The increasing complexity and performance demand of modern electronic systems have led to widespread adoption of Heterogeneous Integration (HI) through advanced System-in-Package (SiP) technologies. These architectures integrate diverse chiplets onto a common interposer to enable modular design, improved performance, and reduced cost. However, such integration introduces new runtime security vulnerabilities, particularly across the inter-chiplet communication facilitated by Network-on-Chip (NoC) interconnects. Traditional system-on-chip (SoC) security solutions are inadequate in this context due to the heterogeneity of chiplets and the lack of unified control across vendors and already fabricated blackbox chiplets. In this work, we propose RunSiP, a property-based runtime security monitoring framework for NoC-based SiP architectures. RunSiP integrates Distributed Runtime Security Monitors (DRSMs) into the Chiplet Hardware Security Module (CHSM) to dynamically enforce security policies during runtime. These policies are generated based on SiP and chiplet specifications, identifying potential attack vectors and guiding policy enforcement across the distributed NoC. RunSiP monitors and detects violations of confidentiality, integrity, and secure communication during inter-chiplet data exchange. We demonstrate the RunSiP framework on a Xilinx Kintex UltraScale 2.5D FPGA platform and show its effectiveness in detecting various runtime security violations with minimal performance overhead. Our framework provides a scalable and modular solution for safeguarding SiP designs, ensuring secure runtime operation even in the presence of untrusted or compromised chiplets.

In order to maintain the historical progress of CMOS technology and comply with Moore's Law, heterogeneous integration (HI) integrates separately manufactured chiplets onto an interposer substrate to create systems that resemble system-on-chip (SoC) structures, which are now more widely known as system-in-package (SiP). Heterogeneously integrated SiP designs based on chiplets continue to improve in transistor density despite the slowing rate of Moore's Law, satisfying strict time-tomarket requirements while minimizing area and cost. However, concerns about SiP intellectual property (IP) overproduction, reverse engineering, and IP infringement arise as advanced packaging methods emerge. The attackers would like to gain a competitive edge by illicitly acquiring confidential IP, leading to potential repercussions such as IP loss and overproduction. While hardware-focused solutions like traditional hardware metering offer IP protection, safeguarding chiplets and entire systems in 2.5D/3D SiPs demands advancements beyond traditional approaches used in 2D monolithic SoC designs. This paper introduces SiPMeter, an innovative hardware metering technique for SiP architectures leveraging open-market chiplets. SiPMeter utilizes inter-chiplet interactions through interposers to prevent chiplet(s) re-use and tracing SiPs in the heterogeneous integration supply chain. Interactions are also protected using Shamir-Secret-Sharing, allowing SiP integrators to confidentially enable hardware metering throughout the system from cloning, misuse, and attacks.

As the semiconductor industry adopts system-in-package (SiP) technologies to address the limitations of Moore's Law and Dennard scaling, the increasing complexity of heterogeneous integration (HI) makes efficient physical fault injection attacks much more challenging to execute. Hence, skilled attackers attempt to compromise the device's confidentiality, integrity, or availability through remote fault-injection (RFI) attacks without physical access to the target system. In this case, this transformative shift of the threat models and evolving threats associated with RFI attacks render the traditional countermeasures incompatible. To overcome these challenges, we propose ReFID, a system-wide RFI attack detection and mitigation strategy for heterogeneous systems, by placing on-chip sensors and developing a system-level root-of-trust (RoT) module. The RoT module implemented as an embedded FPGA (e-FPGA) controls the sensors and analyzes the run-time sensor data to detect malicious activities within a trusted region of a heterogeneous system caused by remote attacks. Upon detecting any fault, it immediately creates uncontrollable scenarios to prevent an attacker from exploiting the impact of an RFI attack. We execute a combination of remote overclocking and undervolting attacks on a crypto application within the trusted execution environment of a CPU-FPGA heterogeneous system to showcase the ReFID's efficacy.

Abstract A system-in-package (SiP) design takes advantage of cutting-edge packaging technology and heterogeneous integration (HI) in response to the growing need for aggressive time-to-market, high-performance, less expensive, and smaller systems. However, aggregating dies with different functionalities introduces new attack vectors with fault-injection attacks (FIA) that can effectively alter a circuit's data and control flow maliciously to cause disruptions of secure communication or sensitive information leakage. Additionally, traditional threat models associated with FIA on a 2D monolithic system-on-chip (SoC), and the corresponding mitigation techniques may not be compatible with modern 2.5D and 3D SiP architectures. To address these limitations, we propose system-aware fault injection attack detection for SiP architectures (SYSFID), a real-time and on-chip sensor-based fault monitoring approach integrated into a system-level design. SYSFID detects any fault-induced anomalous alterations in path delays of the components of inter-chiplet networks by strategically placing on-chip fault-to-time converter (FTC) sensors and controlling them efficiently to safeguard overall system security. To demonstrate the effectiveness of SYSFID, we detect several fault injection attempts on the FPGA implementation of a network-on-chip (NoC) based architecture during secure network packet transfers. Our experiments also illustrate that the SYSFID framework reliably senses both global and local FIAs with minimal overheads.

As Moore's law comes to a crawl, advanced package and integration techniques become increasingly crucial by allowing for the combination of fabricated silicon dies, so-called chiplet, to constitute system-in-package (SiP) achieving a much better yield and time-to-market. However, due to inherent security concerns within the convoluted semiconductor supply chain and in-field environment, hostile attacks targeting software and hardware applications can present a formidable challenge to ensuring the security of SiP. Even worse, the immanent black-box nature of product chiplets renders most conventional security inspection and testing solutions less useful. Therefore, we present our SiPGuard in this article to enable the security monitoring capability during run time to noninvasively track the application-level behaviors of target chiplets and detect any deviations potentially induced by underlying malicious intrusions. The security monitoring mechanism utilizes information-bearing system-level power noise variation and machine learning (ML) techniques. Specifically, we utilize a trusted field-programmable gate array (FPGA) chiplet as our trust anchor to implement the lightweight power sensor and on-chip ML inference engine for near-sensor analysis. We prototype our solution on a 2.5-D chiplet-based FPGA device and demonstrate the effectiveness against threats at software/hardware levels by identifying the consequent power anomalies of malicious activities.

As Moore's law comes to a crawl, heterogeneous integration-based system-in-package emerges as a promising direction to maintain the speedy rate of performance density improvement of modern integrated circuits by integrating fabricated silicon dies into a unified package. However, hardware security threats such as fault injection attacks present formidable challenges to the protection of on-chip assets. Even worse than a conventional monolithic device, system-in-package (SiP) might introduce malicious chiplets to allow for internal and remote power fault injection attacks due to the obscurity of the semiconductor supply chain. In order to thwart fault injection attacks, we present FISHI which aims to include a root-of-trust chiplet in the SiP to enable run-time system-level power noise variation monitoring capabilities and near-sensor machine learning inference for attack-induced anomaly detection. Specifically, we design a time-to-digital converter to collect power profiles of targeted applications as a reference and create a hardware ML engine accordingly to measure the deviations between the run-time power fluctuations and the golden ones. We prototype our FISHI solution on one of the chiplets in a Xilinx 2.5D FPGA SiP and demonstrate its effectiveness by detecting power fault injection attempts on an AES implementation on the other chiplet.

Social media platforms have become one of the primary mediums of communication nowadays. Along with communication, they are currently being utilized in a wide range of activities like digital marketing, customer care, e-learning etc. The unceasing use of social media is generating gigantic amount of textual data everyday. It is essential to properly analyze these data with the consideration of underlying human traits sentiments for exploring the full potential of these platforms. However, sentiment analysis from text has been considered as a challenging task because of the rapid use of informal and noisy words. Updated and powerful word embeddings are being invented almost every two years so that the machines could understand the underlying features of linguistics. Each of these embedding techniques excel in different aspects. In this paper, we present a novel RNN based sentiment polarity detection framework which feeds the power of three different word embeddings: Word2Vec, GloVe and SSWE into a single powerful network with three parallel branches. The proposed network effectively utilizes the semantic, syntactic and sentiment polarity wise embeddings in word vectors encoding three major aspect of language from the viewpoint of extracting sentiment information from text. Posts collected from twitter was used to train and validate the proposed network. The results demonstrate that the proposed network containing parallelly configured multiple word embeddings outperforms the single word vectorization techniques. Additionally, it shows comparable or better evaluation scores when compared to several contemporary state-of-the-art models.

People with hearing disability find it difficult to establish a communication specially because most of the communication mediums that people naturally use are vocal. Hearing impaired people sometimes risk their life in cases like busy roads or run by high speed vehicles. Furthermore, they are unable to hear people calling them. Even if they understand that they are being called or they hear any sudden noise, they can't figure out the direction of the sound which is out of their regular vision. Recent technologies can be used to help them. In this paper we propose a smart glass that will enable them to understand any sudden sound by separating the noise using active noise cancellation and recognize the sound by speech recognition, notify that they are being called and provide them the direction of sound using binaural cue. This glass can also be used for old people or people with lower hearing ability. Which can be integrated with modern commercial smart glasses for better performance and lower price.

Osteosarcoma is an osseous tumor that occurs in the metaphyseal area around the knee accounts for roughly 20% of bone cancers mostly affects patients younger than 20 years. Early diagnosis of osteosarcoma cancer can pave the way for an unlimited choice of therapy opportunities. Moreover, pathological estimation of necrosis and tumor cells determines the future intensity of chemotherapy radiation to apply to patient. The biopsy confirms the diagnosis and divulges the grade of the tumor, necrotic, and non-tumor cells. Due to a lack of radiologists in third world countries like Bangladesh, it is extremely difficult to diagnose cancer in the early stage. Moreover, to identify the chemotherapy effect during the chemotherapy period, multiple radiologists are required which is quite expensive for most cancer hospitals. In this paper, a Sequential Recurrent Convolutional Neural Network (RCNN) model consisting of CNN and bidirectional Gated Recurrent Units (GRU) is proposed, which performs exceptionally well with small numbers of histopathological osteosarcoma Haematoxylin and Eosin (H & E) stained images despite having the over-fitting problem, heterogeneity, intra-class variation, inter-class similarity, crowded context, the irregular shape of the nucleus and noisy data. Performance of the is compared with that of AlexNet, ResNet50, VGG16, LeNet and SVM models with the histopathological image dataset on osteosarcoma.

The number of patients with muscle disorders are increasing day by day for corporate jobs and less physical activity. Moreover, many people injure their muscles from sports or accidents. In most of these cases they need muscle stimulation treatment to gain the nerve sensitivity back. Muscle stimulator is frequently used in such cases. However, most of the people cannot get the service of a physiotherapist every day for muscle stimulation. On the other hand, the muscle stimulators are very costly and hard to use. Several conditions of muscle need a change of settings of the machine which also requires experts help. For this purpose, this paper suggests an Internet of thing (IoT) based cost efficient muscle stimulator which combines the facility of being affordable, providing better safety for current and easy to operate. This process can further be integrated with telemedicine and a smart hospital system.

Most of the patients today who face health problems, initially take advice from unprofessional or people with no knowledge that makes them more vulnerable. In many occasions, doctors also get confused with identifying actual disease. This might happen as they usually identify disease based on their limited experience. Moreover, general patient selects doctor according to their will and with no knowledge about the disease that may need specialist doctor. But some disease cannot be confirmed without a specialized doctor. Therefore, this paper proposes a Machine Learning based disease symptom analysis technique for assisting the patients seeking proper treatment by selecting accurate medical department using the symptom that they can easily recognize. Proposed framework will use machine learning technique to select a medical department based on the joint consideration of various disease symptoms of the patient. We investigate our proposed framework by using 9 different supervised machine learning techniques. Performance of framework for identifying appropriate medical department under the machine learning techniques is thoroughly investigated and compared. This framework can be used for telemedicine platform or in automated hospital management sector. This may create a path of enormous development in health care sector.

Despite the combined effort, the COVID-19 pandemic continues with a devastating effect on the healthcare system and the well-being of the world population. With a lack of RT-PCR testing facilities, one of the screening approaches has been the use of is chest radiography. In this paper, we propose an automatic chest x-ray image classification model that utilizes the pre-trained CNN architecture (DenseNet121, MobileNetV2) as a feature extractor, and wavelet transformation of the pre-processed images using the CLAHE algorithm and SOBEL edge detection. Our model can detect COVID-19 from x-ray images with high accuracy, sensitivity, specificity, and precision. The result analysis of different architectures and a comparison study of pre-processing techniques (Histogram Equalization and Edge Detection) are thoroughly examined. In this experiment, the Support Vector Machine (SVM) classifier fitted most accurately (accuracy 97.73%, sensitivity 97.84%, F1score 97.73%, specificity 97.73%, and precision 98.79%) with a wavelet and MobileNetV2 feature sets to identify COVID-19. The memory consumption is also examined to make the model more feasible for telemedicine and mobile healthcare application.

Study of highly effective dopants of carbon nanotube is essential to understand the mechanism and produce ultrahigh conductive materials for high-power or high-current electrical applications. Though there are several experimental reports of chemically doped high electrical conductivity carbon nanotubes, influence of doping on quantum conduction is not studied well. Here, we investigated the impact of dopant such as I 2 and AuCl 3 on the electronic structure and quantum conduction using ab initio theoretical calculations. For both I 2 and AuCl 3 , they are adsorbed in carbon nanotube molecules and act as p-type dopants. Our study reveals that -1.61 eV shift in Fermi level occurs for AuCl 3 doping in CNT, which implies possibility of ultrahigh conductivity. Furthermore, transmission function calculations confirm significant increase of available quantum channels for conduction in AuCl 3 doped CNT.