Steady State Visually Evoked Potentials (SSVEPs) are intrinsic responses to specific visual stimulus frequencies. When the retina is activated by a frequency ranging from 3.5 to 75 Hz, the brain produces electrical activity at the same frequency as the visual signal, or its multiples. Identifying the preferred frequencies of neurocortical dynamic processes is a benefit of SSVEPs. However, the time consumed during calibration sessions limits the number of training trials and gives rise to visual fatigue since there is significant human variation across and within individuals over time, which

This research paper presents the wireless data and power transfer system for optogenetics visual cortical prosthesis. The system uses the inductive coupling power transfer and 2.4GHz Bluetooth 4.0 data transfer. This system contains two hardware parts: the external headset consists of power and data transmitters, image capture, and image processing units; the subcutaneous implant PCB consists of power and data receiver and the control unit. We also present the relative image processing method for this system. The whole system could power and control the optogenetic neural stimulus of the

Piezoelectric vibration energy harvesters have attracted a lot of attention as a way to power self-sustaining electronic systems. Furthermore, as part of the growing Internet of Things (loT) paradigm, the ongoing push for downsizing and higher degrees of integration continues to constitute major drivers for autonomous sensor systems. Two of the most effective interface circuits for piezoelectric energy harvesters are synchronised switch harvesting (SSH) on inductor and synchronous electrical charge extraction; nevertheless, inductors are essential components in both interfaces. This study

The applications of Federated Learning are many, and they can be used to predict electricity consumption and, at the same time, enable smart meters to collaboratively learn a shared model while keeping all their data locally in their own private database. With this approach, the central model will see more data and will work better to predict electricity consumption more accurately than the models trained on only one local Dataset. The planning of infrastructure, grid operation, and budgeting all depend on accurate load forecasting. As a result, this paper suggests federated learning for load

Optogenetic-based neuroprosthetic therapies are increasingly being considered for human trials. However, the optoelectronic design of clinical-grade optogenetic-based neuroprosthetic probes still requires some thought. Design constraints include light penetration into the brain, stimulation efficacy, and probe/tissue heating. Optimisation can be achieved through experimental iteration. However, this is costly, time-consuming and ethically problematic. Hence it is highly desirable to have an alternative to excessive animal trials. Thus, a simulation tool for optimising probe design can be an

In order to strengthen the safety of corporate multimedia assets, a semi-fragile watermarking method is developed, which makes use of the integer wavelet transform (IWT) and the discrete cosine transform (DCT) for tamper detection and recovery. In this paper, we produce two distinct kinds of watermarks: an authentication watermark and a recovery watermark. A tamper detection methodology is utilized at the receiving end to check the watermarked image for validity and detect any assaults. If the changes are determined to be malicious, the suggested tamper recovery method is used to restore the

Harvesting the energy from environmental sources is a promising solution for perpetual and continuous operation of biomedical wearable devices. Although the energy harvesting technology ensures the availability of energy source, yet power management is crucial to ensure prolonged and stable operation under a stringent power budget. Thus, power-aware task scheduling can play a key role in minimizing energy consumption to improve system durability while maintaining device functionality. This chapter proposes a novel biosensor task scheduling of energy harvesting-based biomedical wearable devices

In smart metering applications, transferring and collecting data within delay constraints is crucial. IoT devices are usually resource-constrained and need reliable and energy-efficient routing protocol. Furthermore, meters deployed in lossy networks often lead to packet loss and congestion. In smart grid communication, low latency and low energy consumption are usually the main system targets. Considering these constraints, we propose an enhancement in RPL to ensure link reliability as well as low latency. We refer to the proposed new additive composite metric as Delay-Aware RPL (DA-RPL)

Water contamination with paints causes a colour agent to the water that negatively affects the environment, organisms, and humans. Different physicochemical processes are applied for wastewater treatment; however, they have many drawbacks such as high cost, generating toxic waste, and non-effective at low concentrations. Adsorption is considered a promising technique for pollutant removal from polluted wastewater. Commercial activated carbon, nano-materials, and natural biological materials are used as adsorbents in adsorption. This chapter focuses on discussing the adsorption process, the

Area and power consumption are the main challenges in Network on Chip (NoC). Indeed, First Input First Output (FIFO) memory is the key element in NoC. Increasing the FIFO depth, produces an increas in the performance of NoC but at the cost of area and power consumption. This paper proposes a new hybrid CMOS-Memristor based FIFO architecture that consumes low power and has a small size compared to the conventional CMOS-based FIFOs. The predicted area is approximately equal to the half of that wasted in conventional FIFOs. The implementation of FIFO controller module is implemented using HDL