An adaptive command filter control of nonlinear system with friction input is formulated in this paper. First, based on the obtained state space model, a command filter control method is proposed, which can address the “explosion of complexity” problem existed in traditional backstepping design and ensure the asymptotic convergence of the tracking errors. Moreover, to cope with the problem of filter error between filter output and virtual control signal, dynamic error compensation system is designed. Next, a HONN system is employed to simplify the calculation and approximate the uncertainties in the system. At last, in order to clarify the effectiveness of the above theory, simulation results are given.

To solve the problem that the filtering accuracy of the online calibration decreases or even diverges due to timevarying noise and outlier value interference, a SINS/CNS/GPS high-precision integrated navigation online calibration method based on the improved Sage-Husa adaptive filtering algorithm is designed. In the proposed method, a 21-dimensional state space model and 9-dimensional measurement model are established. Furthermore, on the basis of the simplified Sage-Husa adaptive filtering algorithm, a smoothing estimator and an adaptive robust factor are introduced to suppress the influence on the filtering accuracy due to the abnormal disturbances in the measurement information, which improving the online calibration accuracy of integrated navigation. The simulation results show that the online calibration method based on the improved Sage-Husa adaptive filtering algorithm can better calibrate the error parameters, especially the calibration of the lever arm error for the east and up directions.

This article presents a new concept of fully analogue adaptive filters. The adaptation is based on fully analogue neural networks. With the use of a filter bank, it can be used for high frequency and real-time adaptation. The properties of this concept are verified using electronic circuit simulations.

At the heart of most adaptive filtering techniques lies an iterative statistical optimisation process. These techniques typically depend on adaptation gains, which are scalar parameters that must reside within a region determined by the input signal statistics to achieve convergence. This manuscript revisits the paradigm of determining near-optimal adaptation gains in adaptive learning and filtering techniques. The adaptation gain is considered as a matrix that is learned from the relation between input signal and filtering error. The matrix formulation allows adequate degrees of freedom for near-optimal adaptation, while the learning procedure allows the adaption gain to be formulated even in cases where the statistics of the input signal are not precisely known.

This paper considers adaptive signal equalization in a channel with inter-symbol interference (ISI) distortion. In this process two adaptive FIR filters with different "forgetting factors" are used to update their filter coefficients. RLS algorithm is applied to optimize the filter coefficients. A comparison to a Genetic algorithm was done. The difference between the estimated mean square errors of both filters provides indication on how to change the "forgetting factors" to get closer to their optimal value. Finally, the computational efforts for filters with five different filter orders are compared. The obtained results prove the applicability of the presented approach.

Electrocardiography (ECG) is the most popular non-invasive method for generating an Electrocardiogram which contains some very interesting information about the electrical and myographic activities of the heart. It is a graph of voltage vs. time of the electrical activity of the heart using electrodes connected on the skin in various configurations. Due to the noninvasive nature of ECG and also due to capacitive or inductive coupling in this electrical circuit for ECG acquisition or electromyographic noises due to muscles adjacent to heart there is usually significant noises present in a typical ECG which makes it harder to analyze. There are many methods for denoising ECGs. In this paper an adaptive unscented Kalman filter, where the measurement noise covariance matrix is varied adaptively, is used for denoising acquired discrete ECG signals. The filtered output as well as the improvement of SNR is compared with other existing denoising frameworks like discrete wavelet transform and digital filters and extended Kalman filter, and unscented Kalman filter. The Adaptive Unscented Kalman Filter performed better than the aforementioned existing filtering algorithms in terms of maximum output SNR and MSE computed using Monte Carlo simulation.

This paper proposes improved combined step-size sign subband adaptive filter (ICSS-SSAF) algorithms with variable mixing factors robust to non-Gaussian noises such as impulsive noise. The CSS scheme is adopted to resolve a trade-off problem of step size in the SSAF, combining two adaptive filters with a large step size for a fast convergence rate and a small step size for low steady-state misalignment. Variable mixing factors (VMFs), whose values are changed at every iteration, are introduced to combine the two adaptive filters. To design the VMFs, a modified sigmoidal or arctangent function is employed. They are updated indirectly to minimize the power of approximated system output error, unlike the conventional algorithm using the 1 norm of the error vector composed of error signals divided by subbands. The recursive forms of VMFs are acquired by adopting the gradient method. The simulation results show that the proposed algorithms perform better than conventional algorithms in system identification scenarios.

Noise has become a significant concern in every domain. For instance, in image processing, we can see background noise when we take a snap, also in the field of communication, the information is corrupted by the noises present in the environment, and at the time of decryption, it is becoming challenging. Back then, in earlier days, discrete filters that had fixed frequency response were used to minimize the level of Noise in the information signals. But these filters were not effective as most noise sources have a flat wideband spectrum. After the availability of digital signal processors, to obliterate the wideband Noise, adaptive filters are frequently used in communication systems and digital signal processing systems to filter noisy signals. The Adaptive Noise Cancellation (ANC) approach helps to eliminate the Noise by altering its transient parameters dependent on the incoming signal. In this article, the performance of LMS, NLMS and RLS algorithms is studied for various types of ambient noises. A speech signal that is corrupted by engine noise, waterfall noise, and audio noise and with echo are applied to an ANC filter and the improvement in signal to noise ratio is evaluated with different adaptive filter algorithms.

Through the thorough exploration of the adaptive filter structure and the LMS adaptive filter algorithm, the filter performance of the adaptive filter algorithm can be clearly mastered.The solution formula of LMS algorithm is based on it, and DSP software programming and Matlab simulation programming methods are used to lay the foundation for the effective implementation of LMS algorithm.Therefore, based on the adaptive filtering algorithm, the embedded software simulation development system is analyzed to help the application of adaptive filtering theory.

Mobile Ad Hoc Networks (MANETs) are more susceptible to security threats due to the nature of openness and decentralized administration. Even though there exist several standard cryptography and encryption methods, they induce an additional computational and storage burden on the resource constrained mobile nodes in MANETs. To sort out this issue, this paper proposes a simple trust management mechanism called as Mobility and Trust Aware Adaptive Location Aided Routing (MTALAR). Initially, MTALAR founds the request zone whose sides are parallel to the line connecting the source and destination nodes. Next, the source node finds a trustworthy route through multi-hop nodes based on a new factor called as Mobile-Trust Factor (MTF). MTF is the combination of communication trust and mobility. Communication trust ensures a correct detection of malicious nodes and mobility ensures a proper protection for innocent nodes. After route discovery, the source node periodically measures the MTF of the multi-hop nodes through HELLO packets. Based on the obtained MTF values, the source node declares the corresponding node as malicious or not. Extensive simulations performed on the proposed method prove the superiority in the identification of malicious nodes.

The internal attack launched by compromised nodes is a serious security risk in distributed networks. It is due to the openness of wireless channel and the lack of trust relationships between nodes. The trust model is an interesting approach to detect and remove these compromised nodes from the trusted routing table and maintain trust relationships in distributed networks. However, how to construct the secure dominating node to aggregate and forward the information is an enormous challenge in distributed networks. In this paper, a distributed construction algorithm of multi-layer connected dominating set is proposed for secure routing with trust models. The probabilities of nodes being compromised, combined with the trust value of the trust model, are used to construct a trusted multi-layer connected dominating set. Moreover, the impact of a node being compromised on the distributed network is quantified as loss expectation. The simulation results show that the proposed algorithm can effectively reduce the impact of nodes being compromised on the distributed network, and enhance the security of the network.

Mobile Ad-hoc Network (MANET) is a collection of self-contained mobile nodes that communicate with each other over wireless links. The mobile units within the radio range can interact with one another directly, whilst other nodes need the assistance of nearby nodes in order to communicate, which is accomplished via the use of routing protocols. Several routing protocols make use of rebroadcasting strategies in order to decrease the amount of route overhead they incur. Zone-based scheduling heuristics are being explored to decrease redundant transmitting by on-demand simultaneous collision steered broadcasting, resulting in reduced energy consumption. While broadcast storms are common, they are caused by parallel collision directed transmission, which leads to increased power utilization. This article examined a unique technique for boosting the energy efficiency of zone-based scheduling strategies, which manage the network architecture by predicting the node die out rate and hence regulate the network configuration. In addition, a game theory framework integrated with an energy-efficient region routing protocol to enhance MANET QoS routing (QoS-IRBRP) was developed. Ultimately, the simulation results reveal the recommended algorithm s gives enlarged energy efficiency in terms of packet drop index and control overhead when compared to alternative routing strategies (RBRP, IRBRP).

Mobile ad hoc networks (MANETs) are selforganizing nodes that work together to create continuous nodes to reach its final destination. In this research, fractional hybrid optimization is proposed to attain the optimal path from the existing multipath during routing in MANET. The quality of service (QoS) is need to be optimization. Thus, the routing table is established to identify the performance of the node for further communication based on the trust factor, response time, request message, and packets sent, and received between the source as well as the destination. The multiobjective function considered for attaining the possible routes is energy, jitter, delay, throughput, distance, and latency using the shortest path algorithm. The fractional optimization attains the average residual energy as 0.077 J, delay as 0.512 sec, and 27 alive nodes for the analysis of 200 nodes.

Traditional multi-path transmission routing can improve the reliability of transmission, but the maintenance and update of paths need to consume a lot of resources. Opportunistic routing, compared with traditional routing strategies, is suitable for wireless sensor network transmission, but it faces the problem that there will be conflicts in simultaneous propagation. In this paper, combining reliable routing and opportunistic routing, considering opportunistic routing with node location information to determine the priority of forwarding candidate nodes can well balance the relationship between energy consumption and reliable transmission.

Along with the recent growth of IOT applications, related security issues have also received a great attention. Various IOT vulnerabilities have been investigated so far, among which, internal attacks are the most important challenge that are mostly aimed at destroying IOT standard routing protocol (RPL). Recent studies have introduced trust concept as a practical tool for timely diagnosis and prevention of such attacks. In this paper trust evaluation is performed based on investigating the traffic flow of devices and detecting their behavior deviations in case of RPL attack scenarios, which is formulated as a sequence prediction problem and a new Trust-based RPL Attacks Detection (TRAD) algorithm is proposed using Recurrent Neural Networks (RNNs). Traffic behavior prediction based on historical behavior and deviation analysis, provides the possibility of anomaly detection, which has an enormous effect on the accuracy and predictability of attack detection algorithms. According to the results, the proposed model is capable of detecting compromised IOT nodes in different black-hole and selective-forwarding attack scenarios, just at the beginning time of the first attack, which provides the possibility of early detection and isolation of malicious nodes from the routing process.

In wireless sensor networks (WSNs), data communication is performed using different routing protocols. One of the mostly used routing protocol is cluster-based routing protocol. The foundation of cluster-based routing protocol is the formation of clusters and selection of Cluster Head (CH) for energy-efficient transmission. CH are solely responsible for data packets transmission among nodes. But, such protocols are susceptible to network attacks. In this paper, a novel secure cluster-based routing protocol is designed for safe data transmission. Based on the findings, the designed solution offers excellent capabilities in terms of packet delivery ratio (PDR), energy usage, throughput, and End-to-End delay.

The intra cluster routing protocol designed in this paper is based on the trust model of vehicle nodes. The trust model is mainly composed of the reliability of vehicle nodes and other attribute factors of vehicle nodes. The cloud data center uses principal component analysis to calculate the trust value. The attributes of the vehicle nodes in the cluster are forwarded to the cluster head along with the Hello message packet. The cluster head forms an attribute list and sends it to the cloud data center, which calculates the trust value. When there are multiple paths to choose, the vehicle nodes in the cluster take the trust value of each node on the path as an important basis for path selection, forming the routing process in the cluster. By adding trust computation, reliable data transmission between vehicle nodes can be guaranteed. The experimental results show that the intra cluster routing protocol designed by the trust model in this scheme is effective and feasible, and the maximum speed of vehicle nodes has an impact on the performance of the routing algorithm.

Vehicular Ad Hoc Networks (VANETs) rely heavily on trustworthy message exchanges between vehicles to enhance traffic efficiency and transport safety. Although cryptographybased methods are capable of alleviating threats from unauthenticated attackers, they can not prevent attacks from those legitimate network participants. This paper proposes a trust model to deal with attackers from the latter case, who can tamper with their received messages and deliberately decrease the trust value of benign vehicles. The trust evaluation process is formed by two stages: (i) the local trust evaluation at vehicles and (ii) trust aggregation on Road Side Units (RSUs). In the local trust evaluation stage, vehicles detect attacks and calculate the trust value for others in a distributed manner. Also, the social metrics of vehicles are calculated based on interaction records and trajectories. In the trust aggregation stage, each RSU collects local data from nearby vehicles and derives aggregation weights from the eigenvector centrality of the local trust network and social metrics. Then the RSU broadcasts the aggregated trust value towards vehicles in proximity. These vehicles can thus obtain a more accurate and comprehensive view. Vehicles with trust value below a preset threshold will be considered malicious. Extensive simulations based on the ONE simulator show that the proposed model (TECS) outperforms another benchmark model (IWOT-V) regarding the malicious vehicle detection and the delivery rate of authentic messages.

In Mobile Adhoc Networks (MANETs), resilient optimization is based on the least energy utilization as well as privacy. The crucial concerns for the productive design to provide multi-hop routing are security and energy consumption. Concerning these problems, we present in this paper an author proposed routing protocol called Protected Quality of Service (QoS) aware Energy Efficient Routing protocol. It is developed on trust along with energy efficiency and points to improve MANET security.

Internet of Things (IoT) has become extremely prominent for industrial applications and stealthy modification deliberately done by insertion of Hardware Trojans has increased widely due to globalization of Integrated Circuit (IC) production. In the proposed work, Hardware Trojan is detected at the gate level by considering netlist of the desired circuits. To mitigate with golden model dependencies, proposed work is based on unsupervised detection of Hardware Trojans which automatically extracts useful features without providing clear desired outcomes. The relevant features from feature dataset are selected using eXtreme Gradient Boosting (XGBoost) algorithm. Average True Positive Rate (TPR) is improved about 30\% by using Clustering-based local outlier factor (CBLOF) algorithm when compared to local outlier factor algorithm. The simulation is employed on Trust-HUB circuits and achieves an average of 99.83\% True Negative Rate (TNR) and 99.72\% accuracy which shows the efficiency of the detection method even without labelling data.

Hardware Trojans (HT) are minuscule circuits embedded by an adversary for malicious purposes. Such circuits posses stealthy nature and can cause disruption upon activation. To detect the presence of such circuits, appropriate test vectors need to be applied. In this regard, the genetic algorithm (GA) seems to be the most promising technique due to its exploration capability. However, like most of the existing techniques, GA also suffers from exploring the huge search space. In this article a GA based methodology is proposed incorporating the information about potential inputs into it. Experimental results analysis signifies that the identification of the relevant inputs for GA provides an optimal solution. The significance of proposed methodology is endorsed by applying the proposed GA technique on different ISCAS ’85 benchmark circuits. A noteworthy improvement on run time is observed while simultaneously providing improved test set quality than the state-of-the art technique.

Recently, hardware Trojan has become a serious security concern in the integrated circuit (IC) industry. Due to the globalization of semiconductor design and fabrication processes, ICs are highly vulnerable to hardware Trojan insertion by malicious third-party vendors. Therefore, the development of effective hardware Trojan detection techniques is necessary. Testability measures have been proven to be efficient features for Trojan nets classification. However, most of the existing machine-learning-based techniques use supervised learning methods, which involve time-consuming training processes, need to deal with the class imbalance problem, and are not pragmatic in real-world situations. Furthermore, no works have explored the use of anomaly detection for hardware Trojan detection tasks. This paper proposes a semi-supervised hardware Trojan detection method at the gate level using anomaly detection. We ameliorate the existing computation of the Sandia Controllability/Observability Analysis Program (SCOAP) values by considering all types of D flip-flops and adopt semi-supervised anomaly detection techniques to detect Trojan nets. Finally, a novel topology-based location analysis is utilized to improve the detection performance. Testing on 17 Trust-Hub Trojan benchmarks, the proposed method achieves an overall 99.47\% true positive rate (TPR), 99.99\% true negative rate (TNR), and 99.99\% accuracy.

There have been reports of threats that cause electromagnetic information leakage by inserting Hardware Trojans (HT) into the signal traces around components on the printed circuit board (PCB). In this threat, the HT insertion is assumed not only at the manufacturing stage but also during the in-transit or in the field after shipment, and the threat may extend to devices that are not considered to be threatened by HT insertion implemented inside conventional ICs. This paper discusses the detection method for the HT insertion, which is implementable on a PCB without external measurement equipment. Additionally, we validate the method in more practical situations, detecting the HT on populated PCBs. The method employs an on-chip touch sensor to measure the changes in electrical characteristics caused by HT insertion. Specifically, HT insertion is detected by observing the change in capacitance and insulation resistance associated with HT insertion using the on-chip sensor, and detecting the difference from the measurement result when HT is not inserted to signal traces. In the experiment, we build an evaluation environment, which emulates a populated PCB, based on the HT insertion method reported in previous studies and observe the change in capacitance and insulation resistance on the connected signal trace using a microprocessor equipped with a constant current source and an analog-digital converter that constitute the onchip sensor. Then, we show that HT insertion on the signal trace can be detected from the output values of the on-chip sensor before and after HT insertion.

Emerging Analog Trojans such as A2, large-delay Trojans, and row-hammer have been shown to be more stealthier than previously known digital Trojans. They are smaller sized, do not rely on inputs for triggering, and the trigger for their payload can be made arbitrarily delayed, like a ticking time bomb. Furthermore, analog Trojans can easily evade detection due to their novel nature and incompatibility with the digital design and validation flow. In this paper, we propose a current signature-based detection scheme, which can effectively catch various analog Trojans at both run-time and production time validation. The paper includes techniques that advance Trojan detection method through incorporating detection of transient variation in the power supply current. Proposed current-sensor can sense currents down to 10s of nano-Amps improving over prior power sensing based techniques. Further, a configurable design of current sensor is developed to enable large range sensing capability. The design is also developed to be compatible with the digital design flow and can be logic obfuscated. This detection method can be used at run-time to potentially fence off activation of analog Trojans in the field through early warning signals. The commercial 65nm CMOS technology is utilized to verify the proposed idea.

This work proposes a novel hardware Trojan detection method that leverages static structural features and behavioral characteristics in field programmable gate array (FPGA) netlists. Mapping of hardware design sources to look-up-table (LUT) networks makes these features explicit, allowing automated feature extraction and further effective Trojan detection through machine learning. Four-dimensional features are extracted for each signal and a random forest classifier is trained for Trojan net classification. Experiments using Trust-Hub benchmarks show promising Trojan detection results with accuracy, precision, and F1-measure of 99.986\%, 100\%, and 99.769\% respectively on average.