Cockroaches can traverse unknown obstacle-terrain, self-right on the ground and climb above the obstacle. However, they have limited motion, such as less activity in light/bright areas and lower temperatures. Therefore, the movement of the cyborg cockroaches needs to be optimized for the utilization of the cockroach as a cyborg insect. This study aims to increase the search rate and distance traveled by cockroaches and reduce the stop time by utilizing automatic stimulation from machine learning. Multiple machine learning classifiers were applied to classify the offline binary classification of the cockroach movement based on the inertial measuring unit input signals. Ten time-domain features were chosen and applied as the classifier inputs. The highest performance of the classifiers was implemented for the online motion recognition and automatic stimulation provided to the cerci to trigger the free walking motion of the cockroach. A user interface was developed to run multiple computational processes simultaneously in real time such as computer vision, data acquisition, feature extraction, automatic stimulation, and machine learning using a multithreading algorithm. On the basis of the experiment results, we successfully demonstrated that the movement performance of cockroaches was importantly improved by applying machine learning classification and automatic stimulation. This system increased the search rate and traveled distance by 68% and 70%, respectively, while the stop time was reduced by 78%.

Due to their size and locomotion ability, cockroaches are favorable as hybrid robot platforms in search and rescue (SAR) missions. However, cockroaches most likely approach the corner area and stay for an uncertain time. This natural behavior will hinder the utilization of cyborg cockroaches in SAR missions under rubble, unstructured, and unknown areas. Therefore, we proposed onboard automatic obstacle avoidance and human detection that can run on the wireless backpack stimulator without an external motion capture system. A low-power and small-size Time of Flight (ToF) sensor was selected as a distance measurement sensor, while a low-resolution thermopile array sensor was applied for human presence detection. The implemented feedback control based on IMU and ToF sensors has successfully navigated the cyborg cockroach to avoid obstacles and escape from the sharp corners in the laboratory unstructured area without stopping or being trapped. It could also recognize the human presence when the human was in front of it in real-time. Due to its performance, the random forest classifier was implemented as an embedded human detection system. It could achieve the highest accuracy at a distance of around 25 cm (92.5%) and the lowest accuracy at about 100 cm (70%).

Biobot-based insects have been investigated so far for various applications such as search and rescue operations, environmental monitoring, and discovering the environment. These applications need a strong international collaboration to complete the tasks. However, during the COVID-19 pandemic, most people could not easily move from one country to another because of the travel ban. In addition, controlling biobots is challenging because only experts can operate the cockroach behavior with and without stimulated response. In order to solve this issue, we proposed a user-friendly teleoperation user interface (UI) to monitor and control the biobot between Japan and Bangladesh without onsite operation by experts. This study applied Madagascar hissing cockroaches (MHC) as a biobot hybrid robot. A multithreading algorithm was implemented to run multiple parallel computations concurrently on the UI. Virtual network computing (VNC) was implemented on the teleoperation UI as remote communication for streaming real-time video from Japan to Bangladesh and sending remote commands from Bangladesh to Japan. In the experiments, a remote operator successfully steered the biobot to follow a predetermined path through a developed teleoperation UI with a time delay of 275 ms. The proposed interactive and intuitive UI enables a promising and reliable system for teleoperated biobots between two remote countries.

Deep learning is very popular methods for facial expression recognition (FER) and classification. Different types of deep learning algorithms have been used for FER such as deep belief network (DBN) and convolutional neural network (CNN). In this paper, we analyze various deep learning methods and their results. We have chosen Deep convolutional neural network as the best algorithms for facial expression detection and classification. In our study, we have tested the algorithm using Japanese Female facial expressions database (JAFFE) datasets by anaconda software. The deep convolution neural networks with JAFFE datasets accuracy rate around 97.01%.

Autonomous navigation of cyborg insects in complex environments remains a challenging issue. Cyborg insects, which combine biological organisms with electronic components, offer a unique approach to tackle such challenges. This study presents a biohybrid behavior-based navigation (BIOBBN) system that enables cyborg cockroaches to navigate complex environments autonomously. Two navigation algorithms were developed: reach-avoid navigation for less complex environments and adaptive reach-avoid navigation for more challenging scenarios. This algorithm, especially the second one, leveraged the cockroaches’ natural behaviors, such as wall-following and climbing, to navigate around and over obstacles. Experiments in simulated environments, including sand and rock-covered surfaces, demonstrate the effectiveness of the BIOBBN system in enabling cyborg cockroaches to navigate and reach target locations. The denser second scenario required more time due to increased obstacle avoidance and natural climbing behavior. Overall performance was promising, highlighting the potential of biohybrid navigation for autonomous cyborg insects in navigating complex environments.

The Purpose of this work is to improve the availability and remote access for secure enterprise network infrastructure by using dual hub dual DMVPN (Dynamic Multipoint VPN). Using multipoint GRE (mgre) over IPsec data transmission in the enterprise network is highly reliable secured. DMVPN is a technology that can be associated with different protocols concept such as IPsec encryption, next hop resolution protocol (NHRP), generic routing encapsulation (GRE) and it provides dynamic and static IPsec tunnel between spoke to the hub, spoke to spoke communication. In this paper, we implement the DMVPN technique to constructs a secure enterprise network for an organization and employing hot standby routing protocol (HSRP) to overcome the unavailability and failure of a certain network. The simulation was done by GNS3 and packets were captured by Wireshark software. It was revealed by the test that, DMVPN technology with HSRP protocols completely fulfills the demand of availability that is vital for any enterprises. It offers a faster mode, highly efficient and practically valued venture and also provides accessibility by keeping the network always in upstate thereby facilities for building a safer and highly dependable network infrastructure.