Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.

Reliable detection of the onset of epileptic seizures has seen renewed interest over the past few years, owing to several factors including, the global push towards digital health-care, the advancements in signal processing techniques, and the increased computational power of machines. A reliable automatic system could result in tremendous improvement in the quality of life of epilepsy patients. This paper presents dynamic mode decomposition (DMD), a data-driven dimensionality reduction technique, originally used in fluid mechanics, as an instrument for epileptic seizure detection from scalp electroencephalograph (EEG) data. DMD is employed in this work to measure power of signals in different frequency bands. These subband-powers, along with signal curve-lengths, are used as features for training Random Under-sampling Boost (RUSBoost) decision-tree classifier. Postprocessing measures ensure an acceptable balance between false positives and true positives. The proposed algorithm has been tested over a thousand hours of EEG data from two different datasets, the CHB-MIT dataset, and the KU Leuven dataset, giving sensitivity values of 0.87 and 0.88, respectively, and specificity values of 0.99 for both datasets.

The combination of non-orthogonal multiple access (NOMA) and cooperative communications can be a suitable solution for fifth generation (5G) and beyond 5G (B5G) wireless systems with massive connectivity, because it can provide higher spectral efficiency, lower energy consumption, and improved fairness compared to the non-cooperative NOMA. However, the receiver complexity in the conventional cooperative NOMA increases with increasing number of users owing to successive interference cancellation (SIC) at each user. Space time block code-aided cooperative NOMA (STBC-CNOMA) offers less numbers of SIC as compared to that of conventional cooperative NOMA. In this paper, we evaluate the performance of STBC- CNOMA under practical challenges such as imperfect SIC, imperfect timing synchronization between distributed cooperating users, and imperfect channel state information (CSI). We derive closed-form expressions of the received signals in the presence of such realistic impairments and then use them to evaluate outage probability. Further, we provide intuitive insights into the impact of each impairment on the outage performance through asymptotic analysis at high transmit signal-to-noise ratio. We also compare the complexity of STBC-CNOMA with existing cooperative NOMA protocols for a given number of users. In addition, through analysis and simulation, we observe that the impact of the imperfect SIC on the outage performance of STBC-CNOMA is more significant compared to the other two imperfections. Therefore, considering the smaller number of SIC in STBC-CNOMA compared to the other cooperative NOMA protocols, STBC-CNOMA is an effective solution to achieve high reliability for the same SIC imperfection condition.

Epilepsy is one of the most common neurological disorders, which manifests as unprovoked seizures. The prevalence of epilepsy is higher in developing countries, where medical facilities are ill-equipped and under-staffed. Mobile EEG devices promise a new dawn for long-term ambulatory EEG monitoring, which has a potential to revolutionize health care for neurological disorders especially epilepsy. Increasing the outreach to underserved communities and continuous monitoring of patients will yield vast amount of data. This requires the development of a method that can mark regions of interest, to aid in the evaluation of the EEG trial by the experts. Such an experimental setting calls for an unsupervised method, which can detect seizure regions with high accuracy. This paper focuses on the development of a seizure detection method with the above-stated characteristics. Group invariant scattering, a novel data representation technique, has been used for feature extraction. Tested on CHB-MIT data set, the proposed methodology outperforms the current state-of-the-art approaches under similar testing conditions, by successfully detecting 180 out of 197 seizures.

This article addresses the design of a path following controller for a payload tethered to a quadrotor. Specifically, the goal is to design a smooth dynamic feedback controller that forces the suspended payload to converge and follow a large class of embedded curves. The given curve is treated as a smooth manifold, and set stabilization is used to find the maximal control invariant manifold. The controller design guarantees that once the aerial robot approaches the path, the payload never leaves the path. The performance of the proposed controller is verified through simulations with practical sensor noise and parametric uncertainties. Moreover, experimental implementation and verification of the proposed control scheme are performed on a Quanser UAV platform in an indoor flying arena.

Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem. Therefore, reliably detecting the onset of seizures has evoked the interest of researchers over the last few years. Major leaps in the domain of machine learning, signal processing methods and computational capabilities have made it a tractable task. In this paper, we apply multi-resolution dynamic mode decomposition (MRDMD), which is a data-driven dimensionality reduction technique, on the problem of epileptic seizure detection. This method can effectively separate a complex non-linear system into a collection of time-scale components at different resolutions. We have applied this algorithm on two different scalp EEG datasets, i.e., CHB-MIT and KU Leaven datasets. We have applied necessary post-processing steps to reduce the false alarm rate and boost the sensitivity and specificity. A detailed analysis of the results has been presented for the proposed method applied to both the datasets. The algorithm achieves a sensitivity of 0.937 and 0.96, specificity of 0.99 and 0.99, false alarm rates of 0.587 and 0.413 per hour, and latency of 3.12 seconds and 2.75 seconds for CHB-MIT and KU Leuven, respectively. The results indicate that multi-resolution analysis yields a significant improvement in sensitivity as compared to the basic dynamic mode decomposition Keywords– Epilepsy, seizure detection, EEG, dynamic mode decomposition, multi-resolution analysis, decision trees, RUSBoost.