In the present work, a methodology has been proposed to design analog/mixed-signal circuits using digital-in-concept circuits in digital technology. This paper presents a digital standard cell-based, scalable, highly synthesizable analog voltage comparator designed in 180 nm CMOS technology at the supply voltage of 1.8 V. The digital design requires less design effort and is highly immune to process, voltage and temperature variations. The proposed comparator has been designed and simulated in the Cadence virtuoso analog design environment. It is observed from the simulation results that the total power dissipation and propagation delay is 143.2 μW and 1.07 ns, respectively. The offset voltage of the proposed comparator is 4.98 mV. Also, as a proof-of-concept, the feasibility of the proposed analog voltage comparator is carried out on Artix-7 field-programmable gate array (FPGA) using Xilinx Basys-3 FPGA kit and other off-the-shelf components. The proposed digital-in-concept comparator is suitable for low power and high-speed SoC application with reduced design effort and lesser time-to-market.

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, as compared to the cloud. Fog node utilization for updates is non-deterministic, largely owing to the patterns in vehicular traffic. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of the regional traffic pattern for a particular time period, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. In order to pinpoint the traffic load and perform fog node distribution, we employ k-means clustering. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using the European WiFi hotspot signal strength NYC dataset and the 5G dataset, we observe that the proposed approach increases the net reserve fog resources by 26.57 percent on an average, and reduces the OTA update time by 5.34 percent. We test the scalability of the proposed approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. We observe that a system with less traffic and small update size overall delivers a higher average throughput of 46 Mbps versus one with more traffic and large update size overall, which provides an average throughput of 30 Mbps. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.

Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2] . The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time; (ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties.

Fog computing brings the cloud closer to a user with the help of a micro data center ($mdc$), leading to lower response times for delay sensitive applications.RT-SANE(Real-TimeSecurityAware scheduling on theNetworkEdge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an$mdc$(in proximity to a user) and a cloud data center ($cdc$) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and$mdcs$and$cdcs$are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local$mdcs$or pre-trusted$cdcs$, and semi-private and public jobs on remote$mdcs$and$cdcs$. A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal thatRT-SANEoffers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

Despite reduced network latency and resilience, fog computing has not been leveraged for vehicular Over-the-Air (OTA) updates. Due to vehicle mobility and traffic, the resource utilization of fog nodes is almost non-deterministic, which increases the delay in communication and handover. In this paper, we propose an approach for distributing fog nodes by analyzing the vehicular traffic pattern in a region. The proposed method: (a) finds the optimal number of fog nodes for a specific time interval based on the traffic pattern of a region and (b) maximizes the net reserve resources enabling specific fog nodes. To do so, we use the k-means algorithm to identify traffic load and distribute the fog nodes using our proposed algorithm to maximize fog resource utilization. We present a case study of OTA updates that considers vehicle mobility, data transmission rate, propagation delay and handover delay to predict the required update time. The experimental results demonstrate that the proposed method of fog node allocation extends the net reserve resources by 30.92% on an average, and reduces the OTA update time.

Summary Fog networks have attracted the attention of researchers recently. The idea is that a part of the computation of a job/application can be performed by fog devices that are located at the network edge, close to the users. Executing latency sensitive applications on the cloud may not be feasible, owing to the significant communication delay involved between the user and the cloud data center (cdc). By the time the application traverses the network and reaches the cloud data center, it might already be too late. However, fog devices, also known as mobile data centers (mdcs), are capable of executing such latency sensitive applications. In this paper, we study the problem of balancing the application load while taking account of security constraints of jobs, across various mdcs in a fog network. In case a particular mdc does not have sufficient capacity to execute a job, the job needs to be migrated to some other mdc. To this end, we propose three heuristic algorithms: minimum distance, minimum load, and minimum hop distance and load (MHDL). In addition, we also propose an ILP‐based algorithm called load balancing aware scheduling ILP (LASILP) for solving the task mapping and scheduling problem. The performance of the proposed algorithms have been compared with the cloud only algorithm and another heuristic algorithm called fog‐cloud‐placement (FCP). Simulation results performed on real‐life workload traces reveal that the MHDL heuristic performs better as compared to other scheduling policies in the fog computing environment while meeting application privacy requirements.

BACKGROUND: The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness. RESULTS: A novel algorithm is presented in the paper for learning-guided automatic recognition and quantification of synaptic markers in 3D confocal images. The method developed a discriminative model based on 3D feature descriptors that detected the centers of synaptic markers. It made use of adaptive thresholding and multi-channel co-localization to improve the robustness. The detected markers then guided the splitting of synapse clumps, which further improved the precision and recall of the detected synapses. Algorithms were tested on lobula plate tangential cells (LPTCs) in the brain of Drosophila melanogaster, for GABAergic synaptic markers on axon terminals as well as dendrites. CONCLUSIONS: The presented method was able to overcome the staining artifacts and the fuzzy boundaries of synapse clumps in 3D confocal image, and automatically quantify synaptic markers in a complex neuron such as LPTC. Comparison with some existing tools used in automatic 3D synapse quantification also proved the effectiveness of the proposed method.