contribute in large current regime. An equivalent circuit model of the BWD is clarified by confirming theoretical fitting for frequency dependent admittance up to 67 GHz. From the circuit model, eliminating the parasitic inductance component, the frequency dependence of voltage sensitivity of the BWD rectifier is derived with respect to several size of mesa diameter. It quantitatively suggests an effectiveness of mesa size reduction to enhance the intrinsic matched voltage sensitivity with increasing junction resistance and keeping the magnitude of I-V curvature coefficient.

In this study, under the assumption that a robot (1) has a remotely controllable yawing camera and (2) moves in a uniform linear motion, we propose and investigate how to improve the target recognition rate with the camera, by using wireless feedback loop control. We derive the allowable data rate theoretically, and, from the viewpoint of error and delay control, we propose and evaluate QoS-Hybrid ARQ schemes under data rate constraints. Specifically, the theoretical analyses derive the maximum data rate for sensing and control based on the channel capacity is derived with the Shannon-Hartley theorem and the path-loss channel model inside the human body, i.e. CM2 in IEEE 802.15.6 standard. Then, the adaptive error and delay control schemes, i.e. QoS-HARQ, are proposed considering the two constraints: the maximum data rate and the velocity of the camera's movement. For the performance evaluations, with the 3D robot simulator GAZEBO, we evaluated our proposed schemes in the two scenarios: the static environment and the dynamic environment. The results yield insights into how to improve the recognition rate considerably in each situation.

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X2, and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study.

The IEEE 802.11p Enhanced Distributed Channel Access (EDCA) is a standardization for vehicle-to-vehicle and road-to-vehicle communications. The saturated throughputs of the IEEE 802.11p EDCA obtained from previous analytical expressions differ from those of simulations. The purpose of this paper is to explain the reason why the differences appear in the previous analytical model of the EDCA. It is clarified that there is a special state wherein the Backoff Timer (BT) is decremented in the first time slot of after a frame transmission, which cannot be expressed in the previous Markov model. In addition, this paper proposes modified Markov models, which allow the IEEE 802.11p EDCA to be correctly analyzed. The proposed models describe BT-decrement procedure in the first time slot accurately by adding new states to the previous model. As a result, the proposed models provide accurate transmission probabilities of network nodes. The validity of the proposed models is confirmed by the quantitative agreements between analytical predictions and simulation results.

Ethernet has become an indispensable technology for communications, and has come into use for many applications. At the IEEE, high-speed standardization has been discussed and has seen the adoption of new technologies such as multi-level modulation formats, high baud rate modulation and dense wave length division multiplexing. The MSA transceiver form factor has also been discussed following IEEE standardization. Optical devices such as TOSA and ROSA have been required to become more compact and higher-speed, because each transceiver form factor has to be miniaturized for high-density construction. We introduce the technologies for realizing 100GbE and those applicable to 400GbE. We also discuss future packages for optical devices. There are many similarities between optical device packages and electrical device packages, and we predict that optical device packages will follow the trends seen in electrical devices. But there are also differences between optical and electrical devices. It is necessary to utilize new technology for specific optical issues to employ advanced electrical packaging and catch up the trends.

In this paper, a parallel flow monitoring technique that achieves accurate measurement of end-to-end delay of networks is proposed. In network monitoring tasks, network researchers and practitioners usually monitor multiple probe flows to measure delays on multiple paths in parallel. However, when they measure an end-to-end delay on a path, information of flows except for the flow along the path is not utilized in the conventional method. Generally, paths of flows share common parts in parallel monitoring. In the proposed method, information of flows on paths that share common parts, utilizes to measure delay on a path by partially converting the observation results of a flow to those of another flow. We perform simulations to confirm that the observation results of 72 parallel flows of active measurement are appropriately converted between each other. When the 99th-percentile of the end-to-end delay for each flow are measured, the accuracy of the proposed method is doubled compared with the conventional method.

We developed a high-frequency and integrated design based on a flip-chip interconnection technique (Hi-FIT) as a wire-free interconnection technique that provides a high modulation bandwidth. The Hi-FIT can be applied to various high-speed (>100 Gbaud) optical devices. The Hi-FIT EA-DFB laser module has a 3-dB bandwidth of 59 GHz. And with a 4-intensity-level pulse amplitude modulation (PAM) operation at 107 Gbaud, we obtained a bit-error rate (BER) of less than 3.8×10-3, which is an error-free condition, by using a 7%-overhead (OH) hard-decision forward error correction (HD-FEC) code, even after a 10-km SMF transmission. The 3-dB bandwidth of the Hi-FIT employing an InP-MZM sub-assembly was more than 67 GHz, which was the limit of our measuring instrument. We also demonstrated a 120-Gbaud rate IQ modulation.

DHT routing algorithms can provide efficient mechanisms for resource placement and lookup for distributed file sharing systems. However, we must still deal with irregular and frequent join/leave of nodes and the problem of load unbalancing between nodes in DHT-based file sharing systems. This paper presents an efficient file backup scheme based on dynamic DHT key space clustering in order to guarantee data availability and support load balancing. The main idea of our method is to dynamically divide the DHT network into a number of clusters, each of which locally stores and maintains data chunks of data files to guarantee the data availability of user data files even when node churn occurs. Further, high-capacity nodes in clusters are selected as backup nodes to achieve adequate load balancing. Simulation results demonstrate the superior effectiveness of the proposed scheme over other file replication schemes.

By taking advantages of deep learning and reinforcement learning, ADNet (Action Decision Network) outperforms other approaches. However, its speed and performance are still limited by factors such as unreliable confidence score estimation and redundant historical actions. To address the above limitations, a faster and more accurate approach named Faster-ADNet is proposed in this paper. By optimizing the tracking process via a status re-identification network, the proposed approach is more efficient and 6 times faster than ADNet. At the same time, the accuracy and stability are enhanced by historical actions removal. Experiments demonstrate the advantages of Faster-ADNet.

Price-based power control problem is investigated in the spectrum sharing cognitive radio networks (CRNs) by Stackelberg game. Using backward induction, the revenue function of the primary user (PU) is expressed as a non-convex function of the transmit power of the secondary users (SUs). To solve the non-convex problem of the PU, a branch and bound based price-based power control algorithm is proposed. The proposed algorithm can be used to provide performance benchmarks for any other low complexity sub-optimal price-based power control algorithms based on Stackelberg game in CRNs.

Instance features based deep learning methods prompt the performances of high speed object tracking systems by directly comparing target with its template during training and tracking. However, from the perspective of human vision system, prior knowledge of target also plays key role during the process of tracking. To integrate both semantic knowledge and instance features, we propose a convolutional network based object tracking framework to simultaneously output bounding boxes based on different prior knowledge as well as confidences of corresponding Assumptions. Experimental results show that our proposed approach retains both higher accuracy and efficiency than other leading methods on tracking tasks covering most daily objects.

Many users are exposed to threats of drive-by download attacks through the Web. Attackers compromise vulnerable websites discovered by search engines and redirect clients to malicious websites created with exploit kits. Security researchers and vendors have tried to prevent the attacks by detecting malicious data, i.e., malicious URLs, web content, and redirections. However, attackers conceal parts of malicious data with evasion techniques to circumvent detection systems. In this paper, we propose a system for detecting malicious websites without collecting all malicious data. Even if we cannot observe parts of malicious data, we can always observe compromised websites. Since vulnerable websites are discovered by search engines, compromised websites have similar traits. Therefore, we built a classifier by leveraging not only malicious but also compromised websites. More precisely, we convert all websites observed at the time of access into a redirection graph and classify it by integrating similarities between its subgraphs and redirection subgraphs shared across malicious, benign, and compromised websites. As a result of evaluating our system with crawling data of 455,860 websites, we found that the system achieved a 91.7% true positive rate for malicious websites containing exploit URLs at a low false positive rate of 0.1%. Moreover, it detected 143 more evasive malicious websites than the conventional content-based system.

This letter proposes a comprehensive assessment of the mission-level damage caused by cyberattacks on an entire defense mission system. We experimentally prove that our method produces swift and accurate assessment results and that it can be applied to actual defense applications. This study contributes to the enhancement of cyber damage assessment with a faster and more accurate method.

In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.

The fifth generation mobile communications (5G) systems will need to support the ultra-reliable and low-latency communications (URLLC) to enable future mission-critical applications, e.g., self-driving cars and remote control. With the aim of verifying the feasibility of URLLC related 5G requirements in real environments, field trials of URLLC using a new frame structure are conducted in Yokohama, Japan. In this paper, we present the trial results and investigate the impact of the new frame structure and retransmission method on the URLLC performance. To reduce the user-plane latency and improve the packet success probability, a wider subcarrier spacing, self-contained frame structure, and acknowledgement/negative acknowledgement-less (ACK/NACK-less) retransmission are adopted. We verify the feasibility of URLLC in actual field tests using our prototype test-bed while implementing these techniques. The results show that for the packet size of 32 bytes the URLLC related requirements defined by the 3GPP are satisfied even at low signal-to-noise ratios or at non-line-of-sight transmission.

In this study, we propose a statistical reputation approach for constructing a reliable packet route in ad-hoc sensor networks. The proposed method uses reputation as a measurement for router node selection through which a reliable data route is constructed for packet delivery. To refine the reputation, a transaction density is defined here to showcase the influence of node transaction frequency over the reputation. And to balance the energy consumption and avoid choosing repetitively the same node with high reputation, node remaining energy is also considered as a reputation factor in the selection process. Further, a shortest-path-tree routing protocol is designed so that data packets can reach the base station through the minimum intermediate nodes. Simulation tests illustrate the improvements in the packet delivery ratio and the energy utilization.

We propose a new method of differential fault attack, which is based on the nibble-group differential diffusion property of the lightweight block cipher MIBS. On the basis of the statistical regularity of differential distribution of the S-box, we establish a statistical model and then analyze the relationship between the number of faults injections, the probability of attack success, and key recovering bits. Theoretically, time complexity of recovering the main key reduces to 22 when injecting 3 groups of faults (12 nibbles in total) in 30,31 and 32 rounds, which is the optimal condition. Furthermore, we calculate the expectation of the number of fault injection groups needed to recover 62 bits in main key, which is 3.87. Finally, experimental data verifies the correctness of the theoretical model.

Vehicular ad hoc network (VANET) is an emerging technology for the future intelligent transportation systems (ITS). How to design an efficient routing protocol for VANET is a challenging task due to the high mobility and uneven distribution of vehicles in urban areas. This paper proposes a backbone-based approach to providing the optimal inner-street relaying strategy. The virtual backbone is created distributively in each road segment based on the newly introduced stability index, which considers the link stability between vehicles and the mobility of vehicles. We also deploy the roadside unit (RSU) at intersections to determine the next path for forwarding data. The RSU gathers a global view of backbone vehicles on each road connected to the junction and analyzes the performance of the backbone as a basis of routing path selection. Simulation results show that the proposed protocol outperforms the conventional protocols in terms of packet delivery ratio and end-to-end delay.