A sub-Terahertz band is envisioned to play a great role in 6G to achieve extreme high data-rate communication. In addition to very wide band transmission, we need spatial multiplexing using a hybrid MIMO system. A recently presented paper, however, reveals that the number of observed multipath components in a sub-Terahertz band is very few in indoor environments. A channel with few multipath components is called sparse. The number of layers (streams), i.e. multiplexing gain in a MIMO system does not exceed the number of multipaths. The sparsity may restrict the spatial multiplexing gain of sub-Terahertz systems, and the poor multiplexing gain may limit the data rate of communication systems. This paper describes fundamental considerations on sub-Terahertz MIMO spatial multiplexing in indoor environments. We examined how we should steer analog beams to multipath components to achieve higher channel capacity. Furthermore, for different beam allocation schemes, we investigated eigenvalue distributions of a channel Gram matrix, power allocation to each layer, and correlations between analog beams. Through simulation results, we have revealed that the analog beams should be steered to all the multipath components to lower correlations and to achieve higher channel capacity.

This paper focuses on verification for reactive system specifications. A reactive system is an open system that continuously interacts with an uncontrollable external environment, and it must often be highly safe and reliable. However, realizability checking for a given specification is very costly, so we need effective methods to detect and analyze defects in unrealizable specifications to refine them efficiently. We introduce a systematic characterization on necessary conditions of realizability. This characterization is based on quantifications for inputs and outputs in early and late behaviors and reveals four essential aspects of realizability: exhaustivity, strategizability, preservability and stability. Additionally, the characterization derives new necessary conditions, which enable us to classify unrealizable specifications systematically and hierarchically.

Cyber-physical systems (CPSs) assisted by digital twins (DTs) integrate sensing-actuation loops over communication networks in various infrastructure services and applications. This study overviews the concept, methodology, and applications of the integrated communication quality estimation and control for the DT-assisted CPSs from both communications and control perspectives. The DT-assisted CPSs can be considered as networked control systems (NCSs) with virtual dynamic models of physical entities. A communication quality estimation observer (CQEO), which is an extended version of the communication disturbance observer (CDOB) utilized for time-delay compensation in NCSs, is proposed to estimate the integrated effects of the quality of services (QoS) and cyberattacks on the NCS applications. A path diversity technique with the CQEO is also proposed to achieve reliable NCSs. The proposed technique is applied to two kinds of NCSs: remote motor control and haptic communication systems. Moreover, results of the simulation on a haptic communication system show the effectiveness of the proposed approach. In the end, future research directions of the CQEO-based scheme are presented.

In spatially distributed systems such as smart buildings and intelligent transportation systems, control of spatio-temporal patterns is an important issue. In this paper, we consider a finite-horizon optimal spatio-temporal pattern control problem where the pattern is specified by a signal spatio-temporal logic formula over finite traces, which will be called an SSTLf formula. We give the syntax and Boolean semantics of SSTLf. Then, we show linear encodings of the temporal and spatial operators used in SSTLf and we convert the problem into a mixed integer programming problem. We illustrate the effectiveness of this proposed approach through an example of a heat system in a room.

Register pushdown system (RPDS) is an extension of pushdown system (PDS) that has registers for dealing with data values. An LTL model checking method for RPDS with regular valuations has been proposed in previous work; however, the method requires the register automata (RA) used for defining a regular valuation to be backward-deterministic. This paper proposes another approach to the same problem, in which the model checking problem for RPDS is reduced to that problem for PDS by constructing a PDS bisimulation equivalent to a given RPDS. This construction is simpler than the previous model checking method and does not require RAs deterministic or backward-deterministic, and the bisimulation equivalence clearly guarantees the correctness of the reduction. On the other hand, the proposed method requires every RPDS (and RA) to have the freshness property, in which whenever the RPDS updates a register with a data value not stored in any register or the stack top, the value should be fresh. This paper also shows that the model checking problem with regular valuations defined by general RA is undecidable, and thus the freshness constraint is essential in the proposed method.

Nowadays, a rapid increase of demand on high-performance computation causes the enthusiastic research activities regarding massively parallel systems. An interconnection network in a massively parallel system interconnects a huge number of processing elements so that they can cooperate to process tasks by communicating among others. By regarding a processing element and a link between a pair of processing elements as a node and an edge, respectively, many problems with respect to communication and/or routing in an interconnection network are reducible to the problems in the graph theory. For interconnection networks of the massively parallel systems, many topologies have been proposed so far. The hypercube is a very popular topology and it has many variants. The bicube is a such topology and it can interconnect the same number of nodes with the same degree as the hypercube while its diameter is almost half of that of the hypercube. In addition, the bicube keeps the node-symmetric property. Hence, we focus on the bicube and propose an algorithm that gives a minimal or shortest path between an arbitrary pair of nodes. We give a proof of correctness of the algorithm and demonstrate its execution.

Intelligent transportation systems (ITSs) have been extensively studied in recent years to improve the safety and efficiency of transportation. The use of a radar system to enable the ITSs monitor the environment is robust to weather conditions and is less invasive to user privacy. Moreover, equipping the roadside units (RSUs) with radar modules has been deemed an economical and efficient option for ITS operators. However, because the detection and tracking parameters can significantly influence the radar system performance and the best parameters for different scenarios are different, the selection of appropriate parameters for the radar systems is critical. In this study, we investigated radar parameter selection and consequently proposes a parameter selection approach capable of automatically choosing the appropriate detection and tracking parameters for radar systems. The experimental results indicate that the proposed method realizes appropriate selection of parameters, thereby significantly improving the detection and tracking performance of radar systems.

We propose “Temporal Ensemble SSDLite,” a new method for video object detection that boosts accuracy while maintaining detection speed and energy consumption. Object detection for video is becoming increasingly important as a core part of applications in robotics, autonomous driving and many other promising fields. Many of these applications require high accuracy and speed to be viable, but are used in compute and energy restricted environments. Therefore, new methods that increase the overall performance of video object detection i.e., accuracy and speed have to be developed. To increase accuracy we use ensemble, the machine learning method of combining predictions of multiple different models. The drawback of ensemble is the increased computational cost which is proportional to the number of models used. We overcome this deficit by deploying our ensemble temporally, meaning we inference with only a single model at each frame, cycling through our ensemble of models at each frame. Then, we combine the predictions for the last N frames where N is the number of models in our ensemble through non-max-suppression. This is possible because close frames in a video are extremely similar due to temporal correlation. As a result, we increase accuracy through the ensemble while only inferencing a single model at each frame and therefore keeping the detection speed. To evaluate the proposal, we measure the accuracy, detection speed and energy consumption on the Google Edge TPU, a machine learning inference accelerator, with the Imagenet VID dataset. Our results demonstrate an accuracy boost of up to 4.9% while maintaining real-time detection speed and an energy consumption of 181mJ per image.

Low-hit-zone frequency-hopping sequences (LHZ-FHSs) are frequency-hopping sequences with low Hamming correlation in a low-hit-zone (LHZ), which have important applications in quasi-synchronous communication systems. However, the strict quasi-synchronization may be hard to maintain at all times in practical FHMA networks, it is also necessary to minimize the Hamming correlation for time-shifts outside of the LHZ. The main objective of this letter is to propose a lower bound on the maximum correlation magnitude outside the low-hit-zone for LHZ-FHS sets. It turns out that the proposed bound is tight or almost tight in the sense that it can be achieved by some LHZ-FHS sets.

This brief proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate BFC area reduction for the use of this system in area-constrained continuous glucose monitoring contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated in 65-nm CMOS technology, the proposed system can achieve 99% BFC area reduction.

In this paper, we study the number of failed components in a consecutive-k-out-of-n:G system. The distributions and expected values of the number of failed components when system is failed or working at a particular time t are evaluated. We also apply them to the optimization problems concerned with the optimal number of components and the optimal replacement time. Finally, we present the illustrative examples for the expected number of failed components and give the numerical results for the optimization problems.

Most research on detecting shilling attacks focuses on users' rating behavior but does not consider that attackers may also attack the users' trusting behavior. For example, attackers may give a low score to other users' ratings so that people would think the ratings from the users are not helpful. In this paper, we define the trust shilling attack, propose the behavior features of trust attacks, and present an effective detection method using machine learning methods. The experimental results demonstrate that, based on our proposed behavior features of trust attacks, we can detect trust shilling attacks as well as traditional shilling attacks accurately.

High frame rate and ultra-low delay vision system, which can finish reading and processing of 1000fps sequence within 1ms/frame, draws increasing attention in the field of robotics that requires immediate feedback from image process core. Meanwhile, tracking task plays an important role in many computer vision applications. Among various tracking algorithms, Lucas Kanade (LK)-based template tracking, which tracks targets with high accuracy over the sub-pixel level, is one of the keys for robotic applications, such as factory automation (FA). However, the substantial spatial iterative processing and complex computation in the LK algorithm, make it difficult to achieve a high frame rate and ultra-low delay tracking with limited resources. Aiming at an LK-based template tracking system that reads and processes 1000fps sequences within 1ms/frame with small resource costs, this paper proposes: 1) High temporal resolution-based temporal iterative tracking, which maps the spatial iterations into the temporal domain, efficiently reduces resource cost and delay caused by spatial iterative processing. 2) Label scanner-based multi-stream spatial processing, which maps the local spatial processing into the labeled input pixel stream and aggregates them with a label scanner, makes the local spatial processing in the LK algorithm possible be implemented with a small resource cost. Algorithm evaluation shows that the proposed temporal iterative tracking performs dynamic tracking, which tracks object with coarse accuracy when it's moving fast and achieves higher accuracy when it slows down. Hardware evaluation shows that the proposed label scanner-based multi-stream architecture makes the system implemented on FPGA (zcu102) with resource cost less than 20%, and the designed tracking system supports to read and process 1000fps sequence within 1ms/frame.

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example.

Traditional safety analysis techniques have shown difficulties in incorporating dynamically changing structures of CPSs (Cyber-Physical Systems). STPA (System-Theoretic Process Analysis), one of the widely used, needs to unfold and arrange all hidden structures before beginning a full-fledged analysis. This paper proposes an intermediate model “Information Unfolding Model (IUM)” and a process “Information Unfolding Process (IUP)” to unfold dynamic structures which are hidden in CPSs and so help analysts construct control structures in STPA thoroughly.

With the high development of computation requirements in Internet of Things, resource-limited edge servers usually require to cooperate to perform the tasks. Most related studies usually assume a static cooperation approach which might not suit the dynamic environment of edge computing. In this paper, we consider a dynamic cooperation approach by guiding edge servers to form coalitions dynamically. It raises two issues: 1) how to guide them to optimally form coalitions and 2) how to cope with the dynamic feature where server statuses dynamically change as the tasks are performed. The coalitional Markov decision process (CMDP) model proposed in our previous work can handle these issues well. However, its basic solution, coalitional Q-learning, cannot handle the large scale problem when the task number is large in edge computing. Our response is to propose a novel algorithm called deep coalitional Q-learning (DCQL) to solve it. To sum up, we first formulate the dynamic cooperation problem of edge servers as a CMDP: each edge server is regarded as an agent and the dynamic process is modeled as a MDP where the agents observe the current state to formulate several coalitions. Each coalition takes an action to impact the environment which correspondingly transfers to the next state to repeat the above process. Then, we propose DCQL which includes a deep neural network and so can well cope with large scale problem. DCQL can guide the edge servers to form coalitions dynamically with the target of optimizing some goal. Furthermore, we run experiments to verify our proposed algorithm's effectiveness in different settings.

In this study, we generate dialogue contents in which two systems discuss their distress with each other. The user inputs sentences that include environment and feelings of distress. The system generates the dialogue content from the input. In this study, we created dialogue data about distress in order to generate them using deep learning. The generative model fine-tunes the GPT of the pre-trained model using the TransferTransfo method. The contribution of this study is the creation of a conversational dataset using publicly available data. This study used EmpatheticDialogues, an existing empathetic dialogue dataset, and Reddit r/offmychest, a public data set of distress. The models fine-tuned with each data were evaluated both automatically (such as by the BLEU and ROUGE scores) and manually (such as by relevance and empathy) by human assessors.

In the fifth-generation mobile communications system (5G), it is critical to ensure wireless security as well as large-capacity and high-speed communication. To achieve this, a chaos modulation method as an encrypted and channel-coded modulation method in the physical layer is proposed. However, in the conventional chaos modulation method, the decoding complexity increases exponentially with respect to the modulation order. To solve this problem, in this study, a hybrid modulation method that applies quadrature amplitude modulation (QAM) and chaos to reduce the amount of decoding complexity, in which some transmission bits are allocated to QAM while maintaining the encryption for all bits is proposed. In the proposed method, a low-complexity decoding method is constructed by ordering chaos and QAM symbols based on the theory of index modulation. Numerical results show that the proposed method maintains good error-rate performance with reduced decoding complexity and ensures wireless security.

Distributed edge cloud computing is an important computation infrastructure for Internet of Things (IoT) and its task offloading problem has attracted much attention recently. Most existing work on task offloading in distributed edge cloud computing usually assumes that each self-interested user owns one edge server and chooses whether to execute its tasks locally or to offload the tasks to cloud servers. The goal of each edge server is to maximize its own interest like low delay cost, which corresponds to a non-cooperative setting. However, with the strong development of smart IoT communities such as smart hospital and smart factory, all edge and cloud servers can belong to one organization like a technology company. This corresponds to a cooperative setting where the goal of the organization is to maximize the team interest in the overall edge cloud computing system. In this paper, we consider a new problem called cooperative task offloading where all edge servers try to cooperate to make the entire edge cloud computing system achieve good performance such as low delay cost and low energy cost. However, this problem is hard to solve due to two issues: 1) each edge server status dynamically changes and task arrival is uncertain; 2) each edge server can observe only its own status, which makes it hard to optimize team interest as global information is unavailable. For solving these issues, we formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) which can well handle the dynamic features under partial observations. Then, we apply a multi-agent reinforcement learning algorithm called value decomposition network (VDN) and propose a VDN-based task offloading algorithm (VDN-TO) to solve the problem. Specifically, the motivation is that we use a team value function to evaluate the team interest, which is then divided into individual value functions for each edge server. Then, each edge server updates its individual value function in the direction that can maximize the team interest. Finally, we choose a part of a real dataset to evaluate our algorithm and the results show the effectiveness of our algorithm in a comparison with some other existing methods.

Parallel processing is a typical approach to answer analytical queries on large database. As the size of the database increases, we often try to increase the parallelism by incorporating more processing nodes. However, this approach increases the possibility of node failure as well. According to the conventional practice, if a failure occurs during query processing, the database system restarts the query processing from the beginning. Such temporal cost may be unacceptable to the user. This paper proposes a fault-tolerant query processing mechanism, named PhoeniQ, for analytical parallel database systems. PhoeniQ continuously takes a checkpoint for every operator pipeline and replicates the output of each stateful operator among different processing nodes. If a single processing node fails during query processing, another can promptly take over the processing. Hence, PhoneniQ allows the database system to efficiently resume query processing after a partial failure event. This paper presents a key design of PhoeniQ and prototype-based experiments to demonstrate that PhoeniQ imposes negligible performance overhead and efficiently continues query processing in the face of node failure.