Using Proportional-Integral-Differential approach for Dynamic Traffic Prediction in Wireless Network-on-Chip

The massive integration of cores in multi-core system has enabled chip designer to design systems while meeting the power performance demands of the applications. Wireless interconnection has emerged as an energy efficient solution to the challenges of multi-hop communication over the wireline paths in conventional Networks-on-Chips (NoCs). However, to ensure the full benefits of this novel interconnect technology, design of simple, fair and efficient Medium Access Control (MAC) mechanism to grant access to the on-chip wireless communication channel is needed. Moreover, to adapt to the varying traffic demands from the applications running on a multicore environment, MAC mechanisms should dynamically adjust the transmission slots of the wireless interfaces (WIs). To ensure an efficient utilization of the wireless medium in a Wireless NoC (WiNoC), in this work we present the design of prediction model that is used by two dynamic MAC mechanism to predict the traffic demand of the WIs and respond accordingly by adjusting transmission slots of the WIs. Through system level simulations, we show that the traffic aware MAC mechanisms are more energy efficient as well as capable of sustaining higher data bandwidth in WiNoCs

Smart and Intelligent Automation for Industry 4.0 using Millimeter-Wave and Deep Reinforcement Learning

Innovations in communication systems, compute hardware, and deep learning algorithms have led to the advancement of smart industry automation. Smart automation includes industrial sectors such as intelligent warehouse management, smart infrastructure for first responders, and smart monitoring systems. Automation aims to maximize efficiency, safety, and reliability. Autonomous forklifts can significantly increase productivity, reduce safety-related accidents, and improve operation speed to enhance the efficiency of a warehouse. Forklifts or robotic agents are required to perform different tasks such as position estimation, mapping, and dispatching. Each of the tasks involves different requirements and design constraints. Smart infrastructure for first responder applications requires robotic agents like Unmanned Aerial Vehicles (UAVs) to provide situation awareness surrounding an emergency. An immediate and efficient response to a safety-critical situation is crucial, as a better first response significantly impacts the safety and recovery of parties involved. But these UAVs lack the computational power required to run Deep Neural Networks (DNNs) that are used to provide the necessary intelligence. In this dissertation, we focus on two applications in smart industry automation. In the first part, we target smart warehouse automation for Intelligent Material Handling (IMH), where we design an accurate and robust Machine Learning (ML) based indoor localization system for robotic agents working in a warehouse. The localization system utilizes millimeter-wave (mmWave) wireless sensors to provide feature information in the form of a radio map which the ML model uses to learn indoor positioning. In the second part, we target smart infrastructure for first responders, where we present a computationally efficient adaptive exit strategy in multi-exit Deep Neural Networks using Deep Reinforcement Learning (DRL). The proposed adaptive exit strategy provides faster inference time and significantly reduces computations

KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35m and 0.37m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles

Autonomous Vehicles and Machines Conference, at IS&T Electronic Imaging

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset

Fabrication of α‑Fe2O3 Nanostructures: synthesis, characterization, and their promising application in the treatment of Carcinoma A549 Lung Cancer Cells

In the present work, iron nanoparticles were synthesized in the α-Fe2O3 phase with the reduction of potassium hexachloroferrate(III) by using l-ascorbic acid as a reducing agent in the presence of an amphiphilic non-ionic polyethylene glycol surfactant in an aqueous solution. The synthesized α-Fe2O3 NPs were characterized by powder X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, atomic force microscopy, dynamic light scattering, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and ultraviolet–visible spectrophotometry. The powder X-ray diffraction analysis result confirmed the formation of α-Fe2O3 NPs, and the average crystallite size was found to be 45 nm. The other morphological studies suggested that α-Fe2O3 NPs were predominantly spherical in shape with a diameter ranges from 40 to 60 nm. The dynamic light scattering analysis revealed the zeta potential of α-Fe2O3 NPs as −28 ± 18 mV at maximum stability. The ultraviolet–visible spectrophotometry analysis shows an absorption peak at 394 nm, which is attributed to their surface plasmon vibration. The cytotoxicity test of synthesized α-Fe2O3 NPs was investigated against human carcinoma A549 lung cancer cells, and the biological adaptability exhibited by α-Fe2O3 NPs has opened a pathway to biomedical applications in the drug delivery system. Our investigation confirmed that l-ascorbic acid-coated α-Fe2O3 NPs with calculated IC50 ≤ 30 μg/mL are the best suited as an anticancer agent, showing the promising application in the treatment of carcinoma A549 lung cancer cells

Evaluation of 60 GHz Wireless Connectivity for an Automated Warehouse Suitable for Industry 4.0

The fourth industrial revolution focuses on the digitization and automation of supply chains resulting in a significant transformation of methods for goods production and delivery systems. To enable this, automated warehousing is demanding unprecedented vehicle-to-vehicle and vehicle-to-infrastructure communication rates and reliability. The 60 GHz frequency band can deliver multi-gigabit/second data rates to satisfy the increasing demands of network connectivity by smart warehouses. In this paper, we aim to investigate the network connectivity in the 60 GHz millimeter-wave band inside an automated warehouse. A key hindrance to robust and high-speed network connectivity, especially, at mmWave frequencies stems from numerous non-line-of-sight (nLOS) paths in the transmission medium due to various interacting objects such as metal shelves and storage boxes. The continual change in the warehouse storage configuration significantly affects the multipath reflected components and shadow fading effects, thus adding complexity to establishing stable, yet fast, network coverage. In this study, network connectivity in an automated warehouse is analyzed at 60 GHz using Network Simulator-3 (NS-3) channel simulations. We examine a simple warehouse model with several metallic shelves and storage materials of standard proportions. Our investigation indicates that the indoor warehouse network performance relies on the line-of-sight and nLOS propagation paths, the existence of reflective materials, and the autonomous material handling agents present around the access point (AP). In addition, we discuss the network performance under varied conditions including the AP height and storage materials on the warehouse shelves. We also analyze the network performance in each aisle of the warehouse in addition to its SINR heatmap to understand the 60 GHz network connectivity

The Advances, Challenges and Future Possibilities of Millimeter-Wave Chip-to-Chip Interconnections for Multi-Chip Systems

With aggressive scaling of device geometries, density of manufacturing faults is expected to increase. Therefore, yield of complex Multi-Processor Systems-on-Chips (MP-SoCs) will decrease due to higher probability of manufacturing defects especially, in dies with large area. Therefore, disintegration of large SoCs into smaller chips called chiplets will improve yield and cost of complex platform-based systems. This will also provide functional flexibility, modular scalability as well as the capability to integrate heterogeneous architectures and technologies in a single unit. However, with scaling of the number of chiplets in such a system, the shared resources in the system such as the interconnection fabric and memory modules will become performance bottlenecks. Additionally, the integration of heterogeneous chiplets operating at different frequencies and voltages can be challenging. State-of-the-art inter-chip communication requires power-hungry high-speed I/O circuits and data transfer over long wired traces on substrates. This increases energy consumption and latency while decreasing data bandwidth for chip-to-chip communication. In this paper, we explore the advances and the challenges of interconnecting a multi-chip system with millimeter-wave (mm-wave) wireless interconnects from a variety of perspectives spanning multiple aspects of the wireless interconnection design. Our discussion on the recent advances include aspects such as interconnection topology, physical layer, Medium Access Control (MAC) and routing protocols. We also present some potential paradigm-shifting applications as well as complementary technologies of wireless inter-chip communications